Starkey Research & Clinical Blog

Listening is more effortful for new hearing aid wearers

Ng, E.H.N., Classon, E., Larsby, B., Arlinger, S., Lunner, T., Rudner, M., Ronnberg, J. (2014). Dynamic relation between working memory capacity and speech recognition in noise during the first six months of hearing aid use. Trends in Hearing 18, 1-10.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

Numerous studies have illustrated the relationship between working memory, cognitive resources and speech perception and suggest that listeners with limited working memory or cognitive resources are more likely to struggle with speech recognition in noise (Gatehouse, et al., 2003; Lunner, 2003). Conversely, larger working memory capacity may allow more rapid and successful matching between speech inputs and stored lexical templates.  This concept is described by the Ease of Language Understanding (ELU) model, which proposes that cognitive processing demands vary according to the degradation of the speech signal in different environments (Ronnberg, 2003; Ronnberg et al., 2008). In quiet, favorable listening conditions, speech inputs are easily matched to stored representations and the processing is automatic. In difficult listening environments, more explicit processing is required to match inputs to stored representations. How efficiently this goal is achieved is dependent upon working memory capacity.

Using these concepts as underpinning, Ng and her colleagues proposed that working memory and cognitive processing may have more of an impact on speech recognition for new hearing aid users than for experienced hearing aid users. Hearing aids improve speech audibility and directional microphones and noise reduction can help preserve speech in adverse listening conditions, which should reduce the need for explicit working memory processing. However, if phonological representations stored in memory have been degraded by hearing loss over time, amplified speech perceived by new hearing aid users will not match their stored templates. Therefore, more explicit processing in working memory may be required to identify words. Over time, as the individual becomes acclimated to the amplified sound, stored templates may adapt and become more similar to their acoustic counterparts, reducing the working memory and cognitive load requirements for correct identification. Following this reasoning, Ng and her colleagues proposed that there would be a significant relationship between cognitive functioning and speech recognition in new, first-time hearing aid users but that the relationship would become weaker over time as stored speech representations based on amplified sound become more established.

To examine this hypothesis, 27 first-time hearing aid users were recruited from a pool of subjects at a Swedish university Audiology clinic. All had mild to moderately-severe sensorineural hearing loss and no previous experience with hearing aids. Nine of the subjects were fitted monaurally and 18 were fitted binaurally. Four participants had in-the-ear or canal instruments and 23 had behind-the-ear instruments. Most of the subjects became full-time hearing aid users and the rest were consistent, part-time users.

Approximately four months prior to being fitted with their hearing aids, subjects attended an experimental session at which they completed speech recognition in noise and cognitive testing. Four cognitive tests were administered: the Reading Span test, a physical matching task, a lexical decision making task and a rhyme judgment test. The Swedish version of the Reading Span test was used to assess working memory or listeners’ ability to process and store verbal information in a parallel task design (Ronnberg et al., 1989). After hearing a list of sentences, subjects were asked to recall either the first or final word of each sentence in the list.  The test was scored according to the total number of words correctly recalled. The physical matching test (Posner & Mitchell, 1967), which measured general processing speed, required participants to judge whether two examples of the same letter were visibly identical or different in physical shape (e.g., A-A vs. A-a). Scores were based on reaction time for correct trials. The lexical decision making task required subjects to judge whether a string of 3 letters presented on a screen was a real Swedish word. Scores were based on reaction time for correct trials. The rhyme judgment test required subjects to determine whether two words, presented on a screen, rhymed or not (Baddeley & Wilson, 1985). This test was intended to measure the quality of stored phonological representations and was scored based on percentage of correct judgments.

The speech recognition in noise test was conducted again at the hearing aid fitting appointment (0 months) and again at approximately 3 month intervals (3 months and 6 months). The investigators chose to evaluate speech recognition and its relationship to cognitive tests at these intervals based on previous reports suggesting that a familiarization period of 4-9 weeks was required to reduce cognitive load (Rudner et al., 2011).

The results of the speech recognition in noise test showed, not surprisingly, that aided SRT was significantly better than unaided SRT.  The change in SRT over time was also significant, in that the SRT measured at 6 months was significantly better than at 0 months. The 3 month SRT was not significantly different from the 0 month or 6 month tests.  Age and pure-tone-average (PTA) were significantly correlated with SRT at 0, 3 and 6 month tests. At 0 and 3 months, the cognitive measures of reading span, physical matching and lexical decision were all correlated with SRT. At 6 months, only the correlations between lexical decision and reading span were significant. These results indicate that the relationship between cognitive measures and speech recognition declined over the first 6 months of hearing aid use.  Regression analysis showed a similar pattern in that reading span and PTA were significant predictors of speech recognition at 0 months, but by 6 months, only PTA was a significant predictor.

The pattern of results in this study supports the authors’ proposal that for first-time hearing aid users, working memory and cognitive processing play a more important role in speech perception in noise immediately after fitting than they do after acclimatization. Their hypothesis that stored perceptual representations are altered by long-term hearing loss and are therefore mismatched with newly amplified speech inputs is supported by their clinical observations. First-time hearing aid users typically experience amplified speech as “tinny”, “metallic”, or in some way “artificial”.  For most new hearing aid users, this perception resolves within a few weeks or so, though others may require longer periods of use to become acclimated. As time goes on, new hearing aid users usually report that speech sounds more natural and the data presented here support the assertion that stored lexical representations, after becoming distorted from long-term hearing impairment, may be adapting based on consistently restored audibility of speech sounds.

The results of this study support the importance of cognitive functioning for speech perception in noise and suggest that new hearing aid users experience increased cognitive demands for understanding speech as compared to experienced hearing aid users. It follows that individuals who have limited working memory or impaired cognition may also experience longer acclimatization periods with their new hearing aids.

Clinicians are accustomed to counseling patients to wear their hearing aids consistently; for most of the day, every day.  The authors of this study did not examine the usage patterns of their subjects with reference to their hypothesis, but future studies should investigate the potential effects of limited hearing aid use on the relationship between cognition and speech recognition in noise. If full-time use results in a more rapidly waning relationship between these variables (indicating a more rapid decrease in cognitive load required for speech recognition) it would underscore the importance of consistent hearing aid use for new users, especially those with cognitive or working memory limitations.

 

References

Baddeley, A. & Wilson, B. (1985). Phonological coding and short term memory in patients without speech. Journal of Memory and Language 24(1), 490-502.

Gatehouse, S., Naylor, G., & Elberling, C. (2003). Benefits from hearing aids in relation to the interaction between the user and the environment. International Journal of Audiology 42 (Suppl. 1), S77-S85.

Hagerman, B. & Kinnefors, C. (1995). Efficient adaptive methods for measuring speech reception threshold in quiet and in noise. Scandinavian Audiology 24 (1), 71-77.

Lunner, T. (2003). Cognitive function in relation to hearing aid use. International Journal of Audiology 42, (Suppl. 1), S49-S58.

Lunner, T., Rudner, M. & Ronnberg, J. (2009). Cognition and hearing aids. Scandinavian Journal of Psychology 50, 395-403.

McCoy, S.L., Tun, P.A. & Cox, L.C. (2005). Hearing loss and perceptual effort: downstream effects on older adults’ memory for speech. Quarterly Journal of Experimental Psychology A, 58, 22-33.

Ng, E.H.N., Classon, E., Larsby, B., Arlinger, S., Lunner, T., Rudner, M., Ronnberg, J. (2014). Dynamic relation between working memory capacity and speech recognition in noise during the first six months of hearing aid use. Trends in Hearing 18, 1-10.

Posner, M. & Mitchell, R. (1967). Chronometric analysis of classification. Psychological Review 74(5), 392-409.

Ronnberg, J., Arlinger, S., Lyxell, B. & Kinnefors, C. (1989). Visual evoked potentials: Relation to adult speechreading and cognitive function. Journal of Speech and Hearing Research 32(4), 725-735.

Ronnberg, J. (2003). Cognition in the hearing impaired and deaf as a bridge between signal and dialogue: a framework and a model. International Journal of Audiology 42 (Suppl. 1), S68-S76.

Ronnberg, J., Rudner, M. & Foo, C. (2008). Cognition counts: A working memory system for ease of language understanding (ELU). International Journal of Audiology 47 (Suppl. 2), S99-S105.

Rudner, M., Ronnberg, J. & Lunner, T. (2011). Working memory supports listening in noise for persons with hearing impairment. Journal of the American Academy of Audiology 22, 156-167.

 

These factors lead to successful hearing aid use

Hickson, L., Meyer, C., Lovelock, K., Lampert, M. & Khan, A. (2014) . Factors associated with success with hearing aids in older adults. International Journal of Audiology 53, S18-S27.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

There is a saying in real estate that the three most important factors determining property value are location, location, location.  A similar argument could be made for hearing aid fitting. Three of the most important factors in hearing aid success may be follow-up, follow-up, follow-up. Certainly, success cannot be expected without appropriate selection and verification, but thorough training, counseling and consultation after the fitting can have a huge impact on the comfort and perceived benefit of new hearing aids.

Hearing aid success can generally be defined as an outcome in which the patient wears the instruments regularly and reports benefit from them.  Knudsen et. al. (2010) reviewed several studies and a few factors emerged that were consistently related to success with hearing aids.  In general, the individuals most likely to do well were those who had positive attitudes about hearing aids prior to fitting and a greater degree of self-reported hearing difficulty (et al., 2010; Hickson et al., 1986, 1999; Cox et al., 2007).  In studies examining why people don’t use their hearing aids, the most commonly cited reasons were lack of perceived benefit and problems with the fit and comfort of the aids (McCormack & Fortnum, 2013).

A better understanding of how these factors interact will help clinicians guide their patients to become consistent, successful hearing aid users. Defining success as a combination of regular use and self-reported benefit, Hickson and her colleagues examined the association between audiological and non-audiological factors and successful hearing aid outcomes.  The audiological factors they studied were duration and degree of hearing loss, presence of tinnitus, style of hearing aid and insertion gain with hearing aids. Non-audiological factors were grouped into four categories: attitudes and cues to action, demographic characteristics, psychological factors and age-related factors.

One hundred and sixty adults over age 60 participated in the study, with a mean age of 73 years. All had hearing loss greater than 25dB HL and fewer than 2 years of experience with hearing aids. Of the 160 subjects, 75 were classified as unsuccessful hearing aid users and 85 were classified as successful users.  Unsuccessful users were defined as those who reported little or no use and/or benefit with their hearing aids.

Subjects participated in one session at which they completed audiological testing, real-ear measurements, cognitive testing, a case history and a general health questionnaire. Two weeks prior to the session they were given 8 self-report questionnaires to complete at home:

1.              Hearing Handicap Questionnaire (HAQ; Gatehouse & Noble, 2004)

2.              Self-Assessment of Communication (SAC; Schow & Nerbonne, 1982)

3.              Attitude to Hearing Aids Questionnaire (VanDenBrink, 1995)

4.              Measure of Audiologic Rehabilitation – Self-Efficacy for Hearing Aids (MARS-HA; West & Smith, 2007)

5.              Coping Strategy Indicator (CSI; Amirkhan, 1990)

6.              Locus of Control scales (Levenson, 1981; Presson et al., 1997)

7.              Auditory Lifestyle & Demand Questionnaire (ALDQ; Gatehouse et al., 1999)

8.              Social Activities Survey (SOCACT; Cruice et al., 2001)

Data analysis revealed that four factors were significantly related to hearing aid success. In order from strongest to weakest associations, these factors were positive support from others, hearing difficulties in everyday life, insertion gain (for 55dB input level) and the interaction between attitude toward hearing aids and advanced handling (e.g., identification of different components of a hearing aid and how confident the user was in manipulating the aids). Overall, hearing aid users were more likely to achieve success if they had support from friends and family, perceived greater difficulty hearing, their insertion gain matched target, they possessed a positive attitude about hearing aids and had greater confidence in their ability to use the hearing aids. Conversely, almost 25% of unsuccessful hearing aid users reported that their hearing aids didn’t help them hear better or were too noisy.  Less common responses from unsuccessful users were that they didn’t need hearing aids, had difficulty manipulating or adjusting to the aids or obtained no benefit from the aids.

The factor most strongly related to success was the support of significant others, as indicated by statements such as “The people around me think it was wise to obtain a hearing aid” or “The people around me think I hear better with my hearing aid”: underscoring the importance of involving spouses, family members or friends in the hearing aid fitting process, so that their observations and comments can be considered and discussed at the initial consultation, fitting and follow-up appointments.  Having support at the initial consultation also helps the potential hearing aid user realize their need for help. As many clinicians know, the hearing-impaired individual is often less aware of their communication difficulties than their close associates are. Friends and family members who support the need for and the observed success with hearing aids can be influential in the patient’s own motivation and perceived benefit.

Detailed discussion of test results and administration of hearing handicap questionnaires can also motivate potential hearing aid users to proceed with an evaluation and fitting. It is common for people with hearing loss to think that other people mumble or that the source of communication difficulty is external rather than related to their hearing loss. Seeing the configuration of the hearing loss, perhaps in the context of speech and familiar sounds can help them understand what they are missing. Hearing handicap questionnaires illuminate some of the familiar challenges that hearing impaired individuals experience. Clinicians are familiar with the scenario in which hearing impaired patients feel they don’t really have hearing loss because “they can still hear, they just don’t understand”.  Simply explaining the audiogram illustrates how their hearing differs from normal hearing can help them understand the implications of the loss and the need for amplification.

A positive attitude about hearing aids was related to increased use and perceived benefit. This is a harder goal to achieve, but should be addressed at the initial consultation and consistently thereafter. Every clinician has met patients who know a friend or neighbor who doesn’t like their hearing aids and it can be challenging to persuade skeptics that there is reason to expect improvement from hearing aids. It may be helpful to have testimonials from satisfied patients available on the clinic website or in written materials in the office.  I also find it helpful to simply assure people that with the quality of today’s hearing aid technology, there are very few problems that can’t be solved with thorough assessment, training and follow-up.

The issue of hearing aid stigma and negative associations is not an easy problem to overcome, but it has improved over time and will likely continue to improve. Clinicians should encourage successful hearing aid users to share their positive experiences with friends, family and co-workers, to act as advocates for the benefits of hearing aids. Similarly, friends and family of those who have experienced hearing aid success should spread the word whenever possible. The most powerful endorsements come from people who have experienced better communication with their own hearing aids. As a clinician, patients often tell me that hearing aids make their lives easier. Others tell me that they can’t imagine trying to function without their hearing aids. The more these hearing aid success stories circulate among the general public, the more motivated hearing-impaired individuals will be to pursue hearing aids for themselves.

Individuals who had greater confidence in their ability to use the hearing aids were more likely to be successful, regular users. This is another factor that can be addressed through training, guided practice, and clearly written materials. Many new patients are overwhelmed by the extent of information is covered during the hearing aid fitting; informing the patient that you have a plan for training during later visits will ease any anxiety with retaining all of the information shared at the time of the first fitting. Including friends or family members at the fitting also contributes to success as these individuals may contribute to use and care during the adjustment period.

The responses of the participants in this study illuminate many of the factors that affect hearing aid success. With an understanding of these factors and thorough follow-up care, clinicians can avoid or solve most problems and most hearing aid users should perceive benefit from their instruments. Because hearing aids are medical devices, they require comprehensive care from trained professionals. Time spent on fine tuning, training and counseling during the first few weeks after the fitting can have long-term impact on usage patterns, satisfaction and perceived benefit. Clinicians and experienced hearing aid users should share stories of positive outcomes to counterbalance negative perceptions so that new and potential users can embark upon hearing aid fittings with expectations of success.

 

References

Amirkhan, J. (1990). A factor analystically derived measure of coping: The coping strategy indicator. Journal of Personality and Social Psychology 59, 1066-1074.

Champion, V. & Skinner, C. (2008). The health belief model. In: K. Glanz, B.K. Rimer, K. Viswanath (eds.) Health Behavior and Health Education: Theory, Research and Practice. San Francisco: Jossey-Bass.

Cox, R. & Alexander, G. (1995). The abbreviated profile of hearing aid benefit. Ear and Hearing 16, 176-186.

Cox, R., Alexander, G. & Gray, G. (2007). Personality, hearing problems, and amplification characteristics: Controbituions to self-report hearing aid outcomes. Ear and Hearing 28, 141-162.

Cruice, M. (2001). Communication and quality of life in older people with aphasia and healthy older people. Ph.D. Dissertation, The University of Queensland, Australia.

Gatehouse, S., Elberling, C. & Naylor, G. (1999). Aspects of auditory ecology and psychoacoustic function as determinants of benefits from and candidature for non-linear processing in hearing aids. In: Kolding (ed.) 18th Danavox Synposium, 221-233.

Gatehouse, S. & Noble, W. (2004). The speech, spatial and qualities of hearing scale (SSQ). International Journal of Audiology 43, 85-99.

Glanz, K., Rimer, B. &National Cancer Institute – U.S. (2005). Theory at a glance a guide for health promotion practice: U.S. Department of Health and Human Services National Cancer Institute.

Hickson, L., Hamilton, L. & Orange, S. (1986). Factors associated with hearing aid use. Australian Journal of Audiology 8, 37-41.

Hickson, L. Timm, M., Worrall, L. & Bishop, K. (1999). Hearing aid fitting: Outcomes for older adults. Australian Journal of Audiology 21, 9-21.

Hickson, L., Meyer, C., Lovelock, K., Lampert, M. & Khan, A. (2014) . Factors associated with success with hearing aids in older adults. International Journal of Audiology 53, S18-S27.

Knudsen, L., Oberg, M., Nielsen, C., Naylor, G.  & Kramer, S. (2010).  Factors influencing help seeking, hearing aid uptake, hearing aid use and satisfaction with hearing aids: A review of the literature. Trends in Amplification 14, 127-154.

Levenson, H. (1981). Differentiating among internality, powerful others, and chance. In: H.M. Lefcourt (ed.) Research with the Locus of Control Construct: Assessment Methods. New York: Academic Press. pp. 15-63.

McCormack, A. & Fortnum, H. (2013).  Why do people fitted with hearing aids not wear them? International Journal of Audiology 52, 360-368.

Metselaar, M., Maat, B., Krijnen, P., Verschure, H. & Dreschler, W. (2008). Self-reported disability and handicap after hearing aid fitting and benefit of hearing aids: Comparison of fitting procedures, degree of hearing loss, experience with hearing aids and unilateral and bilateral fittings. European Archives of Otorhinolarygology, open access.

Presson, P., Clark, S. & Benassi, V. (1997). The Levenson locus of control scales: Confirmatory factor analyses and evaluation. Journal of Social Behavior and Personality 25, 93-104.

Stark, P. & Hickson, L. (2004). Outcomes of hearing aid fitting for older people with hearing impairment and their significant others. International Journal of Audiology 43, 390-398.

Schow, R. & Nerbonne, M. (1982). Communication screening profile: Use with elderly clients. Ear and Hearing 3, 135-147.

VanDenBrink, R. (1995). Attitude and illness behavior in hearing impaired elderly. Ph.D. dissertation, University of Groningen.

Ventry, I. & Weinstein, B. (1982). The hearing handicap inventory for the elderly: a new tool. Ear and Hearing 3, 128-134.

West, R. & Smith, S. (2007). Development of a hearing aid self-efficacy questionnaire. International Journal of Audiology 46, 759-771.

Cognitive Benefits of Digital Noise Reduction

Desjardins, J. & Doherty, K. (2014). The effect of hearing aid noise reduction on listening effort in hearing-impaired adults. Ear and Hearing 35 (6), 600-610.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

Understanding speech in noise can be a challenge for anyone with hearing loss, but it is especially difficult for older listeners (Plomp, 1978; Duquesnoy, 1983; Dubno et al., 1984; Helfer & Freyman, 2008).  Older individuals also experience increased listening effort in noisy conditions as compared to younger listeners (Desjardins & Doherty, 2013).  Listening effort is often measured in a dual-task paradigm during which subjects perform a second task while simultaneously repeating speech in noise. Increased listening effort is reflected by the allocation of cognitive resources away from the secondary task, resulting in poorer performance. When this effect is considered with reference to everyday situations, increased listening effort could have repercussions for elderly individuals beyond communication, affecting their ability to multi-task which could have associated safety concerns.  Listening effort could also affect the risk of social isolation as elderly, hearing-impaired individuals may feel reluctant to exert the energy and effort required to interact with others in group situations.

In a review of the literature on speech recognition and cognitive abilities, Akeroyd (2008) found that hearing was the primary predictor of speech recognition performance, but working memory capacity was the second best predictor. Because speech perception is affected by peripheral auditory processing as well as cognitive functions like working memory and speed of processing, the measurement of speech recognition scores alone may not be sufficient to evaluate the potential benefits of noise reduction in hearing aids. To this end, some studies have examined the effects of noise reduction on listening effort and the allocation of cognitive resources. Ng et al. (2013) found that noise reduction improved working memory function for some subjects and Sarampalis et al. (2009) found that the use of noise reduction reduced listening effort, resulting in quicker visual reaction times and better word recall on secondary tasks.

The current study investigated the relationships among noise reduction, listening effort and speech recognition in middle-aged to older adults with hearing loss. A dual-task paradigm, with speech recognition in noise as the primary task and visual tracking as the secondary task, was used to evaluate listening effort. Working memory tends to decline with advancing age (Salthouse, 1994), as does the speed of perceptual processing (Salthouse, 1985; Wingfield et al., 1985), so measures of working memory and processing speed were also examined. Twelve subjects participated in the study, ranging in age from 50 to 74 years.  All had symmetrical, sensorineural hearing loss and were experienced hearing aid users. For the purpose of the study, subjects were fitted with behind-the-ear hearing aids and disposable canal earmolds, fitted to DSL v5 targets (Scollie et al., 2005). Hearing aids were programmed with two memories: in the first, all special signal processing features were disabled and in the second, noise reduction (Voice iQ2) was set to maximum.

Speech recognition in noise was examined using the R-SPIN Test (Bilger et al., 1984), which consists of eight lists of 50 sentences each. Half of the sentences on each list are high context and half are low-context.  The sentences were presented in two-talker babble (TTB), composed of two female talkers reciting nonsense sentences. TTB has significantly affected listening effort for older subjects in previous research (Desjardins & Doherty, 2013).  Prior to the dual task procedure, subjects performed a sentence recognition test in noise to determine the SNR required to achieve 76% performance and 50% performance.  Not surprisingly, subjects required more favorable SNRs to achieve 76% performance, with average SNRs of 4.4dB for 76% performance and 1.8dB for 50% performance. The individual SNRs were used later to derive four listening conditions for the dual task procedure:

1.              Moderate listening condition (more favorable SNR) with noise reduction

2.              Moderate listening condition without noise reduction

3.              Difficult listening condition (less favorable SNR) with noise reduction

4.              Difficult listening condition without noise reduction

Following the sentence blocks in the dual-task procedure, listeners were asked to rate their ease of listening. They were asked to rate how easy it was to listen to the sentences on a scale from 0-100, with 0 being “very, very difficult” and 100 being “very, very easy” (Geller & Margolis, 1984; Feuerstein, 1992).

Results for the R-SPIN speech recognition task revealed significantly higher scores in the moderate listening condition compared to the difficult listening condition. There was no main effect of noise reduction or any interaction between listening condition and noise reduction, indicating that noise reduction did not have a significant impact on speech recognition ability in noise. Scores for high-context sentences were significantly better than for low-context sentences, indicating that listeners used context to help them understand the sentences better. There was no interaction between context and listening condition.

Listening effort was measured with the dual-tasks of speech recognition in noise and visual tracking. Poorer performance on the visual tracking task indicated higher listening effort or the allocation of more cognitive resources toward speech recognition. Sentence recognition scores were not significantly different based on the four test conditions, but listening effort was affected by test condition.  Without noise reduction, listening effort was significantly higher for the difficult listening condition than for the moderate condition. With noise reduction, there was no significant difference in listening effort between the moderate and difficult listening conditions. In other words, noise reduction reduced listening effort in the difficult listening condition, but did not have a significant effect in the moderate condition. Perhaps surprisingly, listening effort did not vary depending on sentence context. Secondary task performance remained consistent during high and low context sentences, despite the fact that speech recognition scores were significantly better for high context sentences.

Self-perceived ease of listening ratings showed that subjects rated the moderate listening condition as significantly easier than the difficult condition. There was no significant difference in their ease of listening ratings based on noise reduction, nor was there an interaction between noise reduction and listening condition. These results indicated that the subjects did not feel that noise reduction made listening easier, despite the fact that their measured listening effort was significantly reduced with noise reduction, at least in the difficult listening condition.

There were no significant effects between working memory or processing speed on listening effort, with or without noise reduction. There was, however, a significant trend for subjects with faster processing speeds to show reduced listening effort with noise reduction activated, but this occurred only for the difficult listening condition.

The results of this study are in agreement with prior reports, in that noise reduction did not significantly improve significant speech recognition scores in noise. Noise reduction did, however, reduce listening effort in the difficult listening condition with the poorer signal-to-noise ratio. This is in agreement with Sarampalis et al. (2009) who reported listening effort reduction only in the more difficult listening condition of -6dB SNR.  Though working memory and processing speed did not significantly affect listening effort in this study, there was a trend showing subjects with faster processing were able to derive more benefit from noise reduction in the difficult listening situation. Prior studies have shown relationships between working memory, processing speed and listening effort, so this is an area that requires further study.

Desjardins and Doherty’s study provides further evidence that tests of listening effort may be a reasonable tool for evaluating the benefits of noise reduction. The authors point out that the ability of noise reduction to reduce listening effort in noisy conditions could have implications for multi-tasking, which could in turn affect safety in some scenarios.  For instance, an older hearing-impaired person, driving a car while talking to a passenger, may unwittingly divert cognitive resources toward the task of understanding conversation, thus potentially reducing their ability to respond to other stimuli related to driving. If noise reduction helps reduce the cognitive demand required for speech recognition in this scenario, in theory there would be more cognitive resources available for driving and attending to surrounding activity.

Concerns like this are particularly important when considering hearing aid fittings for older patients.  Hearing loss increases with advancing age, while working memory and processing speed are known to decline with age.  Therefore, older individuals are more likely to be challenged by the cognitive demands of multi-tasking while at the same time facing additional obstacles like poorer hearing and speech recognition ability and impaired vision.  Though the maximum noise reduction settings used in this study are not usually used in clinical fittings, it may be appropriate to use higher noise reduction settings for older hearing aid users, especially those with known cognitive processing deficits. Directional microphones and automatic noise programs with low-frequency gain reduction may provide additional benefit.  In counseling sessions, clinicians should discuss potential multi-tasking difficulties and the need to reduce distractions in order to optimize speech recognition ability. The importance of properly fitted hearing instruments to facilitate speech communication during simultaneous tasks should also be emphasized, especially at early appointments when hearing loss is diagnosed and hearing aids are being considered. This is particularly relevant to situations in which older, hearing-impaired listeners may be executing other tasks like driving, walking on stairs or through parking lots or streets, in which safety is a concern.

Social isolation is a concern for older adults with hearing loss. The National Council on Aging (www.ncoa.org) reports that hearing loss can reduce participation in social activities, reduce self-confidence, cause depression and strain relationships with family and friends. Hearing loss is also known to increase mental fatigue, making hearing-impaired listeners feel “exhausted” and fatigued from conversational interaction (Hornsby, 2013). Older adults who treat their hearing loss with hearing aids are less likely to experience these negative effects. It is plausible to assert that factors affecting listening effort could also affect the risk of social isolation, as the increased effort and fatigue required in group interactions could be too daunting for some individuals.  The beneficial effect of noise reduction on listening effort is encouraging and should be considered for hearing aid fittings with older adults at risk of social isolation and depression.

 

References

Akeroyd, M. (2008). Are individual differences in speech reception related to individual differences in cognitive ability? A survey of twenty experimental studies with normal and hearing-impaired adults. International Journal of Audiology 47 (Suppl 2), S53-S71.

Bilger, R., Nuetzel, M., Rabinowitz, W. & Rzeczkowski, C. (1984). Standardization of a test of speech perception in noise. Speech and Hearing Research 27, 32-48.

Desjardins, J. & Doherty, K. (2013). Age-related changes in listening effort for various types of masker noises. Ear and Hearing 34, 261-272.

Desjardins, J. & Doherty, K. (2014). The effect of hearing aid noise reduction on listening effort in hearing-impaired adults. Ear and Hearing 35 (6), 600-610.

Dubno, J., Dirks, D. & Morgan, D. (1984). Effects of age and mild hearing loss on speech recognition in noise. Journal of the Acoustical Society of America 76, 87-96.

Duquesnoy, J. (1983). The intelligibility of sentences in quiet and noise in aged listeners. Journal of the Acoustical Society of America 74, 1136-1144.

Feuerstein, J. (1992). Monaural versus binaural hearing: Ease of listening, word recognition and attentional effort. Ear and Hearing 13, 80-86.

Geller, D. & Margolis, R. (1984). Magnitude estimation of loudness I: Application to hearing aid selection. Journal of Speech and Hearing Research 27, 20-27.

Helfer, K. & Freyman, R. (2008).  Aging and speech on speech masking. Ear and Hearing 29, 87-98.

Hornsby, B. (2013). The effects of hearing aid use on listening effort and mental fatigue associated with sustained speech processing demands. Ear and Hearing 34(5), 523-534.

Ng, E., Rudner, M. & Lunner, T. (2013). Effects of noise and working memory capacity on memory processing of speech for hearing aid users. International Journal of Audiology 52, 433-441.

Plomp, R. (1978). Auditory handicap of hearing impairment and the limited benefit of hearing aids. Journal of the Acoustical Society of America 68, 1616-1621.

Salthouse, T. (1994). The aging of working memory. Neuropsychology 8(4), 535-543.

Salthouse, T. (1985). A Theory of Cognitive Aging. New York, NY: North-Holland.

Sarampalis, A., Kalluri, S. & Edwards, B. (2009). Objective measures of listening effort: Effects of background noise and noise reduction. Journal of Speech, Language and Hearing Research 52, 1230-1240.

Scollie, S., Seewald, R. & Cornelisse, L. (2005).  The desired sensation level multistage input/output algorithm. Trends in Amplification 9, 159-197.

Wingfield, A., Poon, L. & Lombardi, L. (1985). Speed of processing in normal aging: Effects of speech rate, linguistic structure and processing time. Journal of Gerontology 40, 579-585.

Woods, W., Nooraei, N. & Galster, J. (2010). Real-world listening preference for an optimized digital noise reduction algorithm. Hearing Review 17(9), 38-43.

 

Hearing Aid Use is Becoming more Accepted

Rauterkus, E. & Palmer, C. (2014). The hearing aid effect in 2013. Journal of the American Academy of Audiology 25, 893-903.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

Years ago, one of my patients quoted an aphorism, “Your hearing loss is more noticeable than your hearing aid”. At the time, it wasn’t always applicable. Hearing aids were larger and more visible in the ear and whistling feedback was harder to control, often resulting in embarrassment for the wearer. Today’s hearing aids are smaller, discreet, and comfortable, with effective feedback management. Still, there remains concern among many current and potential hearing aid users about a negative stigma associated with hearing aid use. Despite numerous potential benefits like improved communication ability and decreased stress, listening effort and fatigue, hearing impaired individuals quite frequently postpone or avoid amplification because they believe that wearing hearing aids will cause others to label them as old or less capable.

These negative associations have collectively been described as the hearing aid effect. Blood, Blood and Danhauer (1977) coined this term during a study in which 25 college students were shown photographs of 12 teenage males with and without hearing aids. The participants were asked to judge the boys in the photographs in terms of intelligence, achievement, personality, and appearance. On all attributes, the participants rated the boys in the photographs lower when they were wearing hearing aids versus when they were not.  Since their initial study, other reports showed a similar hearing aid effect (Blood, et al., 1978; Danhauer et al., 1980; Brimacombe & Danhauer, 1983).  Studies in which children rated other children showed strong and consistently negative judgments of individuals with hearing aids, on attributes such as intelligence and attractiveness (Dengerink & Porter, 1984; Silverman & Klees, 1989).  In contrast, some studies in which adults rated other adults did not find a hearing aid effect (Iler et al., 1982; Johnson & Danhauer, 1982; Mulac et al., 1983).

In general, a review of several reports from 1977 through 1985 indicates that hearing aid stigma at that time may have been changing slowly for the better.  A much more recent study (Clucas, et al., 2012) essentially reported the opposite of the typical hearing aid effect, in which 181 medical students rated photographs of a young male wearing a hearing aid as more worthy of respect than the photographs of the same young male without the hearing aid.

Through the years, hearing aids have become smaller and more discreet. Feedback reduction, automatic features and improved performance in noise have allowed hearing aid users to function better in everyday situations, calling less attention to their hearing loss. Ear level devices like earbuds for MP3 players and Bluetooth headsets have become widely used and visible. The Americans with Disabilities Act (ADA) has promoted equal participation of disabled individuals, including those with hearing loss. Public figures have openly discussed their hearing loss and hearing aid use, including Presidents Ronald Reagan and Bill Clinton and musicians like Pete Townsend and Neil Young. All of these factors have likely had a positive influence on public perception of hearing loss and hearing aids and may have reduced the negative stigma so prevalent in earlier reports.

The hearing aid effect, however, has not been re-examined in the same paradigm as the original report, so it is unknown how today’s perceptions might compare to the defining work. Rauterkus and Palmer’s study asked young adults to view and evaluate photographs of young men with and without hearing aids, in an effort to replicate the methods of earlier studies and derive an understanding of the hearing aid effect today.

Twenty-four graduate students in an MBA program were recruited to evaluate photographs of 5 young men, from age 15-17 years. The young men were photographed in 5 different configurations:

1. Wearing a standard BTE hearing aid coupled to a standard earmold and tubing

2. Wearing an open-fit BTE hearing aid coupled to a slim tube and dome

3. Wearing a CIC hearing aid that was not visible in the photo

4. Wearing earbud headphones as would be used with an MP3 device

5. Wearing a Bluetooth ear-level telephone headset

In the pictures, the young man was seated, reading a book. All photographs were taken from the rear left side of the young man, so that the left side and back of his head was visible and ear level devices could clearly be seen. All of the men in the pictures wore the same clothing so that differences would not affect the judgments of the participants.

No participant viewed the same man in more than one device configuration. Each photograph was shown on a page above a list of 8 attributes: attractive, young, successful, hard-working, trustworthy, intelligent, friendly, and educated. Participants were asked to rate the man in the picture on each attribute on a scale of 1-7.  These 8 attributes were selected because they were the most common attributes to have been rated in previous studies of the hearing aid effect.

The results showed no significant difference in ratings among the five young men in the photographs. Therefore, the data for all of the photographs were combined for data analysis.  There was a significant difference in the judgment of age between the photographs of the CIC user and the earbud user, with the CIC user being judged as significantly older than the earbud user.  Because the CIC instruments were not visible in the photographs, this difference is likely to be related to an association between younger people wearing earbuds to listen to music, as opposed to a negative judgment on the use of CIC instruments.  There was a significant difference in trustworthiness between the BTE user and Bluetooth device user, with the Bluetooth headset user deemed significantly less trustworthy. The authors’ findings clearly indicate that the participants did not have adverse reactions to the photographs of hearing aid users and did not demonstrate the hearing aid effect found in earlier studies.

The work of Rauterkus and Palmer suggests the hearing aid effect has diminished or even reversed. A welcome message for hearing care professionals, but we must also understand self-perception of hearing aid use. One could speculate that the commonality of ear-level devices and improvements in hearing aid size, design, performance and connectivity, have improved others perception of hearing aid use, resulting in the documented decrease of the hearing aid effect. It’s possible that the same social and technological factors are taking a similar toll on the negative self-perception of hearing aid use. Time will reveal the reality of these trends but smart research design helps us take a peak into the not-too-distant future.

 

References

Blood, G., Blood, I. & Danhauer, J. (1977). The hearing aid effect. Hearing Instruments 28, 12.

Blood, G., Blood, I. & Danhauer, J. (1978). Listeners’ impressions of normal-hearing and hearing-impaired children. Journal of Communication Disorders 11(6), 513-518.

Clucas, C., Karira, J. & Claire, L. (2012). Respect for a young male with and without a hearing aid: a reversal of the “hearing aid effect” in medical and non-medical students? International Journal of Audiology 51(10), 739-745.

Danhauer, J., Blood, G., Blood, I. & Gomez, N. (1980). Professional and lay observers’ impressions of preschoolers wearing hearing aids. Journal of Speech and Hearing Disorders 45(3), 415-422.

Dengerink, J. & Porter, J. (1984). Children’s attitudes towards peers wearing hearing aids. Language, Speech and Hearing Services in Schools 15, 205-209.

Iler, K., Danhauer, J. & Mulac, A. (1982).  Peer perceptions of geriatrics wearing hearing aids. Journal of Speech and Hearing Disorders 47(4), 433-438.

Johnson, C. & Danhauer, J. (1982). Attitudes towards severely hearing impaired geriatrics with and without hearing aids. Australian Journal of Audiology 4, 41-45.

Mulac, A., Danhauer, J. & Johnson, C. (1983). Young adults’ and peers’ attitudes towards elderly hearing aid wearers. Australian Journal of Audiology 5(2), 57-62.

Rauterkus, E. & Palmer, C. (2014). The hearing aid effect in 2013. Journal of the American Academy of Audiology 25, 893-903.

Silverman, F. & Klees, J. (1989).  Adolescents’ attitudes toward peers who wear visible hearing aids. Journal of Communication Disorders 22(2), 147-150.

 

Tinnitus Treatment through Sound Therapy

Henry, J., Frederick, M., Sell, S., Griest, S. & Abrams, H. (2014). Validation of a novel combination hearing aid and tinnitus therapy device. Ear and Hearing, e-published ahead of print, September 2014.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

Background

Most tinnitus management programs include a combination of counseling and sound therapy (Jastreboff, 1990; Jastreboff & Hazell, 2004). The goals of sound therapy for tinnitus treatment include achieving immediate relief as well as facilitating long-term habituation to the tinnitus (Vernon, 1988; Jastreboff & Hazell, 1998). Many sound generators or tinnitus masking devices offer only basic amplification features because they were intended primarily for tinnitus treatment through sound therapy. Current combination devices with advanced digital signal processing can provide improved audibility and comfort in addition to offering noise stimuli (i.e., sound therapy) for tinnitus management. Some estimates report that up to 90% of patients with tinnitus may benefit from amplification (Johnson, 1998; Schechter et al., 2002) so combination hearing aid / sound therapy devices are a valuable tool for tinnitus treatment and hearing loss remediation.

Most scientific studies support the potential benefit of hearing aids for tinnitus management. In a recent literature review, Shekhawat et al. (2013) reported that 17 of 18 research studies included the use of hearing aids in tinnitus treatment, but they highlighted the absence of randomized control trials with hearing aids that include sound therapy options. Parazzini et al. (2010) found that open-fit hearing aids were as effective as sound generator-only devices for use in tinnitus therapy, but they did not investigate combination devices. A primary goal of therapy is to reduce tinnitus awareness, so combination devices could be particularly beneficial because they employ masking stimuli as well as amplified environmental sound that may effectively draw attention away from the tinnitus. Though this proposition has merit, it has not yet been supported by scientific evidence. To this end, Henry and his colleagues prepared a randomized, controlled trial to investigate the benefit of hearing aids versus combination devices for tinnitus management.

Methods and Findings

Thirty participants with mild-to-moderately severe, symmetrical, sensorineural hearing loss were recruited for this study. All had clinically significant tinnitus according to Section A of the Tinnitus and Hearing Survey (Henry et al., 2010a, 2012). At the first session, subjects completed audiometry, medical and tinnitus screening and responded to 3 questionnaires: the Tinnitus Functional Index (TFI; Meikle et al., 2012), the Hearing Handicap Inventory for the Elderly (HHIE; Ventry & Weinstein, 1982) and a general tinnitus survey.  The TFI evaluates the negative impact of tinnitus and measures changes in tinnitus impact after treatment. TFI scores range from 0 to 100, with higher scores indicating more severe problems. Scores of at least 25 are considered significant and a 13-point difference from one test administration to another is considered a significant change. The HHIE evaluates the social and emotional effects of hearing loss and higher scores indicate more social and emotional impact. In this study, the HHIE was administered face-to-face, so a change of 19 points from one session to another was considered significant.

At the second session, participants were fitted with receiver-in-canal (RIC) hearing instruments that included the Multiflex adjustable sound-generator. Most subjects used manufacturer’s silicone domes, but two required custom fitted acrylic earmolds. Hearing aids were programmed to NAL-NL2 targets, verified with real-ear measures and adjusted according for sound quality and comfort. Following hearing aid fitting, all participants received general tinnitus counseling derived from Progressive Tinnitus Management: Counseling Guide (Henry et al., 2010b). Following counseling, the experimental group had the tinnitus sound therapy in their hearing aids adjusted according to their individual preferences to obtain immediate relief from their tinnitus, while the control group was prescribed hearing aids without the tinnitus sound therapy.  The default settings for the modulated noise stimuli were based on the individual’s audiogram, but could be adjusted in 16 channels and subjects could select a slow, medium or fast modulation rate.

Approximately 3 to 4 months after the initial fitting appointment, participants returned to complete an exit interview. They were asked about their general impressions of hearing aids and experience of tinnitus relief and completed the TFI and HHIE inventories two more times; once to indicate their responses when they were using their hearing aids and again to indicate their responses when they were not using their hearing aids.

TFI and HHIE scores were obtained 3 times each: at the initial visit prior to hearing aid fitting and at the 3-month session, for responses referring to experiences with the hearing aids and without. The initial average TFI score for the overall subject group was 58.3. At the 3-month session, the average TFI scores were 22.2 (with hearing aids) and 44.8 (without hearing aids). Though the change in score for the with-hearing-aid condition was much larger, the reductions in score were significant for both conditions. For the control group, the initial score was 60.5 and at 3 months the average scores were 27.6 (with hearing aids) and 44.3 (without hearing aids). Again, both reductions were significant, though the effect size for the with-hearing-aids condition was much larger. For the experimental group, the initial average score was 56.1. At the 3-month session, the average scores were 16.8 (with hearing aids) and 45.3 (without hearing aids). The score reduction was significant for the with-hearing-aids condition but not for the without-hearing-aids condition. These outcomes indicate that both groups, regardless of whether the sound therapy was used or not, responded better to TFI questions with respect to when they were wearing the hearing aids versus when they were not.  There was no significant difference between the TFI score reductions for the control versus experimental groups, though the experimental group had a larger score reduction by about 6 points.

At the 3-month session, the average HHIE scores were 23.6 (with hearing aids) and 47.5 (without hearing aids). The score reduction was significant for the with-hearing-aid condition but was not for the without-hearing-aid condition. For the control group, the initial score was 55.3 and at 3 months the average scores were 26.9 (with hearing aids) and 47.5 (without hearing aids). For the experimental group, the initial average HHIE score was 49.3 and at the 3-month session the average scores were 20 (with hearing aids) and 47.5 (without hearing aids). Again, for both the control and experimental groups, the score reduction was significant for the with-hearing-aid condition but was not for the without-hearing-aid condition.  There was a significant main effect between initial scores and 3-month scores for the with-hearing-aid condition but not for the without-hearing-aid condition. There was also a significant difference between the two conditions at the 3-month session; the with-hearing-aid scores were significantly lower than without-hearing-aid scores.

Discussion

The findings of Henry and colleagues indicate that hearing aid use significantly reduces the negative effects of tinnitus, regardless of the presence or absence of sound therapy. Though there was not a significant difference between the control and experimental groups, the group using sound therapy had a larger reduction in TFI score than the group that used amplification alone. This difference approached but did not reach significance and the authors posit that perhaps with a larger subject group this difference would have been significant. HHIE results suggest that hearing aid benefit was not hampered by the use of sound therapy.

From a clinical perspective, several factors should be considered when fitting combination devices. The TFI is a good way to determine candidacy for combination devices, but a few key questions in the patient history can be helpful. We ask patients how they would rate their tinnitus and if it disrupts concentration, distracts or upsets them. It is also informative to ask if their tinnitus keeps them awake at night, though this concern is not directly addressed by the use of a combination device. Even a question about how motivated they are to seek treatment, such as the one employed in this study, can be indicative of candidacy.

After candidacy is established, there are still several factors to consider. Discussion of the individual’s tinnitus characteristics might help indicate which type of noise is most likely to be effective. Shaping the noise by frequency and intensity can help to achieve relief, while avoiding annoyance that may come with continued use. Clinicians should also discuss whether patients would like to use the noise constantly, in their main hearing aid program, or have it allocated to an alternate program for use as needed. We have found that most people prefer to have a “masking program” that they can use on occasion when their tinnitus is disruptive or annoying. For many people, this is in quiet conditions when they must concentrate on reading or quiet work. Follow-up consultations are critical to determine if the approach is working. Some individuals prefer to modify the characteristics of their sound therapy at later visits, either increasing or decreasing the intensity or shaping the frequency bands. The TFI is useful as a follow-up measure, but it should probably be administered after a few months of use, to make sure that programming adjustments are worked out before treatment efficacy is assessed.

References

Bock, K. & Abrams, H. (2013). An evaluation of the efficacy of a remotely driven auditory training program. Biennial NCRAR International Conference: Beyond the Audiology Clinic: Innovations and Possibilities of Connected Health. Portland, OR.

Coles, R. (2000). Medicolegal issues. In R.S. Tyler (Ed.). Tinnitus Handbook (pp. 399-417). San Diego: Singular Publishing Group.

Henry, J., Frederick, M., Sell, S., Griest, S. & Abrams, H. (2014). Validation of a novel combination hearing aid and tinnitus therapy device. Ear and Hearing, e-published ahead of print, September 2014.

Henry, J., Zaugg, T. & Myers, P. (2010a). Progressive Tinnitus Management: Clinical Handbook for Audiologists. San Diego, CA: Plural Publishing.

Henry, J., Zaugg, T. & Myers, P. (2010b).  Progressive Tinnitus Management: Counseling Guide. San Diego, CA: Plural Publishing.

Henry, J., Zaugg, T. & Myers, P. (2012). Pilot study to develop telehealth tinnitus management for persons with and without traumatic brain injury. Journal of Rehabilitation Research Developments 49, 1025-1042.

Hoffman, H. & Reed, G. (2004). Epidemiology of tinnitus. In J.B. Snow (Ed.). Tinnitus: Theory and Management (pp. 16-41). Lewiston, NY: BC Decker, Inc.

Humes, L., Wilson, D. & Barlow, N. (2002). Longitudinal changes in hearing aid satisfaction and usage in the elderly over a period of one or two years after hearing aid delivery. Ear and Hearing 23, 428-438.

Jastreboff, P. (1990). Phantom auditory perception (tinnitus): Mechanisms of generation and perception. Neuroscience Research 8, 221-254.

Jastreboff, P.  & Hazell, J. (1998). Treatment of tinnitus based on a neurophysiological model. In J.A. Vernon (Ed.). Tinnitus Treatment and Relief (pp. 201-217). Needham Heights: Allyn & Bacon.

Jastreboff, P. & Hazell, J. (2004). Tinnitus Retraining Therapy: Implementing the Neurophysiological Model. Cambridge University Press.

Johnson, R. (1998). The masking of tinnitus. In J.A. Vernon (Ed.). Tinnitus Treatment and Relief (pp. 164-186). Needham Heights: Allyn & Bacon.

Meikle, M. & Taylor-Walsh, E. (2012). Characteristics of tinnitus and related observations in over 1800 tinnitus patients. Proceedings of the Second International Tinnitus Seminar. New York 1983. Ashford, Kent, Invicta Press. Journal of Laryngology and Otology Suppl. 9, 17-21.

Mulrow, C., Tuley, M. & Aguilar, C. (1992). Sustained benefits of hearing aids. Journal of Speech and Hearing Research 35, 1402-1405.

Parazzini, M., Del Bo, L., Jastreboff, M., Tognola, G. & Ravazzani, P. (2010). Open ear hearing aids in tinnitus therapy: An efficacy comparison with sound generators. International Journal of Audiology 2011 Early Online, 1-6.

Schechter, M., Henry, J. & Zaugg, T. (2002). Selection of ear level devices for two different methods of tinnitus treatment. VIIth International Tinnitus Seminar Proceedings. R. Patuzzi. Perth, Physiology Department, University of Western Australia, p. 13.

Shekhawat, G., Searchfield, G. & Stinear, C. (2013). Role of hearing aids in tinnitus intervention: A scoping review. Journal of the American Academy of Audiology 24, 747-762.

Surr, R., Montgomery, A. & Mueller, H. (1985). Effect of amplification on tinnitus among new hearing aid users. Ear and Hearing 6, 71-75.

Ventry, I. & Weinstein, B. (1982). The hearing handicap inventory for the elderly: A new tool. Ear and Hearing 3, 128-134.

Vernon, J. (1988). Current use of masking for the relief of tinnitus. In M. Kitahara (Ed.). Tinnitus. Pathophysiology and Management (pp. 96-106). Tokyo: Igaku-Shoin.

Vernon, J. (1992).  Tinnitus: causes, evaluation and treatment. In G.M. English (Ed.). Otolaryngology (Revised Edition), pp. 1-25. Philadelphia: J.B. Lippincott.

Can hearing aid settings improve working memory?

Souza, P., & Sirow, L. (2014). Relating working memory to compression parameters in clinically-fit hearing aids. American Journal of Audiology, Just Accepted, released August 14.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

Working memory provides short-term processing and storage of information during complex cognitive tasks, combining information from numerous sources into a coherent whole (Baddeley, 1992). Incoming stimuli are compared and matched to long-term memory representations, prior to identification and further processing.

The term working memory describes our ability to store and process information during cognitively demanding tasks. In the context of hearing, working memory capacity affects ones ability to match speech inputs with stored representations of that speech. Several studies suggest that individuals with impaired working memory experience increased difficulty understanding speech in complex listening environments (Lunner, 2003). It is assumed that working memory tends to decline with advancing age (Salthouse, 1994). Therefore, it is important to understand how these variables affect speech perception and how they interact with each other, particularly for older hearing aid users.

Working memory may impact the optimal hearing aid characteristics for an individual and a number of studies have investigated the relationship between working memory and wide-dynamic range compression (Foo, et al., 2007; Gatehouse, et al., 2006; Lunner & Sundewall-Thoren, 2007; Ohlenforst, et al., 2014).  In these studies,  hearing aid compression speed was examined while keeping other amplification characteristics constant. Subjects with better working memory were generally found to perform better with fast-acting compression, whereas subjects with poorer working memory performed better with slow-acting compression.  The authors interpret these results as an indication that fast-acting compression alters the speech envelope in ways that make it more difficult to match incoming stimuli to stored lexical representations (Jenstad & Souza, 2007; Jenstad & Souza, 2005; Ronnberg et al., 2013; Ronnberg et al., 2008).

Laboratory experiments inherently must control the variables under study in order to glean meaningful interpretations. However, comparing fast and slow compression speed in isolation does not represent the typical conditions of a clinical hearing aid fitting, in which these characteristics are not independently adjustable. Furthermore, with changes in compression speed from one hearing aid model to another, many other variables are likely to differ as well, such as feedback management, noise reduction characteristics and the number of compression channels. These considerations make it difficult to extrapolate laboratory findings to everyday clinical experiences. The goal of Souza and Sirow’s study was to examine how compression speed and working memory relate to each other, using selection, fitting and verification techniques as they would typically be used in a clinical setting.

Twenty-seven participants with hearing loss were fitted with at least three different sets of receiver-in-canal hearing instruments, from several manufacturers. Because only one manufacturer offered an aid with adjustable compression speed, each subject completed a comparison of two compression settings with this single hearing aid, plus two or three additional models from other manufacturers that varied in their compression characteristics.  All aids were fitted with closed domes in the appropriate size for the individual. Real-ear verification and adjustments to prescribed levels were completed as they would in a typical clinical hearing aid fitting, to ensure audibility and comfort.  Aids were programmed with omnidirectional microphones and special hearing aid parameters such as feedback management and noise reduction were set according to manufacturer defaults.

Working memory is often assessed with a dual-paradigm task, in which the subject is required to process information while storing it for later recall. In this study, working memory was assessed with a reading span test, the same procedure used in previous studies of hearing aid compression and working memory. Subjects were presented with five-word sentences flashed on a computer screen and were asked to judge if the sentences made sense or not.  Sentences were presented one at a time in blocks of three, four or five. After each block, subjects were asked to recall either the first or last words of the sentences. The working memory score was taken as the percentage of correctly recalled words across all blocks.

Speech intelligibility was tested using the QuickSIN (Killion, et al., 2004) test, because of its ease of clinical administration and similarity to test materials and conditions in prior studies of compression (Lunner & Sundelwall-Thoren, 2007).  The test was administered in a sound booth via loudspeaker at a 0-degree azimuth, at 70dBHL for most subjects.  The QuickSIN yields an SNR loss score, which indicates the increase in SNR required to achieve a performance threshold. Larger SNR loss scores represent poorer performance.

Correlations were calculated to examine the relations among working memory, age, degree of hearing loss and speech-in-noise performance.  Not surprisingly, increases in age and degree of hearing loss were associated with poorer scores on the QuickSIN test. Working memory scores were also significantly correlated with aided QuickSIN scores.  Lower working memory scores were loosely associated with increased age and poorer unaided QuickSIN scores, but these relationships did not reach significance.

Reading span test scores, representing working memory, ranged from 17% to 50%, with a mean of 34%.  As in previous studies, subjects were divided into high and low working memory groups, based on the median score for the group. For slower compression speeds, comparable performance was achieved by both high and low working memory groups. At faster compression speeds, individuals in the high working memory group performed better than those in the low working memory group. For the fastest compression times, the difference in SNR loss between the high and low working memory groups was greater than 5dB. The authors point out that this is a substantial difference, as a QuickSIN SNR loss difference of 2.7dB is considered significant.

Aided QuickSIN scores were significantly affected by working memory for fast compression speed, but not for slow compression speed. There was high variability in the scores, especially for slow compression times, so further analysis was conducted to examine the contributions of other variables. For fast compression speeds, working memory and hearing loss accounted for most of the variance. For slow compression speeds, age and hearing loss were significant predictors of performance, but working memory was not.

The results of this study are consistent with previous reports suggesting that listeners with low working memory may not perform well with fast acting compression, whereas those with high working memory can be expected to do better.  The findings of the current study appear particularly robust because they emerged under less controlled conditions than in the laboratory studies. The authors point out that even in the hearing aid that allowed manipulation of compression speed, changing it resulted in other changes in signal processing as well.  The fact that compression speed still had a significant effect on speech-in-noise performance under these conditions is support for its relationship with working memory.

Though further study is needed to illuminate the relationship between working memory and the selection of hearing aid parameters, there are a number of potential benefits to incorporating working memory tests into clinical practice. The working memory assessment could help to explain poor performance with a current set of hearing aids and indicate the need for new aids or adjustments to signal processing parameters, if possible. Souza and Sirow offer a cautionary statement regarding the use of working memory assessment during a diagnostic hearing evaluation. They suggest that patients may not understand the link between auditory assessment and a task that could involve assessment of memory. With that cautionary consideration, hearing care providers may be more likely than other clinical professionals to recognize symptoms of cognitive decline. Atypical results of a working memory assessment may provide insight into a patient’s performance with hearing aids as well as their general cognitive health, prompting referrals to a primary care physician or other specialists.

The study of hearing loss, hearing aids, cognition and memory is an interesting area of inquiry with potentially important implications for clinical hearing aid fitting. Souza and Sirow’s report on the relationship between working memory and compression speed illustrates how individual variability in working memory could have specific impact on the selection of hearing aid characteristics.  Their findings represent an important link between laboratory investigation on this topic and the clinical prescription of hearing aids.

References

Baddeley, A. (1992). Working memory. Science 255 (5044), 556-559.

Foo, C., Rudner, M., Ronnberg, J. & Lunner, T. (2007). Recognition of speech in noise with new hearing instrument compression release settings requires explicit cognitive storage and processing capacity. Journal of the American Academy of Audiology 18(7), 618-631.

Gatehouse, S., Naylor, G. & Elberling, C. (2006). Linear and nonlinear hearing aid fittings: 2. Patterns of candidature. International Journal of Audiology 45(3), 153-171.

Jenstad, L. & Souza, P. (2005). Quantifying the effect of compression hearing aid release time on speech acoustics and intelligibility. Journal of Speech, Language and Hearing Research 48(3), 651-667.

Jenstad, L. & Souza, P. (2007). Temporal envelope changes of compression and speech rate: combined effects on recognition for older adults. Journal of Speech, Language and Hearing Research 50(5), 1123-1138.

Killion, M., Niquette, P., Gudmundsen, G., Revit, L. & Banerjee, S. (2004). Development of a quick speech-in-noise test for measuring signal-to-noise ratio loss in normal-hearing and hearing-impaired listeners. Journal of the Acoustical society of America 116(4), 2395-2405.

Lunner, T. (2003). Cognitive function in relation to hearing aid use. International Journal of Audiology 42 (Suppl.): S49-S58.

Lunner, T. & Sundewall-Thoren, E. (2007). Interactions between cognition, compression and listening conditions: effects on speech-in-noise performance in a two-channel hearing aid. Journal of the American Academy of Audiology 18(7), 604-617.

Ohlenforst, B., Souza, P. & MacDonald, E. (2014). Interaction of working memory, compressor speed and background noise characteristics. Paper presented at the American Auditory Society, Scottsdale, AZ.

Remensnyder, L. (2012). Audiologists as gatekeepers and it’s not just for hearing loss. Audiology Today, July/August,  24-31.

Ronnberg, J., Rudner, M., Foo, C. & Lunner, T. (2008 ). Cognition counts: a working memory system for ease of language understanding (ELU). International Journal of Audiology 47(Suppl. 2), S99-105.

Ronnberg, J., Lunner, T., Zekveld, A., Sorqvist, P., Danielsson, H., Lyxell, B. & Rudner, M. (2013). The Ease of Language Understanding (ELU) model: theoretical, empirical and clinical advances. Frontiers in Systems Neuroscience 7, 31.

Rudner, M., Foo, C., Ronnberg, J. & Lunner, T. (2009). Cognition and aided speech recognition in noise: specific role for cognitive factors following nine-week experience with adjusted compression settings in hearing aids. Scandinavian Journal of Psychology 50(5), 405-418.

Salthouse, T. (1994). The aging of working memory. Neuropsychology 8(4), 535-543.

Considerations for Music Listening

Croghan, N., Arehart, K. & Kates, J.  (2014). Music preferences with hearing aids: effects of signal properties, compression settings and listener characteristics. Ear & Hearing, in press.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

For the hearing aid wearer, speech is arguably the most important sound but hearing aid satisfaction is affected by the way in which the devices process other environmental sounds, including music. Adoption of hearing aids by active, technology savvy users makes their ability to process music with optimal sound quality and minimal distortion is more important than ever.  Though modern hearing aids do an effective job of processing speech, even in the presence of competing noise, many hearing aid users report that hearing aids either make no difference or make music less enjoyable (Leek et al., 2008).

Music and speech have different spectral and temporal characteristics, with music often being higher in intensity and more dynamic than speech (Chasin, 2003, 2006, 2010).  Speech maintains somewhat similar and predictable acoustic characteristics across talkers; in contrast, the spectral and temporal characteristics of music vary widely from one instrument to another and one piece to another (Chason & Russo, 2004). Not surprisingly, some studies have indicated that the best hearing aid circuit characteristics and settings for speech recognition may not be optimal for music perception (Higgins et al, 2012; van Buuren et al., 1999). For instance, faster compression time constants may be helpful for restoring speech audibility and loudness perception (Moore, 2008) but listeners may prefer longer release times for listening to music (Hansen, 2002; Moore, 2011).

Recorded music heard by hearing-aid users is subject to two stages of compression; compression limiting during the studio recording and wide-dynamic range compression in the hearing aid. Processing at both of these stages could impact music sound quality and subsequent enjoyment by the listener.  Croghan and colleagues investigated the acoustic and perceptual effects of compression on music processed through hearing aids. They examined the effect of compression limiting prior to hearing aid processing and compared slow versus fast hearing aid compression time constants as well as small versus large numbers of channels. In addition to these compression variables, they examined potential effects of suprathreshold processing and prior musical training.

Eighteen hearing aid users, ranging from 49 to 87 years of age, participated in the study. Subjects were divided into non-musician and musician groups. Two pieces of music, one classical and one rock, were used in the study. The pieces were selected to be relatively unfamiliar to the subjects, to reduce any effect of prior experience or expectations. To simulate studio processing, the music samples were recorded in three compression limiting conditions: no compression, mild compression limiting, and heavy compression limiting.  These compression conditions were applied to the music samples prior to hearing aid processing.

Music was presented over binaural headphones, via a simulated hearing aid.  Individual WDRC hearing aid simulations were programmed according to NAL-NL1 formulae for each subject. Stimuli were processed with two sets of compression release times and processing channels: fast (50msec) vs. slow (1000msec) release times and 3-channels vs. 18 channels.  Two linear conditions were also included, using the NAL-R prescription with 3-channels and 18-channels for frequency shaping.  The combination of 3 compression limiting, 4 WDRC and 2 linear conditions resulted in 18 processing conditions for each piece of music.   Stimuli were presented at 65dB SPL and subjects made preference judgments in a 2-interval forced-choice paradigm.

To examine the effect of suprathreshold processing, three psychophysical tests were administered. Loudness perception was measured with the Contour Test of Loudness Perception (Cox et al., 1997), amplitude modulation depth discrimination was measured using speech-shaped noise modulated at 4Hz and frequency selectivity was measured with psychophysical tuning curves (Sek et al. 2005; Sek & Moore, 2011). The music stimuli were also analyzed with a modification of the Hearing Aid Speech Quality Index (HASQI; Kates & Arehart, 2010). Roughly stated, the HASQI provides an object sound quality rating by comparing time-frequency modulation and long-term spectrum of an unmodified signal to a modified one (the modified signal being one with the targeted signal processing applied). Not surprisingly, the lowest HASQI values, indicating the most difference between unprocessed and processed stimuli, were observed for fast WDRC combined with heavy compression limiting.

The 18 stimulus conditions were examined for the effect of compression on the overall dynamic range, amplitude by frequency and modulation of the music samples.  Generally any increase in processing – increasing compression limiting, increasing the number of channels, going from linear to slow WDRC or slow to fast WDRC – reduced the dynamic range of the classical and rock music samples. WDRC caused more dynamic range reduction in the high frequencies. Compression limiting affected classical music similarly across frequencies, whereas rock music was affected more in the high and low-frequency regions than in the mid-frequencies. Compression – either WDRC or limiting – reduced the magnitude of modulation, likely making the rhythmic structure of the music less distinguishable.

Listener preference results for compression limiting and WDRC indicated some differences based on the type of music that was presented. For classical music, there was no significant difference between slow WDRC and linear processing, but both of these were preferred over fast WDRC. Mild or no compression was significantly preferred over heavy compression limiting.  There was no effect for the number of channels on classical music preferences.  Slightly different results were obtained for rock music: linear processing was preferred over both WDRC conditions and slow WDRC was significantly preferred over fast WDRC. There was no significant effect of compression limiting but the 3-channel condition was rated significantly better than the 18-channel condition.

The following listener-related factors were examined for their effects on preference: gender, musician vs. non-musician, PTA, dynamic range, tuning curve bandwidth and modulation depth discrimination threshold. Because PTA and dynamic range were strongly correlated to each other, these factors were excluded from the analysis.  For classical music, the only significant findings were interactions among tuning curve bandwidth, WDRC condition and number of processing channels. Listeners with broader tuning curves showed a slight preference for linear amplification over WDRC and 3-channel over 18-channel processing. In contrast, the group with narrower tuning curves had a slight preference for slow WDRC and 18-channel processing. There were no other significant findings for listener-related factors. The authors posit that listeners with better frequency resolution may have preferred slow WDRC and 18-channel processing because they were able to resolve the harmonics and benefit from greater audibility. Conversely, listeners with poorer frequency resolution may have responded to reduced distortion in the linear and 3-channel conditions, despite potentially reduced audibility.

In this study, Croghan and her colleagues found that for music stimuli, compression limiting and WDRC reduced temporal envelope contrasts. These results are in agreement with previous studies using speech stimuli (Bor et al., 2008; Jenstad & Souza, 2005). They also found that compression limiting was more likely than WDRC to reduce the peaks of the modulation spectrum. This is somewhat in agreement with a previous report by Souza & Gallun (2010) on consonant discrimination, in which hearing aid compression limiting had an adverse effect but multi-channel, fast WDRC was beneficial.  However, the authors point out that hearing aid compression limiting is different from music industry compression limiting in that the former compresses only high-level sounds and does not affect average (RMS) sound level.

The results of this study indicate that music was adversely affected by compression limiting and WDRC and that in general, listeners preferred listening to music with little or no compression. Listeners with broad psychophysical tuning curves showed a preference for 3-channel processing, whereas those with narrower tuning curves preferred 18-channel processing. This may be related to the ability of those with narrower tuning curves to perceive harmonics, especially in the classical piece, which is related to the perceived quality of stringed instruments (Chasin & Russo, 2004).  This result is similar to a report using speech stimuli by Souza et al. (2012) in which listeners with better frequency resolution were more able to benefit from multi-channel compression. More research is needed to illuminate the relationship between suprathreshold processing and music perception. Traditional measures of psychophysical tuning curves is an unwieldy proposition for clinicians, but hearing aid users with impaired frequency resolution may require a modified treatment approach.

In contrast to previous studies in which musicians outperformed non-musicians on tests of frequency discrimination, speech discrimination and working memory (Parbery-Clark et al., 2009; 2012), Croghan and her colleagues found no significant difference in the psychophysical tests or preference ratings for musicians versus non-musicians.  They point out, however, that their study used recorded music samples and preferences for live music cannot be extrapolated from their results. Clinicians should expect musicians to be analytical about the sound quality of their hearing aids and be prepared to offer a separate, manually accessible programs for music listening. Similarly, many non-musicians are music aficionados who would also appreciate an alternate program for music. In many hearing instruments, alternate music programs can be added using defaults available in manufacturer software, or can be individually customized. In music listening programs, special features like automatic directionality and noise reduction should be disabled and based on Croghan’s report, should probably have more linear processing and less compression than primary, everyday listening programs.

Croghan’s study provides insight into the ways in which hearing aid signal processing affects music acoustics and perception. Our current knowledge of music acoustics and hearing aid signal processing may be more meaningfully applied to the technical design of hearing aids than to routine clinical practice. While opportunities remain for meaningful advancement in the processing of music through hearing aids, some clinical advice can be offered:

  • Musicians or individuals with strong musical interests may benefit from a dedicated memory, optimized for music listening.
  • Optimization of a dedicated music listening memory is best attempted following a patient’s initial adaptation period to new hearing aids.
  • Follow-up visits addressing music perception and sound quality should include multiple music samples of the patient’s own selection.
  • Using default settings for music listening, the patient should be prompted to set the playback loudspeakers to a level they find pleasing for a given music sample.

Although the preferences of each patient are different, these suggestions are a solid foundation for providing patients with a high-quality music listening experience.

 

References

Chasin, M. (2003). Music and hearing aids. Hearing Journal 56, 36-41.

Chasin, M. (2006). Hearing aids for musicians. Hearing Review 13, 11-16.

Chasin, M. (2010). Amplification fit for music lovers. Hearing Journal 63, 27-30.

Chason, M. & Russo, F. (2004). Hearing aids and music. Trends in Amplification 8, 35-47.

Cox, R., Alexander, G. & Taylor, I. (1997). The contour test of loudness perception. Ear and Hearing 18, 388-400.

Croghan, N., Arehart, K. & Kates, J.  (2014). Music preferences with hearing aids:

effects of signal properties, compression settings and listener characteristics. Ear & Hearing, in press.

Hansen, M. (2002). Effects of multi-channel compression time constants on subjectively perceived sound quality and speech intelligibility. Ear and Hearing 23, 369-380.

Higgins, P., Searchfield, G. & Coad, G. (2012). A comparison between the first-fit settings of two multichannel digital signal-processing strategies: music quality ratings and speech-in-noise scores. American Journal of Audiology 21, 13-21.

Leek, M., Molis, M. & Kubli, L. (2008).  Enjoyment of music by elderly hearing-impaired listeners. Journal of the American Academy of Audiology 19, 519-526.

Moore, B. (2008) . The choice of compression speed in hearing aids: Theoretical and practical considerations and the role of individual differences. Trends in Amplification 12, 103-112.

Moore, B., Fullgrabe, C. & Stone, M. (2011).  Determination of preferred parameters for multichannel compression using individually fitted simulated hearing aids and paired comparisons. Ear and Hearing 32, 556-568.

Moore, B. & Glasberg, B. (1997) A model of loudness perception applied to cochlear hearing loss. Auditory Neuroscience 3, 289-311.

Neumann, A., Bakke, M. & Hellman, S. (1995a). Preferred listening levels for linear and slow-acting compression hearing aids. Ear and Hearing 16, 407-416.

Parbery-Clark, A., Skoe, E. & Lam, C. (2009). Musician enhancement for speech-in-noise. Ear and Hearing 30, 653-661.

Parbery-Clark, A., Tierney, A. & Strait, D. (2012). Musicians have fine-tuned neural distinction of speech syllables. Neuroscience 219, 111-119.

Sek, A., Alcantara, J. & Moore, B. ( 2005). Development of a fast method for determining psychophysical tuning curves. International Journal of Audiology 44, 408-420.

Sek, A. & Moore, B. (2011). Implementation of a fast method for measuring psychophysical tuning curves. International Journal of Audiology 50, 237-242.

van Buuren, R., Festen, J. & Houtgast, T. (1999).  Compression and expansion of the temporal envelope: Evaluation of speech intelligibility and sound quality. Journal of the Acoustical Society of America 105, 2903-2913.

A Pediatric Prescription for Listening in Noise

Crukley, J. & Scollie, S. (2012). Children’s speech recognition and loudness perception with the Desired Sensation Level v5 Quite and Noise prescriptions. American Journal of Audiology 21, 149-162.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

Most hearing aid prescription formulas attempt to balance audibility of sound with perception of loudness, while keeping the amplified sound within a patient’s dynamic range (Dillon, 2001; Gagne et al., 1991a; Gagne et al., 1991b; Seewald et al., 1985). Use of a prescriptively appropriate hearing aid fitting is particularly important for children with hearing loss. For the needs of language development, they benefit from a higher proportion of audible sound and broader bandwidth than diagnostically similar older children and adults (Pittman & Stelmachowicz, 2000; Stelmachowicz et al., 2000; Stelmachowicz et al., 2001; Stelmachowicz et al., 2004; Stelmachowicz et al., 2007).

Historically, provision of access to speech in quiet has been a primary driver in the development of prescription formulas for hearing aid.  However, difficulty understanding speech in noise is one of the primary complaints of all hearing aid users, including children. In a series of studies compared NAL-NL1 and DSL v4.1 fittings and examined children’s listening needs and preferences (Ching et al., 2010; Ching et al., 2010; Scollie et al., 2010) two distinct listening categories were identified: loud, noisy and reverberant environments and quiet or low-level listening situations. The investigators found that children preferred the DSL fitting in quiet conditions but preferred the NAL fitting for louder sounds and when listening in noisy environments. Examination of the electroacoustic differences between the two fittings showed that the DSL fittings provided more gain overall and approximately 10dB more low-frequency gain than the NAL-NL1 fittings.

To address the concerns of listening in noisy and reverberant conditions, DSL v5 includes separate prescriptions for quiet and noise. Relative to the formula for quiet conditions, the noise formula prescribes higher compression thresholds, lower overall gain, lower low-frequency gain and more relative gain in the high frequencies.  This study of Crukley and Scollie addressed whether the use of the DSL v5 Quiet and Noise formulae resulted in differences in consonant recognition in quiet, sentence recognition in noise and different loudness ratings.  Because of the lower gain in the noise formula, it was expected to reduce loudness ratings and consonant recognition scores in quiet because of potentially reduced audibility. There was no expected difference for speech recognition in noise, as the noise floor was considered the primary limitation to audibility in noisy conditions.

Eleven children participated in the study; five elementary school children with an average age of 8.85 years and six high school children with an average age of 15.18 years. All subjects were experienced, full-time hearing aid users with congenital, sensorineural hearing losses, ranging from moderate to profound.  All participants were fitted with behind-the-ear hearing aids programmed with two separate programs: one for DSL Quiet targets and one for DSL Noise targets. The Noise targets had, on average, 10dB lower low-frequency gain and 5dB lower high-frequency gain, relative to the Quiet targets. Testing took place in two classrooms: one at the elementary school and one at the high school.

Consonant recognition in quiet conditions was tested with the University of Western Ontario Distinctive Features Differences Test (UWO-DFD; Cheesman & Jamieson, 1996). Stimuli were presented at 50dB and 70dB SPL, by a male talker and a female talker. Sentence recognition in noise was performed with the Bamford-Kowal-Bench Speech in Noise Test (BKB-SIN; Niquette et al., 2003). BKB-SIN results are scored as the SNR at which 50% performance can be achieved (SNR-50).

Loudness testing was conducted with the Contour Test of Loudness Perception (Cox et al., 1997; Cox & Gray, 2001), using BKB sentences presented in ascending then descending steps of 4dB from 52dB to 80dB SPL. Subjects rated their perceived loudness on an 8-point scale ranging from “didn’t hear it” up to “uncomfortably loud” and indicated their response on a computer screen. Small children were assisted by a researcher, using a piece of paper with the loudness ratings, and then the researcher entered the response.

The hypotheses outlined above were generally supported by the results of the study. Consonant recognition scores in quiet were better at 70dB than 50dB for both prescriptions and there was no significant difference between the Quiet and Noise fittings. There was, however, a significant interaction between prescription and presentation level, showing that performance for the Quiet fittings was consistent at the two levels but was lower at 50dB than 70dB for the Noise fittings. The change in score from Quiet to Noise at 50dB was 4.2% on average, indicating that reduced audibility in the Noise fitting may have adversely affected scores at the lower presentation level. On the sentence recognition in noise test, BKB-SIN scores did not differ significantly between the Quiet and Noise prescriptions, with some subjects scoring better in the Quiet program, some scoring better in the Noise program and most not demonstrating any significant difference between the two. Loudness ratings were lower on average for the Noise prescription. When ratings for 52-68dB SPL and 72-80dB SPL were analyzed separately, there was no difference between the Quiet and Noise prescriptions for the lower levels but at 72dB and above, the Noise prescription yielded significantly lower loudness ratings.

Although the average consonant recognition scores for the Noise prescription were only slightly lower than those for the Quiet prescription, it may not be advisable to use the Noise prescription as the primary program for regular daily use, because of the risk of reduced audibility. This is especially true for pediatric hearing aid patients, for whom maximal audibility is essential for speech and language development. Rather, the Noise prescription is better used as an alternate program, to be accessed manually by the patient, teacher or caregiver, or via automatic classification algorithms within the hearing aid. Though the Noise prescription did not improve speech recognition in noise, it did not result in a decrement in performance and yielded lower loudness ratings, suggesting that in real world situations it would improve comfort in noise while still maintaining adequate speech intelligibility.

Many audiologists find that patients prefer a primary program set to a prescriptive formula (DSL v5, NAL-NL2 or proprietary targets) for daily use but appreciate a separate, manually accessible noise program with reduced low-frequency gain and increased noise reduction. This is true even for the majority of patients who have automatically switching primary programs, with built-in noise modes. Anecdotal remarks from adult patients using manually accessible noise programs agree with the findings of the present study, in that most people use them for comfort in noisy conditions and find that they are still able to enjoy conversation.

For the pediatric patient, prescription of environment specific memories should be done on a case-by-case basis. Patients functioning as teenagers might be capable of managing manual selection of a noise program in appropriate conditions. Those of a functionally younger age will require assistance from a supervising adult. Personalized, written instructions will assist adult caregivers to ensure that they understand which listening conditions may be uncomfortable and what actions should be taken to adjust the hearing aids. Most modern hearing aids feature some form of automatic environmental classification: ambient noise level estimation being one of the more robust classifications. Automatic classification and switching may be sufficient to address concerns of discomfort. However, the details of this behavior vary greatly among hearing aids. It is essential that the prescribing audiologist is aware of any automatic switching behavior and works to verify each of the accessible hearing aid memories.

Crukley and Scollie’s study supports the use of separate programs for everyday use and noisy conditions and indicates that children could benefit from this approach. The DSL Quiet and Noise prescriptive targets offer a consistent and verifiable method for this approach with children, while also providing potential guidelines for designing alternate noise programs for use by adults with hearing aids.

 

References

Cheesman, M. & Jamieson, D. (1996). Development, evaluation and scoring of a nonsense word test suitable for use with speakers of Canadian English. Canadian Acoustics 24, 3-11.

Ching, T., Scollie, S., Dillon, H. & Seewald, R. (2010). A crossover, double-blind comparison of the NAL-NL1 and the DSL v4.1 prescriptions for children with mild to moderately severe hearing loss. International Journal of Audiology 49 (Suppl. 1), S4-S15.

Ching, T., Scollie, S., Dillon, H., Seewald, R., Britton, L. & Steinberg, J. (2010). Prescribed real-ear and achieved real life differences in children’s hearing aids adjusted according to the NAL-NL1 and the DSL v4.1 prescriptions. International Journal of Audiology 49 (Suppl. 1), S16-25.

Cox, R., Alexander, G., Taylor, I. & Gray, G. (1997). The contour test of loudness perception. Ear and Hearing 18, 388-400.

Cox, R. & Gray, G. (2001). Verifying loudness perception after hearing aid fitting. American Journal of Audiology 10, 91-98.

Crandell, C. & Smaldino, J. (2000). Classroom acoustics for children with normal hearing and hearing impairment. Language, Speech and Hearing Services in Schools 31, 362-370.

Crukley, J. & Scollie, S. (2012). Children’s speech recognition and loudness perception with the Desired Sensation Level v5 Quite and Noise prescriptions. American Journal of Audiology 21, 149-162.

Dillon, H. (2001). Prescribing hearing aid performance. Hearing Aids (pp. 234-278). New York, NY: Thieme.

Jenstad, L., Seewald, R., Cornelisse, L. & Shantz, J. (1999). Comparison of linear gain and wide dynamic range compression hearing aid circuits: Aided speech perception measures. Ear and Hearing 20, 117-126.

Niquette, P., Arcaroli, J., Revit, L., Parkinson, A., Staller, S., Skinner, M. & Killion, M. (2003). Development of the BKB-SIN test. Paper presented at the annual meeting of the American Auditory Society, Scottsdale, AZ.

Pittman, A. & Stelmachowicz, P. (2000). Perception of voiceless fricatives by normal hearing and hearing-impaired children and adults. Journal of Speech, Language and Hearing Research 43, 1389-1401.

Scollie, S. (2008). Children’s speech recognition scores: The speech intelligibility index and proficiency factors for age and hearing level. Ear and Hearing 29, 543-556.

Scollie, S., Ching, T., Seewald, R., Dillon, H., Britton, L., Steinberg, J. & Corcoran, J. (2010). Evaluation of the NAL-NL1 and DSL v4.1 prescriptions for children: Preference in real world use. International Journal of Audiology 49 (Suppl. 1), S49-S63.

Scollie, S., Ching, T., Seewald, R., Dillon, H., Britton, L., Steinberg, J. & King, K. (2010). Children’s speech perception and loudness ratings when fitted with hearing aids using the DSL v4.1 and NAL-NL1 prescriptions. International Journal of Audiology 49 (Suppl. 1), S26-S34.

Seewald, R., Ross, M. & Spiro, M. (1985). Selecting amplification characteristics for young hearing-impaired children. Ear and Hearing 6, 48-53.

Stelmachowicz, P., Hoover, B., Lewis, D., Kortekaas, R. & Pittman, A. (2000). The relation between stimulus context, speech audibility and perception for normal hearing and hearing impaired children. Journal of Speech, Language and Hearing Research 43, 902-914.

Stelmachowicz, P., Pittman, A., Hoover, B. & Lewis, D. (2001). Effect of stimulus bandwidth on the perception of /s/ in normal and hearing impaired children and adults. The Journal of the Acoustical Society of America 110, 2183-2190.

Stelmachowicz, P. Pittman, A., Hoover, B. & Lewis, D. (2004). Novel word learning in children with normal hearing and hearing loss. Ear and Hearing 25, 47-56.

Stelmachowicz, P. Pittman, A., Hoover, B., Lewis, D. & Moeller, M. (2004). The importance of high-frequency audibility in the speech and language development of children with hearing loss. Archives of Otolaryngology, Head and Neck Surgery 130, 556-562.

Stelmachowicz, P., Lewis, D., Choi, S. & Hoover, B. (2007).  Effect of stimulus bandwidth on auditory skills in normal hearing and hearing impaired children.  Ear and Hearing 28, 483-494.

On the prevalence of hearing loss and barriers to hearing aid uptake

Dawes, P., Fortnum, H., Moore, D., Emsley, R., Norman, P., Cruickshanks, K., Davis, A., Edmondson-Jones, M., McCormack, A., Lutman, M. & Munro, K.  (2014) Hearing in middle age: a population snapshot of 40- to 69-year olds in the United Kingdom. Ear & Hearing 35 (3), 44-51.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

The Biobank is a national program in the United Kingdom, aimed at longitudinal investigation of the prevention, diagnosis and treatment of diseases and health conditions affecting middle-aged individuals. Since 2006, the Biobank has recruited over half a million participants, who complete test procedures, provide biomedical samples and detailed health information and have their health followed over time, periodically providing updated information. One of the health conditions assessed in the Biobank study is hearing loss and over 160,000 participants have completed questionnaires, audiometric assessment and speech-in-noise testing.

Dawes and his colleagues used Biobank data to examine the prevalence of hearing impairment among 164,700 middle-aged respondents in the U.K., “hearing impairment” was defined as reduced or poor performance on a speech recognition in noise test. They assessed how audiologic and demographic factors relate to hearing impairment and the use of hearing aids among individuals in this age group.

For the Biobank database, hearing loss was assessed via audiometric testing and questionnaires covering lifestyle, environment and medical history, including associated symptoms such as tinnitus. Speech recognition in noise was assessed via the Digit Triplet Test (DTT; Smits et al., 2004). The DTT is a large-scale screening tool that can be administered via the telephone and internet.  The test includes 15 sets of monosyllable digit triplets, presented at a comfortable listening level. Noise levels are varied adaptively to arrive at the SNR required for 50% recognition. Speech recognition results were analyzed in relation to several demographic variables: age, work and music related noise exposure socioeconomic status, ethnicity and gender. 

10.7 % of participants had hearing impairment, as measured by the DTT. Tinnitus was reported by 16.9% of the subjects, which is consistent with previous reports (Davis 1995).  The results show, not surprisingly, that the prevalence of hearing loss increases with increasing age, with an acceleration of prevalence beginning in the 55-59 year old age group. The increase in prevalence with increasing age is consistent with previously published reports for this age group (Plomp & Mimpen, 1979; Wilson & Strouse, 2002; Smits et al., 2006). Tinnitus showed a more consistent increase with increasing age, without a steeper increase for respondents in their 50’s.  Hearing aid use was only 2% for the entire sample and increased with age.  Only 21% of the participants with Poor DTT scores reported using hearing aids.  Those who did use hearing aids had significantly higher socioeconomic status than those without hearing aids.

Only 2.0% of the middle-aged individuals in this study reported hearing aid use. This is similar to an earlier report in which hearing aid use for 41-70 year olds was 2.8% (Davis, 1995). The persistently low proportion of hearing aid use contrasts with the fact that 9.4% of the respondents in the current study had average pure tone thresholds of at least 35 dBHL in the better ear. There are many potential explanations for the low proportion of hearing aid use among hearing impaired individuals. Cost is a commonly cited explanation, though cost is not likely to have influenced the present report, as hearing aids are provided free in the United Kingdom and the participants included in this report probably did not purchase their hearing aids privately. Insufficient value and uncomfortable fit have also been reported as explanations for low hearing aid use (McCormack & Fortnum, 2013). Other proposed barriers to hearing aid use are related to motivation, expectations and attitudes toward hearing aids, with self-recognition of hearing handicap being the most consistently related factor to hearing aid use (Vestergaard-Knudsen et al., 2010).

One mechanism for addressing the concern of hearing aid cost is through the unbundling of the hearing aid and services provided. Bundled pricing (the packaging of hearing aid and services into one price) is typical in the U.S. Unbundling may encourage initial uptake because it allows hearing aid users to pay less at the outset and divide additional expenditures into smaller, more manageable amounts, paying fees at each visit after the initial service period. There is some concern that unbundled pricing will make hearing aid users less likely to obtain needed care, but this fear may be overstated. Hearing aid users generally indicate that verification measures and counseling increase satisfaction and perceived value of hearing aids (Kochkin, 2010; 2011), so follow-up care can be perceived by the patient as a valuable part of the rehabilitative process. Unbundling offers the additional benefit for private practices because fee-for-service appointments lead to more consistent monthly cash flow than bundled fees in which a large initial payment is received with free services for a long time thereafter.

The manner in which hearing aids are represented to the general public may further impact uptake. Hearing aids are best positioned as medical devices, prescribed by skilled professionals, in clinical settings where testing is performed in controlled acoustic environments. If price is prioritized, then testing, verification and follow-up care may be abbreviated to control costs. If cosmetic appeal is prioritized, patients may select the smallest devices, perhaps without adequate venting or directional microphones, though this might not be the best option for their loss and listening needs. The potential outcome of both scenarios is disappointment with the performance and comfort of the hearing instruments, resulting in either lack of use or return for credit.  Instead, hearing aid users need to be fully educated about the options that are available and counseled as to why some models are better for their needs than others. This cannot be achieved in an environment that emphasizes price over functionality and service.

As Dawes points out, hearing impairment may be better defined by speech recognition ability in everyday situations, rather than pure tone audiometry. Even so, it is arguable whether either of these measures alone should be used to define hearing aid candidacy. Instead, clinicians gain more insight into their patients’ motivation and readiness by examining how the hearing loss affects their ability to function in their regular activities. A mildly-impaired individual with a quiet, socially inactive lifestyle is less likely to be motivated for hearing aids than a similarly impaired individual who works full time and has an active social life. A thorough patient history and needs assessment, coupled with objective testing can more accurately identify hearing aid candidates than relying on degree of hearing loss alone. The authors of this article cite a study of Swiss hearing aid use and satisfaction, stating that in Switzerland, hearing aid candidacy is “based on the degree of social and emotional handicap due to hearing loss” and that the dispensing process focuses on ongoing counseling and care after the fitting.  This study reported high rates of long-term hearing aid use and satisfaction, where 97% of Swiss hearing aid owners reported using their hearing aids and only 3% were non-users (Bertoli, 2009).

It makes sense to advise unmotivated individuals to assess their difficulties, making note of every time they ask for repetition, misunderstand a word or sentence, or smile and “fake” their way through a conversation. I instruct patients to consider whether their hearing loss causes them to avoid places or situations that they might otherwise enjoy or if the hearing loss affects their ability to perform important work-related or social activities.  With a little patience and attention, most people can determine the point at which they are ready to proceed with a hearing aid purchase. Self-recognition of need is strongly associated with eventual hearing aid uptake and use (Vestergaard-Knudsen et al., 2010), meaning that a person who returns for a consultation after taking time to evaluate their difficulties is more likely to keep their hearing aids and follow through with proper use and care.

Even as testing techniques and prevalence data improve our ability to identify those with hearing impairment and those at risk, there remain barriers to hearing aid use. Consistent representation of hearing aids as medical devices that are fitted by clinical professionals may improve the perception and attitudes of the general public. Unbundled pricing may lower the cost barrier by making the initial purchase more affordable and concomitantly emphasizing the value of follow-up care. Finally, development and adherence to a thorough fitting protocol will ensure that those who do purchase hearing aids will receive a well-prescribed medical device and become an example of success to others.

 

References

Davis, A. (1995). Hearing in adults. London, United Kingdom: Whurr Publishers Ltd. XXX.

Davis, A., Smith, P., Ferguson, M. (2008).  Acceptability: benefit and costs of early screening for hearing disability: A study of potential screening tests and models. Health Technology Assessment 11 (42), 1-294.

Dawes, P., Fortnum, H., Moore, D., Emsley, R., Norman, P., Cruickshanks, K., Davis, A., Edmondson-Jones, M., McCormack, A., Lutman, M. & Munro, K.  (2014) Hearing in middle age: a population snapshot of 40- to 69-year olds in the United Kingdom. Ear & Hearing 35 (3), 44-51.

Department of Health (2001). Health Survey for England 1999: The health of minority ethnic groups. Retrieved from http://webarchiv.nationalarchives.gov.uk/+/www.dh.gov.uk/en/Publicationsandstatistics/Publications/PublicationsStatistics/DH_4009393.

Kochkin, S. (2010). MarkeTrak VIII: Customer satisfaction with hearing aids is slowly increasing. Hearing Journal 63(1), 11-19.

Kochkin, S. (2011). MarkeTrak VIII: Reducing patient visits through verification and validation. Hearing Review 18 (6), 10-12.

McCormack, A. & Fortnum, H. (2013). Why do people fitted with hearing aids not wear them? International Journal of Audiology 52, 360-368.

Plomp, R. & Mimpen, A. (1979). Speech reception threshold for sentences as a function of age and noise level. Journal of the Acoustical Society of America 66, 1333-1342.

Smits, C., Kapteyn, T. & Houtgast, T. .(2004). Development and validation of an automatic speech-in-noise screening test by telephone. International Journal of Audiology 43, 15-28.

Smits, Kramer, S. & Houtgast, T. (2006). Speech reception thresholds in noise and self-reported hearing disability in a general adult population. Ear and Hearing 27, 538-549.

Vestergaard-Knudsen, L., Oberg, M. & Nielsen, C. (2010). Factors influencing help seeking, hearing aid uptake, hearing aid use and satisfaction with hearing aids: A review of the literature. Trends in Amplification 14, 127-154.

Wilson, D. & Strouse, A. (2002). Northwestern University Auditory Test No. 6 in multi-talker babble: A preliminary report. Journal of Rehabilitation Research and Development 39, 105-113.

The most important factors behind directional microphone benefit

Keidser, G., Dillon, H., Convery, E. & Mejia, J. (2013). Factors influencing individual variation in perceptual directional microphone benefit. Journal of the American Academy of Audiology 24, 955-968.

This editorial discusses the clinical implications of an independent research study and does not represent the opinions of the original authors.

Understanding conversation in noisy environments is one of the most common difficulties for individuals with hearing loss. Counseling and training in communication strategies can help listeners with hearing loss make use of supplemental cues to improve speech understanding in noise. However, no hearing aid feature or clinical intervention is as likely to improve the ability to function in noise as directional microphones. Directional microphones, usually twin microphone designs, offer small but helpful increases in the signal-to-noise ratio, facilitating more comfortable listening and an improved ability to understand speech and function in noisy everyday situations.

Directionality consistently demonstrates benefits to speech perception performance in laboratory studies but the amount of directional benefit achieved by subjects is highly variable, even in studies with similar methods and procedures (Freyaldenhoven et al., 2005). A number of factors have been studied and reports have indicated that variability in directional benefit was unrelated to age (Wu, 2010; O’Brien et al, 2009), degree or configuration of hearing loss (Jesperson & Olsen, 2003; Ricketts & Mueller, 2000) or vent size (Ricketts, 2000; O’Brien et al, 2009). Furthermore, laboratory studies may not always predict everyday performance (Walden et al., 2000; Cord et al., 2002; Cord et al., 2004) so it is unclear how numerous factors could converge to affect individual directional benefit in everyday hearing aid use.

Recently emerging evidence has suggested that cognitive capacity may affect a listener’s ability to make use of directional benefits. Working memory affected hearing aid users’ performance with regard to different compression time constants (Gatehouse et al., 2003; Cox & Xu, 2010) and spatial separation ability (Neher et al., 2009). Dawes et al (2010) reported that differences in hearing aid benefit were partly determined by performance on speed of processing, selective attention and switching tasks. Humes (2007) further reported that cognition may affect individual speech perception abilities in noise. Though cognition declines with age, the changes vary tremendously across individuals and cannot be predicted by age alone (Glisky, 2007), so age and cognition, though related, may affect hearing aid use and speech perception in different ways.

The primary goal of Keidser et al’s study was to investigate the factors that contribute to variability in perceptual directional microphone benefit as measured in the laboratory. Specifically, they were interested in the effects and interaction of three potential sources of variability: differences in the individual SNR achieved by physical directional benefit, differences in the ability to make use of SNR improvements and variability related to measurement error.

Fifty-nine subjects participated in the study. All had bilateral, mild-to-moderate, sensorineural hearing loss.  Age ranged from 54 to 91 years, with an average of 74 years. Of the 59 subjects, 51 had experience with amplification, whereas 8 had never worn hearing aids. For the purpose of the study, subjects were fitted with binaural, behind-the-ear hearing aids with dual-microphones and wide dynamic range compression. Advanced signal processing such as noise reduction and adaptive directionality was turned off. Hearing aids were programmed according to NAL-NL2 targets and had two programs: omnidirectional and directional.

Participants attended two experimental sessions. At the first session, subjects completed cognitive testing. First, they were administered subtests of the Test of Everyday Attention (TEA; Robertson et al., 1996) which uses real-life scenarios to measure auditory selective attention and speed of processing. Working memory was assessed using the Reading Span Test (RST; Daneman & Carpenter, 1980).  In the RST, sentences are presented on a computer screen and subjects indicate whether the sentence was meaningful or not, subjects must also recall either the first or last word of each sentence.

At the second session, hearing aids and earmolds were fitted and vent diameters were measured. The frequency range of amplification was measured, with the low frequency limit (f-amp) defined as the point at which real-ear insertion gain exceeded 3dB. The angle of the microphone ports was measured with reference to the loudspeaker axis. Speech in noise testing was completed, using the Australian Bamford-Kowal-Bench (BKB/A) sentences (Bench et al., 1979) in the presence of 8-talker babble from the NAL Speech and Noise for Hearing Aid Evaluation CD (Keidser et al, 2002). Speech was presented from a loudspeaker 1m in front of the subject. A constant level of uncorrelated multi-talker babble was presented from four loudspeakers surrounding the subject at a distance of 2m. Speech levels were adjusted to arrive at the SNR required to achieve 50% performance.

Following speech in noise testing, individual in-situ SNR levels were measured to determine how room acoustics may have affected hearing aid performance.  Individual 3D AI-DI measurements were obtained to ascertain the physical directional benefit for each subject in the test environment. The 3D AI-DI scores are directivity measurements weighted by the Articulation Index model, as measured in the center of a 3D array of 41 loudspeakers (Killion et al, 1998). In-situ SNR and 3D-AI-DI measures were computed for broadband (BB), low-frequency (LF, <2000Hz) and high-frequency (HF, >2000Hz) ranges.

Cognitive test scores were weakly correlated. The only auditory cognitive test, the ASA, was not correlated with audiological pure tone average (PTA) but was weakly correlated with age. For the physical measures, broadband (BB) and low-frequency (LF) in-situ SNRs were strongly correlated with each other. The low-frequency limit or f-amp, was highly correlated to the LF in-situ measures as well as to PTA and vent diameter. These correlations indicate that participants who had higher PTAs (more hearing loss) had smaller vent diameters, frequency responses extending further into the low-frequencies and more physical benefit from directional microphones at low frequencies.

The average perceptual directional benefit as measured by SRTn was 2.7dB, with a range from 0.3 to 5.3dB.  No participants showed negative effects of directionality.  When comparing benefit ranges in individual trials versus the mean of the three trials, effectively removing any variability attributable to random measurement errors, the range of benefit was reduced from around 9.2 dB to 5.0dB. Therefore, about half of the variation in directional microphone benefit was explained by measurement errors.  Variation in perceptual directional benefit was not correlated with age or configuration (slope) of hearing loss. Analysis of the cognitive and the in-situ measures of physical directionality showed that the only factors exerting a significant effect on perceptual benefit were LF 3D AI-DI, ASA scores and microphone angle.

With reference to the goals of their study, Keidser and her colleagues found that measurement error, physical directionality and the individual ability to make use of directional cues may contribute to variability in perceptual directional benefit. About half of the variability in measured perceptual directional benefit was attributable to measurement error associated with speech-in-noise testing. Measurement error could include head movements during testing causing brief head shadow effects, problems with speech test list equivalence (Dillon, 1982) and potential practice effects. The authors suggest that multiple measurements of perceptual directional benefit, in each test condition, should always be carried out in order to mitigate the effects of measurement error.

In agreement with previous reports, there was no direct relation between perceptual directional benefit and age, PTA or configuration of hearing loss, though there was a relation to vent diameter. Greater perceptual directional benefit was derived when greater physical directivity was achieved in the low frequencies, which was related to decreased vent diameter. This result is in agreement with previous work showing increased directional benefit with more occluded molds as compared to more open fittings (Ricketts, 2000; Fabry, 2006; Klemp & Dhar, 2008).

A more upward-pointing microphone angle was associated with improved perceptual directional benefit. This is in agreement with a report by Ricketts (2000) that showed increased physical directivity as microphone angle exceeded 20 degrees from the horizontal plane. The effect of microphone angle in the current study was small, accounting for only 4% of the variation. Because the interaction of microphone angle with other hearing aid and environmental characteristics is unknown, the authors do not recommend that clinicians deliberately fit hearing aids with microphones pointing upward.

The outcomes of this study emphasize the importance of low-frequency amplification to achieve optimal directional benefit. The lower limit of the amplification range as well as vent diameter have an effect on physical directivity that affects the perceptual benefit that can be derived from directionality. Thus, it is of particular importance for clinicians to not only select appropriate venting characteristics for each individual, but to ensure that the range of amplification is set in a manner that accounts for venting effects. Programming software requires the entry of acoustic parameters to guide frequency response characteristics, especially in the low frequency range; failure to enter the correct acoustic properties risks over or under amplifying the low-frequency range.

Of course there are many factors to consider when choosing venting, gain and output characteristics, but achieving optimal directional benefit should be considered among them.  Equalizing low-frequency gain in a directional program for use in noise may be advisable to achieve better directivity, but conversely, reduction of low-frequency gain in noise programs may be more comfortable and therefore more desirable for hearing aid users. Careful consideration of the way in which these variables interact for each individual is critical to their success with hearing aids in their daily activities.

 

References

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