Starkey Research & Clinical Blog

On the Topic of Hearing Loss and Fatigue

Hornsby, B. & Kipp, A. (2016). Subjective ratings of fatigue and vigor in adults with hearing loss are driven by perceived hearing difficulties not degree of hearing loss. Ear and Hearing 37 (1), 1-10.

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

In 2013, we reviewed an article from Dr. Ben Hornsby in which he reported on an initial foray into the fatiguing effects of listening to speech while managing a cognitively challenging secondary task (read here). The outcomes of his investigation suggested that use of hearing aids may reduce fatiguing effects of completing that secondary task. In more recent work, reviewed here, Drs Hornsby and Kipp assessed utility of standardized measures of fatigue among a large group of subjects with hearing loss.

Fatigue can be caused by a combination of physical, mental and emotional factors. Usually fatigue is temporary, resulting from periods of sustained physical or mental labor, and resolves during breaks, in between work days or on weekends. Intermittent fatigue has minimal effects on everyday life and health, but sustained fatigue, caused by unremitting work, stress or illness, has a variety of negative effects. Sustained and severe fatigue makes people less productive and more prone to accidents in the workplace (Ricci et al, 2007), reduces the ability to maintain concentration and attention, reduces processing speed, impairs decision-making abilities and may increase stress and burnout (vanderLinden et al, 2003; Bryant et al, 2004; DeLuca, 2005).

Though fatigue as a result of communication difficulty is commonly acknowledged by anecdotal reports, there has been little systematic examination of the relationship. As mentioned above, Hornsby (2013) found that hearing-impaired individuals experienced increased listening effort and mental fatigue that was mitigated somewhat by the use of hearing aids and other studies have suggested that the increased cognitive effort required for hearing-impaired individuals to understand speech may lead to subjective reports of mental fatigue (Hetu et al., 1988; Ringdahl & Grimby, 2000; Kramer et al., 2006; Copithorne, 2006). The purpose of Hornsby and Kipp’s study was to compare standardized, validated measures of fatigue to audiometric measures of hearing loss and subjective reports of hearing handicap.

The authors recruited subjects from a population of adults who sought help for their hearing loss from an Audiology clinic. There were 149 subjects, with a mean age of 66.1 years and a range from 22 to 94 years and mean pure tone average of 36.7dB HL.

Subjective fatigue was measured with two standardized scales: the Profile of Mood States (POMS; McNair et al., 1971) and the short form of the Multi-Dimensional Fatigue Symptom Inventory (MDFS-SF; Stein et al., 2004).  Two POMS subscales assessed general fatigue and vigor, which was described by words like “energetic” and “alert”.

A presentation summarizing the POMS can be found here

The MFSI-SF assessed vigor and four dimensions of fatigue – general, physical, emotional and mental. On both measures, subjects were asked to rate, on a 5-point scale, how well each item described their feelings during the past week.

The MDFS in long and short form can be found here

Audiometric data included pure tone thresholds in each ear at 500, 1000, 2000 and 4000Hz.  Perceived or subjective hearing handicap was measured with the Hearing Handicap for the Elderly (HHIE; Ventry & Weinstein, 1982) and the Hearing Handicap Inventory for Adults (HHIA; Newman et al., 1990).

Individuals 65 years or older completed the HHIE and those under 65 years completed the HHIA.

A version of the HHIA can be found here

The first set of analyses examined how the hearing-impaired subjects in the current study compared to normative data for the POMS and MFSI-SF.   Scores on vigor subscales were reverse coded and identified as “vigor deficit”, because unlike measures of fatigue or hearing handicap, high scores for vigor indicate less difficulty or less negative impact on the individual.  The authors found that the subjects in their study demonstrated significantly less vigor and slightly more fatigue than the subjects in the normative data. Furthermore, severe fatigue was reported more than twice as often and severe lack of vigor was reported more than four times as often compared to normative data. When subtypes of fatigue were examined, differences in vigor deficit were significantly greater than any of the other subscales, followed by general fatigue and mental fatigue which were both significantly greater than emotional or physical fatigue.

Hearing handicap was significantly related to both subjective fatigue and vigor ratings.  There were significant relationships among all HHIE/A scores (social, emotional, and total) and all subscales of the MFSI-SF scales.  Total score on the HHIE/A had a simple linear relationship with MFSI ratings in the physical and emotional domains. Total HHIE/A score had a nonlinear relationship with general, mental fatigue, and vigor deficit scores. In other words, low HHIE/A scores (little or no handicap) were not significantly associated with MFSI ratings, but as HHIE/A scores increased, there were stronger relationships. This nonlinear relationship indicates that as hearing handicap increased, there was a stronger likelihood of general fatigue, mental fatigue and lack of vigor.

Hornsby and Kipp drew three main conclusions from the study outcomes. First, the hearing-impaired adults in their study, who had contacted a hearing clinic for help, were more likely to report low vigor and increased fatigue than adults of comparable age in the general population.  They acknowledge that hearing loss was not specifically measured in the normative data and it is likely that there were some hearing-impaired individuals in that population. However, if hearing-impaired individuals were included in the normative data, it would likely decrease the significance of the differences noted here.  Instead, severe fatigue was more than twice as high in this study and severely low vigor was more than four times as high as in the normative population.

The second notable conclusion was that there was no relationship between degree of hearing loss and subjective ratings of fatigue or vigor. The authors hypothesized that higher degree of hearing loss would be associated with increased fatigue and vigor deficit but this was not the outcome. This observation presents a future avenue in which speech recognition ability could analyzed as a predictive factor to individuals reported fatigue.

Hearing aid use was not specifically examined in this study, yet it is likely to affect subjective ratings of fatigue and vigor. Several reports indicate that hearing aids, especially those with advanced signal processing, may reduce listening effort, fatigue and distractibility and may improve ease of listening. (Hallgren, 2005; Picou, et al., 2013; Noble & Gatehouse, 2006; Bentler, 2008). If study participants base their subjecting ratings of fatigue and vigor on how they function in everyday environments with their hearing aids, then the non-significant contribution of degree of hearing loss, as measured audiometrically, could be misleading.  Hearing aid experience and usage patterns should be evaluated in future work to ensure that hearing aid benefits do not confound the measured effects of the hearing loss itself.

The significant relationship between hearing handicap and subjective fatigue ratings underscores the importance of incorporating subjective measures into diagnostic and hearing aid fitting protocols.   Hearing care clinicians who counsel patients primarily based on audiometric results may underestimate the challenges faced by individuals who have milder hearing loss but significant perceived hearing handicap.  The HHIE/A and other hearing handicap scales, along with inquiries into work environment and work-related activities, can help us more effectively identify individual needs of our patients and formulate appropriately responsive treatment plans. Similar inquiries should be repeated as follow-up measures to evaluate how well these needs have been addressed and to indicate problem areas that remain.

References

Bentler, R.A., Wu, Y., Kettel, J. (2008). Digital noise reduction: outcomes from laboratory and field studies. International Journal of Audiology 47, 447-460

Bryant, D., Chiaravalloti, N. & DeLuca, J. (2004). Objective measurement of cognitive fatigue in multiple sclerosis. Rehabilitation Psychology 49, 114-122.

Copithorne, D. (2006). The fatigue factor: How I learned to love power naps, meditation and other tricks to cope with hearing-loss exhaustion. [Healthy Hearing Website, August 21, 2006].

DeLuca, J. (2005).  Fatigue, cognition and mental effort. In J. DeLuca (Ed.), Fatigue as a Window to the Brain (pp. 37-58). Cambridge, MA: MIT Press.

Eddy, L. & Cruz, M. (2007).  The relationship between fatigue and quality of life in children with chronic health problems: A systematic review. Journal for Specialists in Pediatric Nursing 12, 105-114.

Hallgren, M., Larsby, B. & Lyxell, B. (2005). Speech understanding in quiet and noise, with and without hearing aids. International Journal of Audiology 44, 574-583.

Hetu, R., Riverin, L. & Lalande, N. (1988). Qualitative analysis of the handicap associated with occupational hearing loss. British Journal of Audiology 22, 251-264.

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.

Hornsby, B. & Kipp, A. (2016). Subjective ratings of fatigue and vigor in adults with hearing loss are driven by perceived hearing difficulties not degree of hearing loss. Ear and Hearing 37 (1), 1-10.

Johnson, S. (2005). Depression and fatigue. In J. DeLuca (Ed.), Fatigue as a Window to the Brain (pp. 37-58). Cambridge, MA: MIT Press.

Kramer, S., Kapteyn, T. & Houtgast, T. (2006). Occupational performance: Comparing normally-hearing and hearing-impaired employees using the Amsterdam Checklist for Hearing and Work. International Journal of Audiology 45, 503-512.

McNair, D., Lorr, M. & Droppleman, L. (1971). Profile of Mood States. San Diego, CA: Educational and Industrial Testing Service. Retrieved from http://www.mhs.com/product.aspx?gr=cl&id=overview&prod=poms.

Noble, W. & Gatehouse, S. (2006). Effects of bilateral versus unilateral hearing aid fitting on abilities measured by the SSQ. International Journal of Audiology 45, 172-181.

Picou, E.M., Ricketts, T.A. & Hornsby, B.W. (2013). The effect of individual variability on listening effort in unaided and aided conditions. Ear and Hearing (in press).

Pronk, M., Deeg, D. & Kramer, S. (2013). Hearing status in older persons: A significant determinant of depression and loneliness? Results from the Longitudinal Aging Study Amsterdam. American Journal of Audiology 22, 316-320.

Ricci, J., Chee, E. & Lorandeau, A. (2007). Fatigue in the U.S. workforce: Prevalence and implications for lost productive work time. Journal of Occupational Environmental Medicine  49, 1-10.

Ringdahl, A. & Grimby, A. (2000). Severe-profound hearing impairment and health related quality of life among post-lingual deafened Swedish adults. Scandinavian Audiology 29, 266-275.

Stein, K., Jacobsen, P. & Blanchard, C. (2004). Further validation of the multidimensional fatigue symptom inventory – short form. Journal of Pain and Symptom Management 27, 14-23.

vanderLinden, D., Frese, M. & Meijman, T. (2003). Mental fatigue and the control of cognitive processes: effects on perseveration and planning. Acta Psychologica (Amst) 113, 45-65.

Ventry, I. & Weinstein, B. (1982). The Hearing Handicap Inventory for the Elderly: a new tool. Ear and Hearing 3, 128-134.

Weinstein, B., Sirow, L. & Moser, S. (2016).  Relating hearing aid use to social and emotional loneliness in older adults. American Journal of Audiology 25, 54-61.

Considerations for music processing through hearing aids

Arehart, K., Kates, J. & Anderson, M. (2011) Effects of Noise, Nonlinear Processing and Linear Filtering on Perceived Music Quality, International Journal of Audiology, 50(3), 177-190.

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

The primary goal of most hearing aid selections and fittings is to improve communication by optimizing the perceived quality and recognition of speech sounds.  While speech is arguably the most important sound that normal hearing or hearing-impaired listeners encounter on a daily basis, the perception of other sounds should be taken into consideration, including music.  Music perception and sound quality is particularly important for hearing aid users who are musicians or music enthusiasts, or those who use music for therapeutic purposes related to stress reduction.

Though some hearing aid users report satisfaction with the performance of their hearing aids for music listening (Kochkin, 2000) the signal processing characteristics that are most appropriate for speech are not ideal for music perception.  Speech is produced by variants of one type of “instrument”, whereas music is produced by a range of instruments that create sounds with diverse timing, frequency and intensity characteristics. The perception of speech and music both rely on a broad frequency range, though high frequency sounds carry particular importance for speech perception and lower frequency sounds may be more important for music perception and enjoyment (Colucci, 2013).  Furthermore, the dynamic range, temporal and spectral characteristics may vary tremendously from one genre of music or even one piece of music to another.  Hearing aid circuitry that is designed, selected and programmed specifically to optimize speech recognition may compromise the perception and enjoyment of music and the effects may vary across musical genres.

A number of studies have examined the effects of non-linear hearing aid processing on music quality judgments.  Studies comparing compression limiting and peak clipping typically found that listeners preferred compression over peak clipping (Hawkins & Naidoo 1993; Tan et al., 2004). Whereas some studies found that listeners preferred less compression (Tan et al., 2004; Tan & Moore, 2008; Van Buuren et al., 1999) or longer compression release times (Hansen, 2002), others determined that listeners preferred wide-dynamic-range compression (WDRC) over compression limiting and peak clipping (Davies-Venn et al., 2007).

Arehart and her colleagues examined the effect of a variety of signal processing conditions on music quality ratings for normal-hearing and hearing-impaired individuals. They used simulated hearing aid processing to examine the effects of noise and nonlinear processing, linear filtering and combinations of noise, nonlinear processing and linear filtering. Their study had three primary goals:

1. To determine the effects of these processing conditions in isolation and in combination.

2. To examine the effects of nonlinear processing, noise and linear filtering on three different music genres.

3. To examine how these signal processing conditions affect the music quality ratings of normal-hearing and hearing-impaired individuals.

Subjects included a group of 19 normal-hearing adults with a mean age of 40 years (range 18-64 years) and a group of hearing-impaired adults with a mean age of 63 years (range 50 to 82 years).  The normal-hearing subjects had audiometric thresholds of 20dBHL or better from 250 through 8000Hz and the hearing-impaired subjects had sloping, mild to moderately-severe hearing losses.

Participants listened to music samples from three genres: a jazz trio consisting of piano, acoustic bass and drums; a full orchestra including string, wind and brass instruments performing an excerpt from Haydn’s Symphony No. 82; and a “vocalese” sample consisting of a female jazz vocalist singing nonsense syllables without accompaniment from other instruments. All music samples were 7 seconds in duration.  Long-term spectra of the music samples showed that they all had less high-frequency energy than the long-term spectrum of speech, with the vocalese and jazz samples having a steeper downward slope to their spectra than the Haydn sample which was mildly sloping through almost 5000Hz.

Music samples were presented in 100 signal processing conditions: 32 separate conditions of noise or nonlinear processing (e.g., speech babble, speech-shaped noise, compression, peak clipping), 32 conditions of linear filtering (e.g., high, low and bandpass filters, various positive and negative spectral tilts) and 36 combination conditions. Additionally, listeners were presented with a reference condition of “clean”, unprocessed music in each genre. Listeners were asked to judge the quality of the music samples on a scale from 1 (bad) to 5 (excellent). They listened to and made judgments on the full stimulus set twice.

The music samples were presented under headphones. Normal-hearing listeners heard stimuli at a level of 72dB SPL, whereas the hearing-impaired listeners heard stimuli as amplified according to the NAL-R linear prescription, to ensure audibility (Byrne & Dillon, 1986). The NAL-R linear prescription was intentionally selected to avoid confounding effects of wide dynamic range compression which could further distort the stimuli and mask the effects of the variables under study.

Both subject groups rated the clean, unprocessed music samples highly. Overall, hearing loss did not significantly affect the music quality ratings and general outcomes were similar between the two subject groups. Average music quality ratings were much higher for the linear processing conditions than for the nonlinear processing conditions. Most noise and nonlinear processing conditions were rated as significantly poorer than the clean samples, whereas many linear conditions were rated as having more similar quality to the clean samples. Compression, 7-bit quantization and spectral subtraction plus speech babble were the only nonlinear conditions that did not differ significantly from clean music samples.

The genre of music was a significant factor in the quality ratings, but the effects were complex, and some processing types affected one music genre more than others. For instance, hearing-impaired listeners judged vocalese samples processed with compression as similar to clean samples, whereas vocalese processed with a negative spectral tilt was judged as having much poorer quality. In contrast, hearing-impaired listeners rated higher mean differences between clean music and compressed samples for Haydn and jazz than they did for vocalese samples, indicating that compression had more of a negative effect on the classical and jazz samples than the vocalese sample.

The outcomes of this study indicate that normal-hearing and hearing-impaired listeners judged the effects of noise, nonlinear and linear processing on the quality of music samples in a similar way and that noise and nonlinear processing had significantly more negative impact on music quality than linear processing did.  The effects of the different types of processing on the three music genres was complex and it was clear that different types of music are affected in different ways. Interestingly, these diverse effects were noted even though the music samples in this study were all acoustic samples, with no electronic or amplified instruments included in the samples. The fact that quality judgments of three acoustic genres were affected in different ways by nonlinear signal manipulation implies that the quality of pop, rock and other genres that use amplified and electronic instruments may also be affected in different and unique ways.

Hearing aid manufacturers have begun to offer automatic and manual program options with settings that have been optimized for music listening, though many clinicians may still be faced with the task of customizing programs for their clients who are musicians or music enthusiasts.  To complicate matters, the outcomes of this study demonstrate that the optimal signal processing parameters for one genre might not be best for another. In addition, individual preferences could be affected by hearing thresholds and audiometric slopes, though in this study, the hearing-impaired and normal-hearing listeners demonstrated similar preferences and quality judgments, independent of hearing status.

Clearly, more study is needed in this area, but hearing care professionals can safely draw a few general conclusions about appropriate settings for music listening programs. Music spectra contain more low-frequency energy on average than speech spectra, so a flatter or slightly negatively-sloping frequency response with more low and mid-frequency emphasis is probably desirable. As such, music programs may require different compression ratios, compression thresholds and release times than would be prescribed for speech listening. While other special signal processing features like noise reduction, frequency lowering, and fast-acting compression for impulse sounds may need to be reduced or turned off in a music program. These factors combine to suggest a much different prescriptive rationale for music listening than would be require for daily use.

 

References

Arehart, K., Kates, J. & Anderson, M. (2011). Effects of Noise, Nonlinear Processing and Linear Filtering on Perceived Music Quality, International Journal of Audiology, 50, 177-190.

Byrne, D. & Dillon, H. (1986). The National Acoustic Laboratories (NAL) new procedure for selecting the gain and frequency response of a hearing aid. Ear and Hearing 7, 257-265.

Colucci, D. (2013). Aided music mapping for musicians: back to basics. The Hearing Journal 66(10), 40.

Davies-Venn, E., Souza, P. & Fabry, D. (2007). Speech and music quality ratings for linear and nonlinear hearing aid circuitry. Journal of the American Academy of Audiology 18, 688-699.

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

Hawkins, D. & Naidoo, S. (1993). Comparison of sound quality and clarity with asymmetrical peak clipping and output limiting compression. Journal of the American Academy of Audiology 4, 221-8.

Kochkin, S. (2000). MarkeTrak VIII: Customer satisfaction with hearing aids is slowly increasing. Hearing Journal 63, 11-19.

Ricketts, T., Dittberner, A. & Johnson, E. (2008). High-frequency amplification and sound quality in listeners with normal through moderate hearing loss. Journal of Speech, Language and Hearing Research 51, 1328-1340.

Tan, C. & Moore, B. (2008). Perception of nonlinear distortion by hearing impaired people. International Journal of Audiology 47, 246-256.

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.

Listening gets more effortful in your forties

DeGeest, S., Keppler, H. & Corthals, P. (2015) The effect of age on listening effort. Journal of Speech, Language and Hearing Research 58(5), 1592-1600.

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

The ability to understand conversational speech in everyday situations is affected by many obstacles. A large proportion of our work involves determining the best treatment plan to help hearing-impaired patients overcome these obstacles.  Though understanding speech in noise poses difficulty for hearing-impaired individuals of all ages, several studies have indicated that in the absence of hearing loss, older adults face increased challenges in noisy environments (Pichora-Fuller & Singh, 2006; Duquesnoy, 1983; Dubno et al., 1984; Helfer & Freyman, 2008); some reports suggest that middle-aged adults have significantly poorer speech recognition in noise compared to young adults. (Helfer & Vargo, 2009).

Competing environmental noise reduces the audibility of acoustic speech information, increasing reliance upon visual, situational and contextual cues, that in turn requires a greater delegation of cognitive resources (Schneider et al., 2002), making listening more effortful. Increases in listening effort in noise could be related to decreases in hearing thresholds or available cognitive resources, as both are known to decrease with advancing age.  But the fact that normal-hearing individuals also experience more difficulty hearing in noise suggests that factors other than hearing loss may be involved, including working memory, processing speed and selective attention (Akeroyd, 2008; Pichora-Fuller et al., 1995).

The work of DeGeest and colleagues examined listening effort and speech recognition in adult subjects from 20 to 77 years of age. All of the subjects were determined to have normal “age corrected” hearing thresholds from 250Hz through 8000Hz, though older subjects had average high-frequency pure tone thresholds in the mild to moderate range of hearing loss. Subjects over age 60 were screened with the Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005), no specific cognitive performance measures were included in data analysis.  Listening effort was evaluated using a dual-task paradigm in which subjects performed a speech recognition task while simultaneously performing a visual memory task. Speech recognition ability was measured with 10-item sets of two-syllable digits, presented at two SNR levels: +2dB SNR and -10dB SNR.  Performance on the dual-task presentation was examined in comparison to baseline measures of each test in isolation. Listening effort was defined as the change in performance on the visual memory task when the dual-task condition was compared to baseline. Speech recognition ability was not expected to change from baseline when measured in the dual-task condition.

The investigators found that listening effort increased in parallel with advancing age. Though subjects were initially determined to have “age corrected” normal hearing, which meant some participants had high frequency hearing loss, the correlation between listening effort and age was maintained even when the factors of pure tone threshold and baseline word recognition performance were controlled. Of note was the observation that listening effort started to increase notably between +2dB and -10dB SNRs at ages of 40.5 years and 44.1 years, respectively. Their determination that listening effort begins to increase in the mid 40’s is in agreement with other research that reported cognitive declines beginning around age 45 years (Singh-Manoux et al., 2012).  The authors suggest that further investigations of listening effort and word recognition in middle-aged and older adults should examine cognitive ability in more detail with specific tests of working memory, processing speed and selection attention included in the data analyses.

Although middle-aged adults are less likely to demonstrate outward effects of cognitive decline than older adults, the should not be regarded as immune to changes in cognitive ability and resulting listening effort.  Middle-aged individuals are more likely than their older counterparts to be working full time and may have more active lifestyles.  Hearing-impaired individuals of middle-age who work in reverberant or noisy environments may face additional challenges to job performance if they are also experiencing changes in processing speed or memory or if they struggle with even mild attentional deficits.  These are tangible considerations that might impact the entirety of treatment plan development, from the selection of hearing aids and assistive technologies to the communication and counseling strategies that are selected for the patient and their family members.

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.

DeGeest, S., Keppler, H. & Corthals, P. (2015) The effect of age on listening effort. Journal of Speech, Language and Hearing Research 58(5), 1592-1600.

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.

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

Keppler, H., Dhooge, I., Corthals, P., Maes, L., D’haenens, W., Bockstael, A. & Vinck, B. (2010). The effects of aging on evoked otoacoustic emissions and efferent suppression of transient evoked otoacoustic emissions. Clinical Neurophysiology 121, 359-365.

Nasreddine, Z., Phillips, M., Bedirian, V., Charbonneau, S., Whitehead, V., Collin, I. & Chertkow, H. (2005).  The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society 53, 695-699.

Pichora-Fuller, M., Schneider, B. & Daneman, M. (1995).  How young and old adults listen to and remember speech in noise. The Journal of the Acoustical Society of America 97, 593-608.

Pichora-Fuller, M. & Singh, G. (2006). Effects of age on auditory and cognitive processing: implications for hearing aid fitting and audiologic rehabilitation. Trends in Amplification 10, 29-59.

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.

Schneider, B., Daneman, M. & Pichora-Fuller, M. (2002). Listening in aging adults: from discourse comprehension to psychoacoustics. Canadian Journal of Experimental Psychology 56, 139-152.

Does hearing aid use slow cognitive decline?

Deal, J., Sharrett, A., Albert, M., Coresh, J., Mosley, T., Knopman, D., Wruck, L. & Lin, F. (2015). Hearing impairment and cognitive decline: A pilot study conducted within the Atherosclerosis Risk in Communities Neurocognitive Study. American Journal of Epidemiology 181 (9), 680-690.

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

Recent evidence has suggested that cognitive decline and hearing impairment may have more of a connection beyond simple co-occurrence in the older population. Certainly, as individuals age, they become more likely to exhibit reduced cognitive function and also more likely to have hearing loss. It has been proposed that hearing loss may be correlated with temporal lobe and whole brain atrophy (Lin & Albert, 2014; Peelle, et al., 2011; Lin et al., 2014).  Whether the two conditions are related to a shared underlying cause is not known, but a number of studies have indicated that hearing loss may put older individuals at higher risk of cognitive decline (Lin, 2011; Lin et al., 2011; Lin, et al., 2013). The effect of hearing loss on cognition may be mediated by social isolation and loneliness or increased listening effort required to process speech via an impaired peripheral auditory system (McCoy, et al., 2005; Tun, et al., 2009). Conversely, cognition affects every-day communication and recent research has shown that hearing aid users with reduced cognitive capacity may have poorer speech recognition ability in noise, be more susceptible to the effects of distortion and noise and may also take a longer time to adapt to new hearing aids (Lunner, 2003; Lunner et al., 2009; Ng et al., 2014)

The work of Deal and colleagues aimed to determine whether older individuals with hearing loss show poorer cognitive performance and experience a more rapid rate of cognitive decline than those with normal hearing. Subjects were recruited from a population originally recruited in 1987-1989 for a longitudinal study called Atherosclerosis Risk in Communities (ARIC). Of the 15,792 ARIC subjects, 253 participated in this study on cognition and hearing, with a mean age of 76.9 years. Approximately 39% of the subjects were men, 61% were women.  At the 2013 session, 48% of the total participants reported ever smoking, 34% had diabetes and 71.9% had hypertension.  About 60% of the subjects had fewer than 12 years of education and 40% had more than 12 years of education.

The ARIC subjects completed a battery of neuropsychological tests on in three domains – memory, language and processing speed/attention – in 1990-1992 and again in 1996-1998.  Memory was tested with the Delayed Word Recall Test (DWRT; Knopman et al., 1989), the Incidental Learning Test (Kaplan et al., 1991) and the Logical Memory Tests I and II (Wechsler, 1945). Language was examined using the Word Fluency Test (Benton et al., 1994), Animals Naming Test (Goodglass & Kaplan, 1983) and the Boston Naming Test (Saxton et al., 2000). Processing speed and attention were assessed with the Digit Symbol Substitution and Digit Span Backwards Tests (Wechsler, 1981) and Trail Making Tests I and II (Spreen & Strauss, 1991; Reitan, 1958). For the purpose of the present study, these neuropsychological tests were administered again in 2013.

Pure tone air conduction thresholds were obtained for all 2013participants and they were categorized according to degree of loss indicated by the pure tone average (PTA) in the better ear: normal (lower than 25dB), mild (26-40dB), moderate/severe (greater than 40dB).  Only 5 individuals had PTAs greater than 70dB, so these individuals were included in the moderate/severe group. Of the total population, 34% had moderate/severe hearing loss, 37% had mild hearing loss and 29% had normal hearing. Hearing aid users made up approximately 20% of the total subject population. Hearing aid use was loosely defined as the self-reported use of a hearing aid in either or both ears during the month prior to the experimental session.  The duration of hearing aid use ranged from less than 1 year to 48 years, with most aided participants reporting hearing aid use for a period of 3 to 7 years.

All of the groups showed a decline in cognitive performance over the 20 years of the study, but the hearing loss groups declined faster than the normal hearing group. The subjects with moderate/severe hearing loss were slightly older and slightly more likely to be male and to have hypertension. However, after correcting for these variables, the subjects with moderate/severe hearing loss still declined significantly faster than the normal hearing group.

Approximately 51% of the subjects with moderate/severe hearing loss wore hearing aids.  The individuals who did not wear hearing aids had significantly poorer performance on the cognitive tests and demonstrated a significantly faster rate of decline compared to those in the moderate/severe group who did wear hearing aids. The rate of 20-year memory decline for the unaided individuals in this group was twice the average rate of decline reported in national studies of cognitive change in older adults (Salthouse, 2010; Hayden et al., 2011).  In comparison, the hearing aid users in this study with moderate/severe hearing loss showed a rate of cognitive decline that was only slightly higher than the rate for subjects with normal hearing.

The authors point out that because hearing was not assessed at earlier experimental sessions, they cannot rule out the possibility that cognitive decline had a causative effect on the measured hearing loss. However, this is unlikely because they corrected for co-occurring diseases and conditions in their analysis. Furthermore, conditions affecting cognition are not known to have any effect on the peripheral auditory system and cognitive deficits were not expected to have influenced the validity of the audiometric test results.

Many have proposed that hearing loss may increase risk of cognitive decline, via increased social isolation, increased perceptual effort and changes in brain volume. Unaided hearing loss is known to increase the risk of social isolation, which in turn has been associated with increases in blood pressure and corticosteroid levels, which could in turn affect brain structure (Mick et al., 2014; Hawkley & Cacioppo, 2010). Similarly, several studies have indicated that hearing loss increased effortful listening, thereby increasing the cognitive demands required to process speech (Rabbitt, 1968; Tun et al., 2009; McCoy et al., 2005).

The outcomes of this study are in agreement with other reports in which hearing impaired individuals demonstrated poorer performance on cognitive tests and faster rates of cognitive decline (Lin, 2011; Lin et al., 2011; Lin, et al., 2013). Other reports also indicate a relationship between hearing loss and subsequent dementia over years of follow-up evaluations (Gallacher et al., 2012; Lin et al., 2011).  The current outcome that hearing aid use had a mitigating effect on cognitive performance and rate of decline is fascinating and supports the need for further investigation on the relationship between cognition and hearing loss.

Though this is an emerging area of study, the results reported here offer strong support for the proposal that the risk of cognitive decline by hearing loss may be reduced, at least partially, by the correction of peripheral hearing loss with hearing aids.  This underscores the importance of amplification for older individuals and clinicians should be prepared to counsel their patients that hearing aids are an effective way to improve communication, decrease social isolation and may slow or decrease the risk of cognitive decline. However, clinicians should be cautious not to suggest that hearing aids will prevent cognitive decline. Although the authors are careful not to claim a causal relationship between hearing loss and cognitive decline, it is clear that the two conditions are related and because hearing loss is easily treatable it may be one of the few ways in which individuals can proactively manage their risk of cognitive decline.

References

Benton, A., Hamsher, K., & Sivan, A. (1994). Multilingual Aphasia Examination 3rd ed. Iowa City, IA: AJA Associates.

Deal, J., Sharrett, A., Albert, M., Coresh, J., Mosley, T., Knopman, D., Wruck, L. & Lin, F. (2015). Hearing impairment and cognitive decline: A pilot study conducted within the Atherosclerosis Risk in Communities Neurocognitive Study. American Journal of Epidemiology 181 (9), 680-690.

Gallacher, J., Ilubaera, V. & Ben-Shlomo, Y. (2012). Auditory threshold, phonologic demand and incident dementia. Neurology 79(15), 1583-1590.

Goodglass, H. & Kaplan, E. (1983). The Assessment of Aphasia and Related Disorders 2nd ed. Philadelphia, PA: Lea and Febiger: 102, 31.

Hawkley, L. & Cacioppo, J. (2010).  Loneliness matters: a theoretical and empirical review of consequences and mechanisms. Annals of Behavioral Medicine 40(2), 218-227.

Hayden, K., Reed, B. & Manly, M. (2011). Cognitive decline in the elderly: an analysis of population heterogeneity. Age and Aging 40(6), 684-689.

Kaplan, E., Fein, D. & Morris, R. (1991). WAIS as a Neuropsychological Instrument. San Antonio, TX: The Psychological Corporation.

Knopman, D. & Ryberg, S. (1989). A verbal memory test with high predictive accuracy for dementia of the Alzheimer type. Archives of Neurology 46(2), 141-145.

Lin, F.  (2011). Hearing loss and cognition among older adults in the United States. The Journals of Gerontology A: Biological Sciences and Medical Sciences 66 (10), 1131-1136.

Lin, F. & Albert, M. (2014). Hearing loss and dementia – who is listening? Aging and Mental Health 18(6), 671-673.

Lin, F., Ferrucci, L. & Metter, E. (2011). Hearing loss and cognition in the Baltimore Longitudinal Study of Aging. Neuropsychology 25(6), 763-770.

Lin, F., Yaffe, K., & Xia, J. (2013). Hearing loss and cognitive decline in older adults. Journal of the American Medical Association Internal Medicine 173 (4), 293-299.

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.

Mick, P., Kawachi, I. & Lin, F. (2014). The association between hearing loss and social isolation in older adults. Otolaryngology Head Neck Surgery 150(3), 378-384.

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.

Peelle, J., Troiani, V. & Grossman, M. (2011). Hearing loss in older adults affects neural systems supporting speech comprehension. Journal of Neuroscience 31(35), 12638-12643.

Rabbitt, P. (1968). Channel-capacity, intelligibility and immediate memory. Quarterly Journal of Experimental Psychology 20(3), 241-248.

Reitan, R. (1958). Validity of the Trail Making Test as an indicator of organic brain damage. Perceptual and Motor Skills 8, 271-276.

Salthouse, T. (2010). Major Issues in Cognitive Aging. Vol. 49, New York, NY: Oxford University Press: 246.

Saxton, J., Rafcliff, G. & Munro, C. (2000).  Normative data on the Boston Naming Test and two equivalent 30-item short forms. Clinical Neuropsychology 14(4), 526-534.

Spreen, O. & Strauss, E. (1991). A Compendium of Neuropsychological Tests: Administration, Norms and Commentary. 2nd ed. New York, NY: Oxford University Press.

Tun, P., McCoy, S. & Wingfield, A. (2009). Aging, hearing acuity and the attentional costs of effortful listening. Psychology and Aging 24(3), 761-766.

Wechsler, D. (1945). A standardized memory scale for clinical use. Journal of Psychology 19(1), 87-95.

Wechsler, D. (1981). Wechsler Adult Intelligence Scale – Revised. New York, NY: The Psychological Corporation.

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.

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 Cochlear Dead Regions

Pepler, A., Munro, K., Lewis, K. & Kluk, K. (2014). Prevalence of Cochlear Dead Regions in New Referrals and Existing Adult Hearing Aid Users. Ear and Hearing 20(10), 1-11.

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

Cochlear dead regions are areas in which, due to inner hair cell and/or nerve damage, responses to acoustic stimuli occur not at the area of peak basilar membrane stimulation but instead occur at adjacent regions in the cochlea. Professor Brian Moore defined dead regions as a total loss of inner hair cell function across a limited region of the basilar membrane (Moore, et al., 1999b). This hair cell loss does not result in an inability to perceive sound at a given frequency range, rather the sound is perceived via off-place or off-frequency listening, a spread of excitation to adjacent regions in the cochlea where inner hair cells are still functioning (Moore, 2004).  Because the response is spread across a broad tonotopic area, individuals with cochlear dead regions may perceive pure tones as “clicks”, “buzzes” or “whooshes”.

Cochlear dead regions are identified and measured by a variety of masking techniques. The most accurate method is the calculation of psychophysical tuning curves (PTCs), originally developed to measure frequency selectivity (Moore & Alcantara 2001). A PTC plots the level required to mask a stimulus frequency as a function of the masker frequency. For a normally hearing ear, the PTC peak will align with the point at which the stimulus can be masked by the lowest level masker.  In ears with dead regions, the tip of the PTC is shifted off of the signal frequency to indicate that the signal is being detected in an adjacent region. Though PTCs are an effective method of identifying and delineating the edges of cochlear dead regions, they are time consuming and ill-suited to clinical use.

The test used most frequently for clinical identification of cochlear dead regions is the Threshold Equalizing Test (TEN; Moore et al., 2000; 2004). The TEN test was developed with the idea that tones detected by off-frequency listening, in ears with dead regions, should be easier to mask with broadband noise than they would in ears without dead regions. With the TEN (HL) test, masked thresholds are measured across the range of 500Hz to 4000Hz, allowing the approximate identification of a cochlear dead region.

There are currently no standards for clinical management of cochlear dead regions. Some reports suggest that affect speech, pitch, loudness perception, and general sound quality (Vickers et al., 2001; Baer et al., 2002; Mackersie et al., 2004; Huss et al., 2005a; 2005b). Some researchers have specified amplification characteristics to be used with patients with diagnosed dead regions, but there is no consensus and different studies have arrived at conflicting recommendations. While some recommend limiting amplification to a range up to 1.7 times the edge frequency of the dead region (Vickers et al., 2001; Baer et al., 2002), others advise the use of prescribed settings and recommend against limiting high frequency amplification (Cox et al., 2012; see link for a review).  Because of these conflicting recommendations, it remains unclear how clinicians should modify their treatment plans, if at all, for hearing aid patients with dead regions.

Previous research on the prevalence of dead regions has reported widely varying results, possibly due to differences in test methodology or subject characteristics. In a study of hearing aid candidates, Cox et al. (2011) reported a dead region prevalence of 31%, but their strict inclusion criteria likely missed individuals with milder hearing losses, so their prevalence estimate may be different from that of hearing aid candidates at large. Vinay and Moore (2007) reported higher prevalence of 57% in a study that did include individuals with thresholds down to 15dB HL at some frequencies, but the median hearing loss of their subjects was higher than that of the Cox et al. study, which likely impacted the higher prevalence estimate in their subject group.

In the study being reviewed, Pepler and her colleagues aimed to determine how prevalent cochlear dead regions are among a population of individuals who have or are being assessed for hearing aids. Because dead regions become more likely as hearing loss increases, and established hearing aid patients are more likely to have greater degrees of hearing loss, they also investigated whether established hearing aid patients would be more likely to have dead regions than newly referred individuals.  Finally, they studied whether age, gender, hearing thresholds or slope of hearing loss could predict the presence of cochlear dead regions.

The researchers gathered data from a group of 376 patients selected from the database of a hospital audiology clinic in Manchester, UK. Of the original group, 343 individuals met inclusion criteria; 193 were new referrals and 150 were established patients and experienced hearing aid users.  Of the new referrals, 161 individuals were offered and accepted hearing aids, 16 were offered and declined hearing aids and 16 were not offered hearing aids because their losses were of mild degree.  The 161 individuals who were fitted with new hearing aids were referred to as “new” hearing aid users for the purposes of the study. All subjects had normal middle ear function and otoscopic examinations and on average had moderate sensorineural hearing losses.

When reported as a proportion of the total subjects in the study, Pepler and her colleagues found dead region prevalence of 36%.  When reported as the proportion of ears with dead regions, the prevalence was 26% indicating that some subjects had dead regions in one ear only. Follow-up analysis on 64 patients with unilateral dead regions revealed that the ears with dead regions had significantly greater audiometric thresholds than the ears without dead regions. Only 3% of study participants had dead regions extending across at three or more consecutive test frequencies. Ears with contiguous dead regions had greater hearing loss than those without.  Among new hearing aid users, 33% had dead regions while the prevalence was 43% among experienced hearing aid users. On average, the experienced hearing aid users had poorer audiometric thresholds on average than new users.

Pepler and colleagues excluded hearing losses above 85dB HL because effective TEN masking could not be achieved. Therefore, dead regions were most common in hearing losses from 50 to 85dB HL, though a few were measured below that range. There were no measurable dead regions for hearing thresholds below 40dB HL. Ears with greater audiometric slopes were more likely to have dead regions, but further analysis revealed that only 4 kHz thresholds had a significant predictive contribution and the slopes of high-frequency hearing loss only predicted dead regions because of the increased degree of hearing loss at 4 kHz.

Demographically, more men than women had dead regions in at least one ear, but their audiometric configurations were different: women had poorer low frequency thresholds whereas men had poorer high frequency thresholds. It appears that the gender effect actually due to the difference in audiometric configuration, specifically the men’s poorer high frequency thresholds. A similar result was reported for the analysis of age effects. Older subjects had a higher prevalence of dead regions but also had significantly poorer hearing thresholds.  Though poorer hearing thresholds at 4kHz did slightly increase the likelihood of dead regions, regression analysis of the variables of age, gender and hearing thresholds found that none of these factors were significant predictors.

Pepler et al’s prevalence data agree with the 31% reported by Cox et al (2012), but are lower than that reported by Vinay and Moore (2007), possibly because the subjects in the latter study had greater average hearing loss than those in the other studies.  But when Pepler and her colleagues used similar inclusion criteria to the Cox study, they found a prevalence of 59%, much higher than the report by Cox and her colleagues and likely due to the exclusion of subjects with normal low frequency hearing in the Cox study. The authors proposed that Cox’s exclusion of subjects with normal low frequency thresholds could have reduced the overall prevalence by increasing the proportion of subjects with metabolic presbyacusis and eliminating some subjects with sensory presbyacusis—sensory presbyacusis is often associated with steeply sloping hearing loss and involves atrophy of cochlear structures (Shuknecht, 1964).

 In summary:

The study reported here shows that roughly a third of established and newly referred hearing aid patients are likely to have at least one cochlear dead region, in at least one ear. A very low proportion (3% reported here) of individuals are likely to have dead regions spanning multiple octaves. The only factor that predicted the presence of dead regions was hearing threshold at 4 kHz.

On the lack of clinical guidance:

As more information is gained about prevalence and risk factors, what remains missing are clinical guidelines for management of hearing aid users with diagnosed high-frequency dead regions. Conflicting recommendations have been proposed for either limiting high frequency amplification or preserving high frequency amplification and working within prescribed targets. The data available today suggest that prevalence of contiguous multi-octave dead regions is very low and a further subset of hearing aid users with contiguous dead regions experience any negative effects of high-frequency amplification. With consideration to these observations, it seems prudent that the prescription of high-frequency gain should adhere to the prescribed targets for all patients at the initial fitting. Any reduction to high-frequency gains should be managed as a result of subjective feedback from the patient after they have completed a trial period with their hearing aids.

On frequency lowering and dead regions:

Some clarity is required regarding the role of frequency lowering and the treatment of cochlear dead regions. Because acoustic information in speech extends out to 10 kHz and because most hearing aid frequency responses roll off significantly after 4-5 kHz, the mild prescription of frequency lowering can be beneficial to many hearing aid users. It must be noted that the benefits of this technology arise largely from the acoustic limitations of the device and not the presence or absence of a cochlear dead region. There are presently no recommendations for the selection of frequency lowering parameters in cases of cochlear dead regions. In the absence of these recommendations, the best practice for the prescription of frequency lowering would follow the same guidelines as any other patient with hearing loss; validation and verification should be performed to document benefit with the algorithm and identify appropriate selection of algorithm parameters.

On the low-frequency dead region: 

The effects of low-frequency dead regions are not well studied and may have more significant impact on hearing aid performance.  Hornsby (2011) reported potential negative effects of low frequency amplification if it extends into the range of low-frequency dead regions (Vinay et al., 2007; 2008). In some cases performance decrements reached 30%, so the authors recommended using low-frequency gain limits of 0.57 times the low-frequency edge of the dead region in order to preserve speech recognition ability. Though dead regions are less common in the low frequencies than in the high frequencies, more study on this topic is needed to determine clinical testing and treatment implications.

References

Baer, T., Moore, B. C. and Kluk, K. (2002). Effects of low pass filtering on the intelligibility of speech in noise for people with and without dead regions at high frequencies. Journal of the Acoustical Society of America 112(3 Pt 1), 1133-44.

Cox, R., Alexander, G., Johnson, J., Rivera, I. (2011). Cochlear dead regions in typical hearing aid candidates: Prevalence and implications for use of high-frequency speech cues. Ear and Hearing 32(3), 339 – 348.

Cox, R.M., Johnson, J.A. & Alexander, G.C. (2012).  Implications of high-frequency cochlear dead regions for fitting hearing aids to adults with mild to moderately severe hearing loss. Ear and Hearing 33(5), 573-87.

Hornsby, B. (2011) Dead regions and hearing aid fitting. Ask the Experts, Audiology Online October 3, 2011.

Huss, M. & Moore, B. (2005a). Dead regions and pitch perception. Journal of the Acoustical Society of America 117, 3841-3852.

Huss, M. & Moore, B. (2005b). Dead regions and noisiness of pure tones. International Journal of Audiology 44, 599-611.

Mackersie, C. L., Crocker, T. L. and Davis, R. A. (2004). Limiting high-frequency hearing aid gain in listeners with and without suspected cochlear dead regions. Journal of the American Academy of Audiology 15(7), 498-507.

Moore, B., Huss, M. & Vickers, D. (2000). A test for the diagnosis of dead regions in the cochlea. British Journal of Audiology 34, 205-224.

Moore, B. (2004). Dead regions in the cochlea: Conceptual foundations, diagnosis and clinical applications. Ear and Hearing 25, 98-116.

Moore, B. & Alcantara, J. (2001). The use of psychophysical tuning curves to explore dead regions in the cochlea. Ear and Hearing 22, 268-278.

Moore, B.C., Glasberg, B. & Vickers, D.A. (1999b). Further evaluation of a model of loudness perception applied to cochlear hearing loss. Journal of the Acoustical Society of America 106, 898-907.

Pepler, A., Munro, K., Lewis, K. & Kluk, K. (2014). Prevalence of Cochlear Dead Regions in New Referrals and Existing Adult Hearing Aid Users. Ear and Hearing 20(10), 1-11.

Schuknecht HF. Further observations on the pathology of presbycusis. Archives of Otolaryngology 1964;80:369—382

Vickers, D., Moore, B. & Baer, , T. (2001). Effects of low-pass filtering on the intelligibility of speech in quiet for people with and without dead regions at high frequencies. Journal of the Acoustical Society of America 110, 1164-1175.

Vinay and Moore, B. C. (2007). Speech recognition as a function of high-pass filter cutoff frequency for people with and without low-frequency cochlear dead regions. Journal of the Acoustical Society of America 122(1), 542-53.

Vinay, Baer, T. and Moore, B. C. (2008). Speech recognition in noise as a function of high pass-filter cutoff frequency for people with and without low-frequency cochlear dead regions. Journal of the Acoustical Society of America 123(2), 606-9.

Patients with higher cognitive function may benefit more from hearing aid features

Ng, E.H.N., Rudner, M., Lunner, T., Pedersen, M.S., & Ronnberg, J. (2013). Effects of noise and working memory capacity on memory processing of speech for hearing-aid users. International Journal of Audiology, Early Online, 1-9.

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

Research reports as well as clinical observations indicate that competing noise increases the cognitive demands of listening, an effect that is especially impactful for individuals with hearing loss (McCoy et al., 2005; Picou et al., 2013; Rudner et al., 2011).  Listening effort is a cognitive dimension of listening that is thought to represent the allocation of cognitive resources needed for speech recognition (Hick & Tharpe, 2002). Working memory, is a further dimension of cognition that involves the simultaneous processing and storage of information; its effect on speech processing may vary depending on the listening conditions (Rudner et al., 2011).

The concept of effortful listening can be characterized with the Ease of Language Understanding (ELU) model (Ronnberg, 2003; Ronnberg et al., 2008). In quiet conditions when the speech is audible and clear, the speech input is intact and is automatically and easily matched to stored representations in the lexicon. When speech inputs are weak, distorted or obscured by noise, mismatches may occur and speech inputs may need to be compared to multiple stored representations to arrive at the most likely match. In these conditions, allocation of additional cognitive resources, is required. Efficient cognitive functioning and large working memory capacity allows more rapid and successful matches between speech inputs and stored representations. Several studies have indicated a relationship between cognitive ability and speech perception: Humes (2007) found that cognitive function was the best predictor of speech understanding in noise and Lunner (2003) reported that participants with better working memory capacity and verbal processing speed had better speech perception performance.

Following the ELU model, hearing aids may allow listeners to match inputs and stored representations more successfully, with less explicit processing. Noise reduction, as implemented in hearing aids, has been proposed as a technology that may ease effortful listening. In contrast, however, it has been suggested that hearing aid signal processing may introduce unwanted artifacts or alter the speech inputs so that more explicit processing is required to match them to stored images (Lunner et al., 2009). If this is the case, hearing aid users with good working memory may function better with amplification because their expanded working memory capacity allows more resources to be applied to the task of matching speech inputs to long-term memory stores.

Elaine Ng and her colleagues investigated the effect of noise and noise reduction on word recall and identification and examined whether individuals were affected by these variables differently based on their working memory capacity. The authors had several hypotheses:

1. Noise would adversely affect memory, with poorer memory performance for speech in noise than in quiet.

2. Memory performance in noise would be at least partially restored by the use of noise reduction.

3. The effect of noise reduction on memory would be greater for items in late list positions because participants were older and therefore likely to have slower memory encoding speeds.

4. Memory in competing speech would be worse than in stationary noise because of the stronger masking effect of competing speech.

5. Overall memory performance would be better for participants with higher working memory capacity in the presence of noise reduction. This effect should be more apparent for late list items presented with competing speech babble.

Twenty-six native Swedish-speaking individuals with moderate to moderately-severe, high-frequency sensorineural hearing loss participated in the authors’ study. Prior to commencement of the study, participants were tested to ensure that they had age-appropriate cognitive performance. A battery of tests was administered and results were comparable to previously reported performance for their age group (Ronnberg, 1990).

Two tests were administered to study participants. First, a reading span test evaluated working memory capacity.  Participants were presented with a total of 24 three-word sentences and sub-lists of 3, 4 and 5 sentences were presented in ascending order. Participants were asked to judge whether the sentences were sensible or nonsense. At the end of each sub-list of sentences, listeners were prompted to recall either the first or final words of each sentence, in the order in which they were presented. Tests were scored as the total number of items correctly recalled.

The second test was a sentence-final word identification and recall (SWIR) test, consisting of 140 everyday sentences from the Swedish Hearing In Noise Test (HINT; Hallgren et al, 2006). This test involved two different tasks. The first was an identification task in which participants were asked to report the final word of each sentence immediately after listening to it.  The second task was a free recall task; after reporting the final word of the eighth sentence of the list, they were asked to recall all the words that they had previously reported. Three of seven tested conditions included variations of noise reduction algorithms, ranging from one similar to those implemented in modern hearing aids to an ‘ideal’ noise reduction algorithm.

Prior to the main analyses of working memory and recall performance, two sets of groups were created based on reading span scores, using two different grouping methods. In the first set, two groups were created by splitting the group at the median score so that 13 individuals were in a high reading span group and the remaining 13 were in a low reading span group. In the second set, participants who scored in the mid-range on the reading span test were excluded from the analysis, creating High reading span and Low reading span groups of 10 participants each. There was no significant difference between groups based on age, pure tone average or word identification performance, in any of the noise conditions. Overall reading span scores for participants in this study were comparable to previously reported results (Lunner, 2003; Foo, 2007).

Also prior to the main analysis, the SWIR results were analyzed to compare noise reduction and ideal noise reduction conditions. There was no significant difference between noise reduction and ideal noise reduction conditions in the identification or free recall tasks, nor was there an interaction of noise reduction condition with reading span score. Therefore, only the noise reduction condition was considered in the subsequent analyses.

The relationship between reading span score (representing working memory capacity) and SWIR recall was examined for all the test conditions. Reading span score correlated with overall recall performance in all conditions but one. When recall was analyzed as a function of list position (beginning or final), reading span scores correlated significantly with beginning (primacy) positions in quiet and most noise conditions. There was no significant correlation between overall reading span scores and items in final (recency) position in any of the noise conditions.

There were significant main effects for noise, list position and reading span group. In other words, when noise reduction was implemented, the negative effects of noise were lessened. There was a recency effect, in that performance was better for late list positions than for early list positions. Overall, the high reading span groups scored better than the low reading span groups, for both median-split and mid-range exclusion groups. The high reading span groups showed improved recall with noise reduction, whereas the low reading span groups exhibited no change in performance with noise reduction versus quiet.  The use of four-talker babble had a negative effect on late list positions, but did not affect items in other positions, suggesting that four-talker babble disrupted working memory more than steady-state noise. These analyses supported hypotheses 1, 2, 3 and 5, indicating that noise adversely affects memory performance (1), that noise reduction and list position interact with this effect (2,3) especially for individuals with high working memory capacity (5).

The results also supported hypothesis 4, which suggested that competing speech babble would affect memory performance more than steady state noise. Recall performance was significantly better in the presence of steady-state noise than it was in 4-talker babble. Though there was no significant effect of noise reduction overall, high reading span participants once again outperformed low reading span participants with noise reduction.

In summary, the results of this study determined that noise had an adverse effect on recall, but that this effect was mildly mitigated by the use of noise reduction. Four-talker babble was more disruptive to recall performance than was steady-state noise. Recall performance was better for individuals with higher working memory capacity. These individuals also demonstrated more of a benefit from noise reduction than did those with lower working memory capacity.

Recall performance is better in quiet conditions than in noise because presumably fewer cognitive resources are required to encode the speech input (Murphy, et al., 2000). Ng and her colleagues suggest that noise reduction helps to perceptually segregate speech from noise, allowing the speech input to be matched to stored lexical representations with less cognitive demand. So, noise reduction may at least partially reverse the negative effect of noise on working memory.

Competing speech babble is more likely to be cognitively demanding than steady-state noise (such as an air conditioner) because it contains meaningful information that is more distracting and harder to separate from the speech of interest (Sorqvist & Ronnberg, 2012). Not only is the speech signal of interest degraded by the presence of competing sound and therefore harder to encode, but additional cognitive resources are required to inhibit the unwanted or irrelevant linguistic information (Macken, 2009).  Because competing speech puts more demands on cognitive resources, it is more potentially disruptive than steady-state noise to perception of the speech signal of interest.

Unfortunately, much of the background noise encountered by hearing aid wearers is competing speech. The classic example of the cocktail party illustrates one of the most challenging situations for hearing-impaired individuals, in which they must try to attend to a proximal conversation while ignoring multiple conversations surrounding them. The results of this study suggest that noise reduction may be more useful in these situations for listeners with better working memory capacity; however, noise reduction should still be considered for all hearing aid users, with comprehensive follow-up care to make adjustments for individuals who are not functioning well in noisy conditions. Noise reduction may generally alleviate perceived effort or annoyance, allowing a listener to be more attentive to the speech signal of interest or to remain in a noisy situation that would otherwise be uncomfortable or aggravating.

More research is needed on the effects of noise, noise reduction and advanced signal processing on listening effort and memory in everyday situations. It is likely that performance is affected by numerous variables of the hearing aid, including compression characteristics, directionality, noise reduction, as well as the automatic implementation or adjustment of these features. These variables in turn combine with user-related characteristics such as age, degree of hearing loss, word recognition ability, cognitive capacity and more.

References

Foo, C., Rudner, M., & Ronnberg, J. (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, 618-631.

Hallgren, M., Larsby, B. & Arlinger, S. (2006). A Swedish version of the hearing in noise test (HINT) for measurement of speech recognition. International Journal of Audiology 45, 227-237.

Hick, C. B., & Tharpe, A. M. (2002). Listening effort and fatigue in school-age children with and without hearing loss. Journal of Speech Language and Hearing Research 45, 573–584.

Humes, L. (2007). The contributions of audibility and cognitive factors to the benefit provided by amplified speech to older adults. Journal of the American Academy of Audiology 18, 590-603.

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.

Macken, W.J., Phelps, F.G. & Jones, D.M. (2009). What causes auditory distraction? Psychonomic Bulletin and Review 16, 139-144.

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.

Picou, E.M., Ricketts, T.A. & Hornsby, B.W.Y. (2013). How hearing aids, background noise and visual cues influence objective listening effort. Ear and Hearing 34 (5).

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.

Sorqvist, P. & Ronnberg, J. (2012). Episodic long-term memory of spoken discourse masked by speech: What role for working memory capacity? Journal of Speech Language and Hearing Research 55, 210-218.