Starkey Research & Clinical Blog

Hearing Aids Alone can be Adjusted to Help with Tinnitus Relief

Shekhawat, G.S., Searchfield, G.D., Kobayashi, K. & Stinear, C. (2013). Prescription of hearing aid output for tinnitus relief. International Journal of Audiology 2013, 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.

The American Tinnitus Association (ATA) reports that approximately 50 million people in the United States experience some degree of tinnitus.About one third of tinnitus sufferers consider it severe enough to seek medical attention. Fortunately only a small proportion of tinnitus sufferers experience symptoms that are debilitating enough that they feel they cannot function normally. But even if it does not cause debilitating symptoms, for many tinnitus still causes a number of disruptive effects such as sleep interference, difficulty concentrating, anxiety, frustration and depression  (Tyler & Baker, 1983; Stouffer & Tyler, 1990; Axelsson, 1992; Meikle 1992; Dobie, 2004).

Therapeutic treatments for tinnitus include the use of tinnitus maskers, tinnitus retraining therapy, biofeedback and counseling . Though these methods provide relief for many the tendency for tinnitus to co-occur with sensorineural hearing loss (Hoffman & Reed, 2004) leads the majority of individuals to attempt management of tinnitus with the use of hearing aids alone (Henry, et al., 2005; Kochkin & Tyler, 2008; Shekhawat et al., 2013).  There are a number of benefits that hearing aids may offer for individuals with tinnitus:  audiological counseling during the fitting process may provide the individual with a better understanding of hearing loss and tinnitus (Searchfield et al., 2010); hearing aids may reduce the stress related to struggling to hear and understand; amplification of environmental sound may reduce perceived loudness of tinnitus (Tyler, 2008).

Prescriptive hearing aid fitting procedures are designed to improve audibility and assist hearing loss rather than address tinnitus concerns. Yet the majority of studies show that hearing aids alone can be useful for tinnitus management (Shekhawat et al., 2013). The Better Hearing Institute reports that approximately 28% of hearing aid users achieve moderate to substantial tinnitus relief with hearing aid use (Tyler, 2008). Approximately 66% of these individuals said their hearing aids offered tinnitus relief most or all the time and 29% reported that their hearing aids relieved their tinnitus all the time. However, little is known about how hearing aids should be adjusted to optimize this apparent relief from tinnitus. In a study comparing DSL I/O v4.0 and NAL-NL1, Wise (2003) found that low compression kneepoints in the DSL formula reduced tinnitus awareness for 80% of subjects, but these settings also made environmental sounds more annoying. Conversely, they had higher word recognition scores with NAL-NL1 but did not receive equal tinnitus reduction. The proposed explanation for this was the increased low-intensity, low-frequency gain of the DSL I/O formula versus the increased high frequency emphasis of NAL-NL1. Based on these findings, the author suggested the use of separate programs for regular use and for tinnitus relief.

Shekhawat and his colleagues began to address the issue of prescriptive hearing aid fitting for tinnitus by studying how output characteristics should be tailored to meet the needs of hearing aid users with tinnitus.  Specifically, they examined how modifying the high frequency characteristics of the DSL v5 (Scollie et al., 2005) prescription would affect subjects’ short term tinnitus perception.  Speech files with variable high frequency cut-offs and gain settings were designed and presented to subjects in matched pairs to arrive at the most favorable configuration for tinnitus relief.

Twenty-five participants mild to moderate high-frequency sensorineural hearing loss were recruited for participation. None of the participants had used hearing aids before but all indicated interest in trying hearing aids to alleviate their tinnitus.  All subjects had experienced chronic, bothersome tinnitus for at least two years and the average perception of tinnitus loudness was 62.6 on a scale from 1-100, where 1 is very faint and 100 is very loud. Subjects had a mean Tinnitus Functional Index (TFI; Meikle et al., 2012) score of 39.30. Six participants reported unilateral tinnitus localized to the left side, 15 had bilateral tinnitus and 4 reported tinnitus that localized to the center of the head, which is likely to be present bilaterally though not necessarily symmetrical.  The majority (40%) of the subjects reported their tinnitus quality as tonal, whereas 28% described it as noise, 20% as crickets and 12% as a combination of sound qualities. Tinnitus pitch matching was conducted using pairs of tones in which subjects were repeatedly asked to indicate which of the tones more closely matched the pitch of their tinnitus. The average matched tinnitus pitch was 7.892kHz with a range from 800Hz to 14.5kHz. When asked to describe the pitch of their tinnitus, most subjects defined it as “very high pitched”, some said “high pitched” and some said “medium pitched”.

There were 13 speech files, based on sentences spoken by a female talker, with variable high frequency characteristics. There were three cut-off frequencies (2, 4 and 6kHz) and four high frequency gain settings (+6, +3, -3 and -6dB). Stimuli were presented via a master hearing aid with settings programmed to match DSL I/O v5.0 prescriptive targets for each subject’s hearing loss.  Pairs of sentences were presented in a round robin tournament procedure  and subjects were asked to choose which one interfered most with their tinnitus and made it less audible. A computer program tabulated the number of “wins” for each sentence and collapsed the information across subjects to determine a “winner”, or the sentence that was most effective at reducing tinnitus audibility.  Real-ear measures were used to compare DSL v5 prescribed settings with the characteristics of the winning sentence and outputs were recorded from 250Hz to 6000Hz.

The most preferred output for interfering with tinnitus perception was a 6dB reduction at 2kHz, which was chosen by 26.47% of the participants.  A 6dB reduction at 4kHz was preferred by 14.74% of the subjects, followed by a 3dB reduction at 2kHz, which was preferred by 11.76%.  There were no significant differences between the preferences for any of these settings.

They found that when tinnitus pitch was lower than 4kHz, the preferred setting had lower output than DSL v5 across the frequency range. The difference was small (1-3dB) and became smaller as tinnitus pitch increased. When tinnitus pitch was between 4-8kHz, subjects preferred slightly less output than DSL v5 for high frequencies and slightly more output for low frequencies, though these differences were minimal as well. When tinnitus pitch was higher than 8kHz, participants preferred output that was slightly greater than DSL v5 at three frequencies: 750Hz, 1kHz and 6kHz. From these results a trend emerged: as tinnitus pitch increased, preferred output became lower than DSL v5 though the differences were not statistically significant.

Few studies investigating the use of hearing aids for tinnitus management have considered the perceived pitch of the tinnitus or the prescriptive method of the hearing aids (Shekhawat et al., 2013). The results of this study suggest that DSL v5 could be an effective prescriptive formula for hearing aids used in a tinnitus treatment plan, though the pitch of the individual’s tinnitus might affect the optimal output settings. In general, they found that the higher the tinnitus pitch, the more the preferred output matched with DSL I.O v5.0 targets. This study agrees with an earlier report by Wise (2003) in which subjects preferred DSL v5 over NAL-NL1 for interfering with and reducing tinnitus. It is unknown how NAL-NL2 targets would fare in a similar comparison, though the NAL-NL2 formula may provide more tinnitus relief than its predecessor because it tends to prescribe slightly higher gain for low frequencies and lower compression ratios which could potentially provide more of a masking effect from environmental sounds. The NAL-NL2 formula should be studied as it pertains to tinnitus management, perhaps along with consideration of other factors including degree of loss, gender and prior experience with hearing aids, since these affect the targets prescribed by the updated formula (Keidser & Dillon, 2006; Keidser et al., 2008). The subjects in the present study had similar degrees of loss and all lacked prior experience with amplification; the NAL-NL2 formula takes these factors into consideration, prescribing slightly different gain based on degree of loss or for those who have used hearing aids before.

The authors recommend offering separate hearing aid programs for use when the listener desires tinnitus relief. Most fitting formulae are designed to optimize speech intelligibility and audibility, and based on previous reports, an individual might prefer one formula when speech understanding and communication is their top priority, and may prefer another, used with or without an added noise masker, when their tinnitus is bothering them.

They also propose that tinnitus pitch matching should be considered when programming hearing aids, though there is often quite a bit of variability in results and testing needs to be repeated several times to increase reliability.  Still, their study agrees with prior work in suggesting that the pitch of the tinnitus affects how likely hearing aids are to reduce it and whether output adjustments can impact how effective the hearing aids are to this end. Schaette (2010) found that individuals with tinnitus pitch lower than 6kHz showed more reduction of tinnitus with hearing aid use than did subjects whose pitch was higher than 6kHz. This makes sense because of the typical bandwidth of hearing aids, in which most gain is delivered below this frequency range. Not surprisingly, another study reported that hearing aids were most effective at reducing tinnitus when the pitch of the tinnitus was within the frequency response range of the hearing aids (McNeil et al., 2012).  Though incorporating tinnitus pitch matching into a clinical protocol might seem daunting or time consuming, it is probably possible to use an informal bracketing procedure, similar to one used for MCLs, to get an idea of the individual’s tinnitus pitch range. Testing can be repeated at subsequent visits to eventually arrive at a more reliable estimate.  If pitch matching measures are not possible, clinicians can question the patient about their perceived tinnitus pitch range and, with reference the current study, adjust outputs in the 2kHz to 4kHz range to determine if the individual experiences improvement in tinnitus relief.

Proposed are a series of considerations for fitting hearing instruments on tinnitus sufferers and for employing dedicated tinnitus programs:

- noise reduction should be disabled;

- fixed activation of omnidirectional microphones introduce more environmental noise;

- in contrast to the previous recommendation, full-time activation of directional microphones will increase the hearing aid noise floor;

- lower compression knee points increase amplification for softer sounds;

- expansion should be turned off to increase amplification of low-level background sound;

- efforts should be made to  minimize occlusion, which can emphasize the perception of tinnitus;

- ensuring physical comfort of the devices can minimize the user’s general awareness of their ears and the hearing aids, potentially reducing their attention to the tinnitus as well (Sheldrake & Jastreboff, 2004; Searchfield, 2006);

- user controls are important as they allow access to alternate hearing aid programs and sound therapy options.

Dr. Shekhawat and his colleagues also underscore the importance of counseling tinnitus sufferers who choose hearing aids. Clinicians need to ensure that these patients have realistic expectations about the potential benefits of hearing aids and that they know the devices will not cure their tinnitus. Follow-up care is especially important to determine if adjustments or further training is necessary to improve the performance of the aids for all of their intended purposes.

Currently, little is known about how to optimize hearing aid settings for tinnitus relief and there are no prescriptive recommendations targeted specifically for tinnitus sufferers. Shekhawat and his colleagues propose that the DSL v5 formula may be an appropriate starting point for these individuals, as their basic program and/or in an alternate program designated for use when their tinnitus is particularly bothersome.  Most importantly, however, are the observations that intentional manipulation of parameters common to most hearing aid fittings may increase likelihood of tinnitus relief with hearing aid use. Further investigation into the optimization of these fitting parameters may reveal a prescriptive combination that audiologists can leverage to benefit individuals with hearing loss who also seek relief from the stress and annoyance of tinnitus.

 

References

American Tinnitus Association (ATA) reporting data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES), conducted by the Centers for Disease Control and Prevention (CDC). www.ata.org, retrieved 9-10-13.

Axelsson, A. (1992). Conclusion to Panel Discussion on Evaluation of Tinnitus Treatments. In J.M. Aran & R. Dauman (Eds) Tinnitus 91. Proceedings of the Fourth International Tinnitus Seminar (pp. 453-455). New York, NY: Kugler Publications.

Cornelisse, L.E., Seewald, R.C. & Jamieson, D.G. (1995). The input/output formula: A theoretical approach to the fitting of personal amplification devices. Journal of the Acoustical Society of America 97, 1854-1864.

Dobie, R.A. (2004). Overview: Suffering From Tinnitus. In J.B. Snow (Ed) Tinnitus: Theory and Management (pp.1-7). Lewiston, NY: BC Decker Inc.

Henry, J.A., Dennis, K.C. & Schechter, M.A. (2005). General review of tinnitus: Prevalence, mechanisms, effects and management. Journal of Speech, Language and Hearing Research 48, 1204-1235.

Hoffman, H.J. & Reed, G.W. (2004). Epidemiology of tinnitus. In: J.B. Snow (ed.) Tinnitus: Theory and Management. Hamilton, Ontario: BC Decker.

Keidser, G. & Dillon, H. (2006). What’s new in prescriptive fittings down under? In: Palmer, C.V., Seewald, R. (Eds.), Hearing Care for Adults 2006. Phonak AG, Stafa, Switzerland, pp. 133-142.

Keidser, G., O’Brien, A., Carter, L., McLelland, M. & Yeend, I. (2008). Variation in preferred gain with experience for hearing aid users. International Journal of Audiology 47(10), 621-635.

Kochkin, S. & Tyler, R. (2008). Tinnitus treatment and effectiveness of hearing aids: Hearing care professional perceptions. Hearing Review 15, 14-18.

McNeil, C., Tavora-Vieira, D., Alnafjan, F., Searchfield, G.D. & Welch, D. (2012). Tinnitus pitch, masking and the effectiveness of hearing aids for tinnitus therapy. International Journal of Audiology 51, 914-919.

Meikle, M.B. (1992). Methods for Evaluation of Tinnitus Relief Procedures. In J.M. Aran & R. Dauman (Eds.) Tinnitus 91: Proceedings of the Fourth International Tinnitus Seminar (pp. 555-562). New York, NY: Kugler Publications.

Meikle, M.B., Henry, J.A., Griest, S.E., Stewart, B.J., Abrams, H.B., McArdle, R., Myers, P.J., Newman, C.W., Sandridge, S., Turk, D.C., Folmer, R.L., Frederick, E.J., House, J.W., Jacobson, G.P., Kinney, S.E., Martin, W.H., Nagler, S.M., Reich, G.E., Searchfield, G., Sweetow, R. & Vernon, J.A. (2012). The Tinnitus Functional Index:  Development of a new clinical measure for chronic, intrusive tinnitus. Ear & Hearing 33(2), 153-176.

Moffat, G., Adjout, K., Gallego, S., Thai-Van, H. & Collet, L. (2009). Effects of hearing aid fitting on the perceptual characteristics of tinnitus. Hearing Research 254, 82-91.

Schaette, R., Konig, O., Hornig, D., Gross, M. & Kempter, R. (2010). Acoustic stimulation treatments against tinnitus could be most effective when tinnitus pitch is within the stimulated frequency range. Hearing Research 269, 95-101.

Shekhawat, G.S., Searchfield, G.D., Kobayashi, K. & Stinear, C. (2013). Prescription of hearing aid output for tinnitus relief. International Journal of Audiology 2013, early online: 1-9.

Shekhawat, G.S., Searchfield, G.D. & Stinear, C.M. In press (2013). Role of hearing aids in tinnitus intervention: A scoping review. Journal of the American Academy of Audiology.

Searchfield, G.D. (2006). Hearing aids and tinnitus. In: R.S. Tyler (ed). Tinnitus Treatment, Clinical Protocols. New York: Thieme Medical Publishers, pp. 161-175.

Searchfield, G.D., Kaur, M. & Martin, W.H. (2010). Hearing aids as an adjunct to counseling: Tinnitus patients who choose amplification do better than those that don’t. International Journal of Audiology 49, 574-579.

Sheldrake, J.B. & Jastreboff, M.M. (2004). Role of hearing aids in management of tinnitus. In: J.B. Sheldrake, Jr. (ed.) Tinnitus: Theory and Management. London: BC Decker Inc, pp. 310-313.

Stouffer, J.L. & Tyler, R. (1990). Characterization of tinnitus by tinnitus patients. Journal of Speech and Hearing Disorders 55, 439-453.

Tyler, R.S.(Ed). (2008). The Consumer Handbook on Tinnitus. Auricle Ink Publishers., Sedona, AZ.

Tyler, R. & Baker, L.J. (1983). Difficulties experienced by tinnitus sufferers. Journal of Speech and Hearing Disorders 48, 150-154.

Wise, K. (2003). Amplification of sound for tinnitus management: A comparison of DSL i/o and NAL-NL1 prescriptive procedures and the influence of compression threshold on tinnitus audibility. Section of Audiology, Auckland: University of Auckland.

 

Hearing Aid Behavior in the Real World

Banerjee, S. (2011). Hearing aids in the real world: typical automatic behavior of expansion, directionality and noise management. Journal of the American Academy of Audiology 22, 34-48.

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

Hearing aid signal processing offers proven advantages for many everyday listening situations. Directional microphones improve speech recognition in the presence of competing sounds and noise reduction decreases annoyance of surrounding noise while possibly improving ease of listening (Sarampalis et al., 2009). Expansion reduces the annoyance of low-level environmental noise as well as circuit noise from the hearing aid.  It is typical for modern hearing aids to offer automatic activation of signal processing features based on various information derived through acoustic analysis of the environment. In the case of some signal processing features, these can be assigned to independent, manually accessible hearing aid memories. The opportunity to manually activate a hearing aid feature allows patients to make conscious decisions about the acoustic conditions of the environment and access an appropriately optimized memory configuration (Keidser, 1996; Surr et al., 2002).

However, many hearing aid users who need directionality and noise reduction may be unable to manually adjust their hearing aids, due to physical limitations or an inability to determine the optimal setting for a situation. Other users may be reluctant to make manual adjustments for fear of drawing attention to the hearing aids and therefore the hearing impairment. Cord et al (2002) reported that as many as 23% of users with manual controls do not use their additional programs and leave the aids in a default mode at all times. Most hearing aids now offer automatic directionality and noise reduction, taking the responsibility for situational adjustments away from the user. This allows more hearing aid users the ability to experience advanced signal processing benefits and reduces the need for manual adjustments.

The decision to provide automatic activation of expansion, directionality, and noise reduction is based on their known benefits for particular acoustic conditions, but it is not well understood how these features interact with each other or with changing listening environments in every day use.  This poses a challenge to clinicians when it comes to follow-up fine-tuning, because it is impossible to determine what features were activated at any particular moment. Datalogging offers opportunity to better interpret a patient’s experience outside of the clinic or laboratory. Datalogging reports often include average daily or total hours of use as well as the proportion of time an individual has spent in quiet or noisy environments but these are general reports and do not provide insight into the activation of some signal processing features and the acoustic environment that occurred at the time of feature activation. For example, a clinician may be able to determine that an aid was in a directional mode 20% of the time and that the user spent 26% of their time listening to speech in the presence of noise, but it does not indicate whether directional processing was active during these exposures to speech in noise. Therefore, the clinician must rely on user reports and observations to determine the appropriate adjustments, which may not reliably represent the array of listening experiences and acoustic environments that were encountered (Wagener, 2008).

In the study discussed here, Banerjee investigated the implementation of automatic expansion, directionality and noise management features. She measured environmental sound levels to determine the proportion of time individuals spent in quiet and noisy environments, as well as how these input levels related to activation of automatic features. She also examined bilateral agreement across a pair of independently functioning hearing aids to determine the proportion of time that the aids demonstrated similar processing strategies.

Ten subjects with symmetrical, sensorineural hearing loss were fitted with bilateral, behind-the-ear hearing aids. Age ranged from 49-78 years with a mean of 62.3 years of age. All of the subjects were experienced hearing aid users.  Some subjects were employed and most participated in regular social activities with family and other groups. The hearing aids were 8-channel WDRC instruments programmed to match targets from the manufacturer’s proprietary fitting formula.  Activation of the automatic directional microphone required input levels of 60dB or above, with the presence of noise in the environment and speech located in front of the wearer. Automatic noise management resulted in gain reductions in one or more of the 8 channels, based on the presence of noise-like sounds classified as “wind, mechanical sounds or other sounds” based on their spectral and temporal characteristics. No gain reductions were applied for sounds classified as “speech”.  Expansion was active for inputs below the compression thresholds, which ranged from 54 to 27dB SPL.

All participants carried a Personal Digital Assistants (PDA) connected via programming boots to their hearing aids. This PDA logged environmental broadband input level as well as the status of expansion, directionality, noise management and channel-specific gain reduction. Participants were asked to wear the hearing aids connected to the PDA for as much of the day as possible and measurements were made in 5-sec intervals to allow time for hearing aid features to update several times between readings.  The PDAs were worn with the hearing aids for a period of 4-5 weeks and at the end of data collection a total of 741 hours of hearing aid use were logged and studied.

Examination of the input level measurements revealed that subjects spent about half of their time in quiet environments with input levels of 50dB SPL or lower. Less than 5% of their time was spent in environments with input levels exceeding 65dB and the maximum recorded input level was 105dB SPL. This concurs with previous studies that reported high proportions of time spent in quiet environments such as living rooms or offices (Walden et al., 2004; Wagener et al., 2008).  The interaural difference in input level was 1dB about 50% of the time and exceeded 5dB only 5% of the time. Interaural differences were attributed to head shadow effects and asymmetrical sound sources as well as occasional accidental physical contact with the hearing aids, such as adjusting eyeglasses or rubbing the pinna.

Expansion was analyzed in terms of the proportion of time it was activated and whether the aids were in bilateral agreement. Expansion thresholds are meant to approximate low-level speech presented at 50dB.  In this study, expansion was active between 42% and 54% of the time, which is consistent with its intended activation, because about half the time the input levels were at or below 50dB SPL.  Bilateral agreement was relatively high at 77-81%.

Directional microphone status was measured according to the proportion of time that directionality was active and whether there was bilateral agreement. Again, directional status was consistent with the broadband input level measurements, in that directionality was active only about 10% of the time. The instruments were designed to switch to directional mode only when input levels were higher than 60dBA, and the broadband input measurements showed that participants encountered inputs higher than 65dB only about 5% of the time. Bilateral agreement for directionality was very high at 97%. Interestingly, the hearing aids were in directional mode only about 50% of the time in the louder environments.  This is likely attributable to the requirement for not only high input levels but also speech located in front of the listener in the presence of surrounding noise. A loud environment alone should not trigger directionality without the presence of speech in front of the listener.

Noise reduction was active 21% of the time with bilateral agreement of 95%. Again, this corresponds well with the input level measurements because noise reduction is designed to activate only in levels exceeding 50dB SPL. This does not indicate how often it was activated in the presence of moderate to loud noise, but as input levels rose, gain reductions resulting from noise management steadily increased as well. Gain reduction was 3-5dB greater in channels below 2250Hz than in the high frequency channels, consistent with the idea that environmental noise contains more energy in the low frequencies. Interaural differences in noise management were very small with a median difference in gain reduction of 0dB in all channels and exceeding 1dB only 5% of the time.

Bilateral agreement was generally quite high. Conditions in which there was less bilateral agreement may reflect asymmetric sound sources, accidental physical contact with the hearing instruments or true disagreement based on small differences in input levels arriving at the two ears. There may be everyday situations in which hearing aids might not perform in bilateral agreement, but this is not necessarily a disadvantage to the user. For instance, a driver in a car might experience directionality in the left aid but omnidirectional pickup from the right aid. This may be advantageous for the driver if there is another occupant in the passenger’s seat. Similarly, at a restaurant a hearing aid user might experience disproportionate noise or multi-talker babble from one side, depending on where he is situated relative to other people. Omnidirectional pickup on the quieter side of the listener with directionality on the opposite side might be desirable and more conducive to conversation. Similar arguments could be proposed for asymmetrical activation of noise management and its potential effects on comfort and ease of listening in noisy environments.

Banerjee’s investigation is an important step toward understanding how hearing aid signal processing is activated in everyday conditions. Though datalogging helps provide an overall snapshot of usage patterns and listening environments, the gross reporting of data limits utility in fine-tuning of hearing aid parameters. This study, and others like it, will provide useful information for clinicians providing follow-up care with hearing aid users.

It is noteworthy that participants spent about 50% of their time in environments with 50dB of broadband input or lower. While some participants were employed and others were not, this remains an acoustic reality of the hearing aid wearer. Subsequent studies with targeted samples would help further determine how special features apply to everyday environments among participants that lead a more consistently active lifestyle.

Automatic, adaptive signal processing features have potential benefits for many hearing aid users, especially those who are unable to or prefer not to operate manual controls. However, proper recommendations and programming adjustments can only be made if clinicians understand how these features are implemented in everyday life. This study provides evidence that some features perform as designed and offers insight for clinicians to leverage when making fine-tuning instruments based on real world hearing aid behavior.

 

References

Banerjee, S. (2011). Hearing aids in the real world: typical automatic behavior of expansion, directionality and noise management. Journal of the American Academy of Audiology 22, 34-48.

Cord, M., Surr, R., Walden, B. & Olsen, L. (2002). Performance of directional microphone hearing aids in everyday life. Journal of the American Academy of Audiology 13, 295-307.

Keidser, G. (1996). Selecting different amplification for different listening conditions. Journal of the American Academy of Audiology 7, 92-104.

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

Surr, R., Walden, B., Cord, M. & Olsen, L. (2002). Influence of environmental factors on hearing aid microphone preference. Journal of the American Academy of Audiology 13, 308-322.

Wagener, K., Hansen, M. & Ludvigsen, C. (2008). Recording and classification of the acoustic environment of hearing aid users. Journal of the American Academy of Audiology 19, 348-370.

You’re getting older. Are your listening demands decreasing?

Wu, Y. & Bentler, R. (2012). Do older adults have social lifestyles that place fewer demands on hearing? Journal of the American Academy of Audiology 23, 697-711.

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

Activities and lifestyle are important considerations for potential hearing aid users because of the variability in listening environments that they may encounter. Individuals who work or have active social lives may be more likely to benefit from advanced signal processing and features like directionality and noise reduction than individuals with less social lifestyles in which a large proportion of time is spent at home or in quiet conditions.

It is often assumed that older individuals have quieter social lives and therefore fewer listening demands. This has been supported by a number of studies showing that older adults report less exposure to noisy environments and less communication demand in a variety of environments (Garstecki & Erler, 1996; Erdman & Demorest, 1998; Kricos, et al., 2007).  Despite the fact that older adults are more likely to experience hearing loss and poorer word recognition ability, older adults generally report less hearing disability and less social or emotional impact from their hearing loss than younger adults do (Gatehouse, 1990, 1994; Gordon-Salant et al., 1994; Garstecki & Erler, 1996; Uchida et al., 2003).  One explanation for this apparent contradiction is that older adults may have less demanding lifestyles than younger adults because they may encounter fewer challenging listening situations. This is assumed to be the case because older adults may participate in fewer social activities and have smaller social networks than younger adults.

The assumption that older adults are less prone to social interaction could be countered by the suggestion that retirement allows more time for social activities that could present communication challenges.  In fact, following retirement, older adults report having more time to travel, visit with family, and volunteer (Wiley et al., 2000).

The purpose of Wu and Bentler’s investigation was to compare auditory lifestyles of younger and older hearing-impaired adults and to study the relationships among age, auditory lifestyle and social lifestyle. They hypothesized that older adults would have quieter, less demanding lifestyles and that the relationship between age and auditory lifestyle would be affected by how socially active the older individuals were.

Twenty-seven hearing-impaired adults, ranging from 40 to 88 years of age, participated in the study. All subjects had symmetrical, sloping, sensorineural hearing losses. The majority of subjects were experienced hearing aid users. Auditory lifestyle, or the auditory environments encountered in typical daily activities, was measured using portable noise dosimeters, worn in a pack over the shoulder, for 7 consecutive days. The dosimeters were capable of measuring overall sound level over time. Though the dosimeters were not capable of specifically measuring signal-to-noise ratio (SNR), previous work has indicated that high overall sound level is associated with low SNR (Pearsons et al., 1976; Banerjee, 2011). Therefore, the authors assumed that the dosimeter reading were providing an indirect measurement of the SNRs encountered in the subjects’ daily lives and offered an indirect assessment of their typical daily listening demands.

Participants supplemented the dosimeter measurements with written journals describing the listening situations that they participated in during the week. They recorded their listening activities as well as the listening environments that they encountered. Listening activities were classified according to 6 categories:

1.              Conversation in small group (3 or fewer people)

2.              Conversation in large group (more than four people)

3.              Conversation on the phone

4.              Speech listening – live talker

5.              Speech listening – media

6.              Little or no conversation

There were five environment categories:

1.              Outdoors – traffic

2.              Outdoors – other than traffic

3.              Home – 10 people or fewer

4.              Indoors other than home – 10 people or fewer

5.              Crowd of people (more than 11 people)

Auditory lifestyle was evaluated with the Auditory Lifestyle and Demand Questionnaire (ALDQ; Gatehouse et al., 1999), which assesses the diversity of listening situations encountered by an individual. It is scaled according to frequency and importance of each situation and higher scores represent more diverse auditory lifestyles.

Social lifestyle was measured with three self-report questionnaires. The Social Network Index (SNI; Cohen et al., 1997) assesses the different social roles or identities held by an individual. For instance, a person could be a spouse, parent, employee or club member. Points are assigned for the various social roles assumed by the individual and higher point values indicate more active social lifestyles.

The second questionnaire, The Welin Activity Scale (WAS; Welin et al., 1992) measures the frequency of 32 activities, divided into three categories: home (e.g., reading), outside home (e.g., dining at restaurant) and social activities (e.g., visiting with friends). Subjects indicate how often they participate in each activity on a 3-point scale.  The sum of points for all activities and for activities outside the home are scored, and higher scores indicate more active lifestyles.

The third scale that was used to measure social lifestyle was the Social Convoy Questionnaire (SCQ; Kahn & Antonucci, 1980; Antonucci, 1986; Lang & Carstensen, 1994).  This questionnaire requires respondents to assign social partners to one of three concentric circles. The ratio of  inner circle partners to those in the outer two circles represents the closeness of social partners. Previous research has shown that younger adults have more peripheral partners than older adults, yielding lower SCQ scores than older adults in general (Lang & Carstensen, 1994).

Journal entries provided information about the proportion of time that subjects spent in speech-related activities, in quiet and noisy conditions. Participants in both age groups spent the highest proportion of time listening to media at home, followed by small-group conversations at home and small-group conversations away from home. The proportion of time spent in phone conversations or outdoors was relatively small for both groups. There were no significant differences between young and old subject groups for the percentages of time spent in any of the activity categories.

Analysis of the dosimetry measurements was conducted to determine the proportion of time participants spent in noisy conditions and the intensity of the sound they encountered.  The sound levels encountered by both groups had a spread of approximately 30dB and not surprisingly, the highest levels occurred in crowds and traffic and the lowest levels occurred at home.  The measured sound levels were higher for younger listeners than older listeners for most of the frequently encountered listening events though age-related differences reached significance for only two events: small group conversation in traffic and media listening in traffic.

ALDQ scores assessed the listeners’ auditory lifestyles and although older subjects had lower scores, suggesting that older listeners experienced less demanding auditory lifestyles, there were no significant differences between the two groups. Social lifestyle was measured with the SNI, WAS and SCQ scales. The only scale to yield a significant age-related difference was the SNI scale, in which younger listeners had higher scores than older listeners. This difference is in keeping with previous reports and indicates that older listeners in this study had less diverse and smaller social networks than younger subjects.

Prior to any further analysis, journal entries and dosimetry information were examined to come up with an indicator of listening demand, which was labeled LD-65. This score represents the amount of time a subject spent in speech-related conditions in which the sound levels were 65dBA or higher. Listeners had indicated that levels of 65dBA were “somewhat noisy”, so levels above this point were assumed to be “noisy”. Therefore, LD-65 was used as a measure of listening demand because higher LD-65 scores indicate that listeners were participating in more speech-related activities in conditions that were likely to be noisy.

Significant correlations were found for age versus SNI as well as age versus LD-65, indicating that older subjects had smaller social networks and were also likely to experience fewer listening demands than younger subjects. Additional analyses were required to determine that the effects of age on listening demand were mediated by social lifestyle. In other words, age did not affect listening demand on its own as much as it did when social lifestyle was also considered.

The results of this study indicate that younger and older adults have similar auditory lifestyles, in terms of the proportion of time they spend in speech-related activities, in quiet and noisy conditions. But whether or not older individuals experience fewer listening demands is a more complicated issue.

Depending upon the analysis, the results of this study may suggest little age-related difference between groups, while contrasting analyses suggest younger adults encountered higher sound levels than older adults did in comparable listening situations. This difference may relate to behavioral as well as situational differences. For instance, younger adults might drive faster, listen to louder music, or drive on the highway more often than older adults, which would have the effect of increasing sound level measurements in these conditions. Similarly, if some of the noisy situations encountered by younger adults were in bars or clubs, they would yield higher sound level measurements than moderately noisy restaurants. Although the age difference for the dosimeter measurements was significant, the difference in mean levels was only 2.8dB. The authors question whether this difference is truly noticeable and appropriately point out that there were not strict controls on placement of the dosimeter packs, so variability in placement could have affected the measurements somewhat.

The findings of this study suggest that assumptions about age should not wholly dictate clinical decisions in structuring a treatment plan so much as social activities and lifestyle should. Certainly, individuals of any age with diverse social activities will experience more listening demands than those with quieter lifestyles. Still, the experiences of employed individuals in the workplace may present more complicated listening demands for reasons other than overall sound levels and duration of exposure.  Employed hearing aid users may experience stress related to their communication ability when interacting with co-workers, managers, and supervisors that is not comparable to the listening demand experienced in purely social situations with similar sound levels. Because the selection of hearing aids can be affected by all of these variables, self-report inventories and detailed clinical histories that illuminate each individual’s social and auditory lifestyle will help to arrive at decisions appropriate for the patient.

 

References

Antonucci, T. (1986). Hierarchical mapping technique. Generations 10 (4), 10-12.

Banerjee, S. (2011). Hearing aids in the real world: typical automatic behavior of expansion, directionality and noise management. Journal of the American Academy of Audiology 22 (1), 34-48.

Cohen, S., Doyle, W., Skoner, D., Rabin, B. & Gwaltney, J. (1997). Social ties and susceptibility to the common cold. Journal of the American Medical Association 277 (24), 1940-1944.

Erdman, S. & Demorest, M. (1998). Adjustment to hearing impairment II: audiological and demographic correlates. Journal of Speech, Language and Hearing Research 41 (1), 123-136.

Garstecki, D. & Erler, S. (1996). Older adult performance on the communication profile for the hearing impaired. Journal of Speech and Hearing Research 39 (1), 28-42.

Gatehouse, S. (1990). The role of non-auditory factors in measured and self-reported disability. Acta Otolaryngologica Supplement 476, 249-256.

Gatehouse, S. (1994). Components and determinants of hearing aid benefit. Ear and Hearing 15 (1), 30-49.

Gatehouse, S., Elberling, C. & Naylor, G. (1999). Aspects of auditory ecology and psychoacoustic function as determinants of benefits from and candidature for non-linear processing hearing aids. In: Rasmussen, A.N., Osterhammel, P.A., Andersen, T., Poulsen, T., eds. Auditory Models and Non-Linear Hearing Instruments. Denmark: The Danavox Jubilee Foundation, 221-233.

Gordon-Salant, S., Lantz, J. & Fitzgibbons, P.J. (1994).  Age effects on measures of hearing disability. Ear and Hearing 15 (3), 262-265.

Kahn, R. & Antonucci, T. (1980). Convoys over the life course: attachment, roles and social support. In: Baltes, P.B., Brim, O.G., eds. Life-span Development and Behavior. San Diego, CA: Academic Press.

Kricos, P., Erdman, S., Bratt, G. & Williams, D. (2007). Psychosocial correlates of hearing aid adjustment. Journal of the American Academy of Audiology 18 (4), 304-322.

Lang, F. & Carstensen, L. (1994). Close emotional relationships in late life: further support for proactive aging in the social domain. Psychology of Aging 9 (2), 315-324.

Pearsons, K., Bennett, R. & Fidell, S. (1976). Speech Levels in Various Environments: Report to the Office of Resources and Development, Environmental Protection Agency. BBN Report #3281. Cambridge: Bolt, Beranek and Newman.

Uchida, Y., Nakashima, T., Ando, F., Niino, N. & Shimokata, H. (2003). Prevalence of self-perceived auditory problems and their relation to audiometric thresholds in a middle-aged to elderly population. Acta Otolaryngologica 123 (5), 618-626.

Welin, L., Larsson, B., Svardsudd, K., Tibblin, B. & Tibblin, G. (1992). Social network and activities in relation to mortality from cardiovascular diseases, cancer and other causes: a 12-year follow up of the study of men born in 1913 and 1923. Journal of Epidemiology and Community Health 46 (2), 127-132.

Wiley, T., Cruickshanks, K., Nondahl, D. & Tweed, S. (2000). Self-reported hearing handicap and audiometric measures in older adults. Journal of the American Academy of Audiology 11 (2), 67-75.

Wu, Y. & Bentler, R. (2012). Do older adults have social lifestyles that place fewer demands on hearing? Journal of the American Academy of Audiology 23, 697-711.

Does lip reading take the effort out of speech understanding?

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, in press.

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

For many people with hearing loss, visual cues from lip-reading are a valuable cue that has been proven to improve speech recognition across a variety of listening conditions (Sumby & Pollock, 1954; Erber, 1975; Grant, et al., 1998). To date is has remained unclear how visual cues, background noise, and hearing aid use interact with each other to affect listening effort.

Listening effort is often described as the allocation of additional cognitive resources to the task of understanding speech. If cognitive resources are finite or limited, then two or more simultaneous tasks will be in competition with each other for cognitive resources. Decrements in performance on one task can be interpreted as an allocation of resources away from the task and toward another concurrent task. Therefore, listening effort is often measured with dual-task paradigms, in which listeners respond to speech stimuli while simultaneously performing another task or responding to another kind of stimulus. Allocation of cognitive resources in this way is thought to represent a competition for working memory resources (Baddeley & Hitch, 1974; Baddeley, 2000).

The Ease of Language Understanding (ELU) model states that the process of understanding language involves matching phonological, syntactic, semantic and prosodic information to stored templates in long-term memory. When there is a mismatch between the incoming sensory information and the stored template, additional effort must be exerted to resolve the ambiguity of the message. This additional listening effort taxes working memory resources and may require the listener to allocate fewer resources to other tasks. Several studies have identified conditions that degrade a speech signal, such as background noise (Murphy, et al., 2000; Larsby et al., 2005; Zekveld et al., 2010) and hearing loss (Rabbitt, 1990; McCoy et al., 2005) in a manner that increases listening effort.

Individuals with reduced working memory capacity may be more negatively affected by conditions that degrade a speech signal. Previous reports have suggested that differences in working memory capacity hold a relationship to speech recognition in noise and performance with hearing aids in noise (Lunner, 2003; Foo et al., 2007).  The speed of retrieval from long-term memory may also affect performance and listening effort in adverse listening conditions (Van Rooij et al., 1989; Lunner, 2003). Because sensory inputs decay rapidly (Cowan, 1984), listeners with slow processing speed might not be able to fully process incoming information and match it to long term memory stores before it decays. Therefore, they would have to allocate more effort and resources to the task of matching sensory input to long-term memory templates.

Just as some listener traits might be expected to increase listening effort, some factors might offset adverse listening conditions by providing more information to support the matching of incoming sensory inputs to long-term memory. The use of visual cues is well known to improve speech recognition performance and some studies indicate that individuals with large working memory capacities are better able to make use of visual cues from lipreading (Picou et al., 2011).  Additionally, listeners who are better lipreaders may require fewer cognitive resources to understand speech, allowing them to make better use of visual cues in noisy environments (Hasher & Zacks, 1979; Picou et al., 2011).

The purpose of Picou, Ricketts and Hornsby’s study was to examine how listening effort is affected by hearing aid use, visual cues and background noise. A secondary goal of the study was to determine how specific listener traits such as verbal processing speed, working memory and lipreading ability would affect the measured changes in listening effort.

Twenty-seven hearing-impaired adults participated in the study. All were experienced hearing aid users and had corrected binocular vision of 20/40 or better. Participants were fitted with bilateral behind-the-ear hearing aids with non-occluding, non-custom eartips. Advanced features such as directionality and noise reduction were turned off, though feedback management was left on in order to maximize usable gain. Hearing aids were programmed with linear settings to eliminate any potential effect of amplitude compression on listening effort, a relationship which is as of yet unestablished.

A dual-task paradigm with a primary speech recognition task and secondary visual reaction time task was used to measure listening effort. The speech recognition task used monosyllabic words spoken by a female talker (Picou, 2011), presented at 65dB in the presence of multi-talker babble. Prior to the speech recognition task, individual SNRs for auditory only (AO) and auditory-visual (AV) conditions were determined at levels that yielded performance between 50-70% correct, because scores in this range are most likely to show changes in listening effort (Gatehouse & Gordon, 1990).

The reaction time task required participants to press a button in response to a rectangular visual probe that occurred prior to presentation of the speech token. The visual probe was presented prior to the speech tokens, so that the probe would not distract from the use of visual cues during the speech recognition task. The visual and speech stimuli were presented within a narrow enough interval (less than 500 msec) so that cognitive resources would have to be expended for both tasks at the same time (Hick & Tharpe, 2002).

Three listener traits were examined with regard to listening effort in quiet and noisy conditions, with and without visual cues. Visual working memory was evaluated with the Automated Operation Span (AOSPAN) test (Unsworth et al., 2005). The AOSPAN requires subjects to solve math equations and memorize letters. After seeing a math equation and identifying the answer, subjects are shown a letter which disappears after 800 msec. Following a series of equations they are then asked to identify the letters that they saw, in the order that they appeared. Scores are based on the number of letters that are recalled correctly.

Verbal processing speed was assessed with a lexical decision task (LDT) in which subjects were presented with a string of letters and were asked to indicate, as quickly as possible, if the letters formed a real word.  The test consisted of 50 common monosyllabic English words and 50 monosyllabic nonwords. The task reflects verbal processing speed because it requires the participant to match the stimuli to representations of familiar words stored in long-term memory (Meyer & Schvaneveldt, 1971; Milberg & Blumstein, 1981; Van Rooij et al., 1989). The reaction time to respond to the stimuli was used as a measure of verbal processing speed.

Finally, lipreading ability was measured with the Revised Shortened Utley Sentence Lipreading Test (ReSULT; Updike, 1989). The test required participants to repeat sentences spoken by a female talker, when the talker’s face was visible but speech was inaudible. Responses were scored based on the number of words repeated correctly in each sentence.

Subjects participated in two test sessions. At the first session, vision and hearing was tested, individual SNR levels were determined for the speech recognition task and AOSPAN, LDT and ReSULT scores were obtained.  At the second session, subjects completed practice sequences with AO and AV stimuli, then the dual speech recognition and visual reaction time tests were administered in eight counterbalanced conditions listed below. Due to the number of experimental conditions, only select outcomes of this study will be reviewed.

1.         auditory only in quiet, unaided

2.         auditory only in noise, unaided

3.         auditory-visual in quiet, unaided

4.         auditory-visual in noise, unaided

5.         auditory only in quiet, aided

6.         auditory only in noise, aided

7.         auditory-visual in quiet, aided

8.         auditory-visual in noise, aided

The main analysis showed that background noise impaired performance in all conditions and hearing aid use and visual cues improved performance. However, there were significant interactions between hearing aid use and visual cues, hearing aids and background noise, and visual cues and background noise, indicating that the effect of hearing aid use depended on the test modality (AV or AO), and background noise (present or absent), and the effect of visual cues depended on background noise (present or absent).  Hearing aid benefit proved to be larger in AO conditions than in AV conditions and was larger in quiet conditions than in noisy conditions.  The effect of noise was greater in the AV conditions than in the AO conditions, but the authors suggest that this could have been related to the individualized SNRs chosen for the test procedure.

On the reaction time task, background noise increased listening effort and hearing aid use reduced listening effort, though there was high variability and the effects of both variables were small. Additional analysis determined that the individual SNRs chosen for the dual task did not affect the hearing aid benefits that were measured. The availability of visual cues did not change overall reaction times and it was therefore determined that visual cues did not affect listening effort in this task of reaction time.

With regard to listening effort benefits derived from hearing aid use, the performance in quiet conditions was strongly related to performance in noise. In other words, subjects who obtained benefit from hearing aid use in quiet also obtained benefit in noise and individuals with slower verbal processing speed were more likely to derive benefit from hearing aid use. With regard to visual cues, there were several correlations with listener traits. Subjects who were better lipreaders derived more benefit from visual cues and those with smaller working memory capacities also showed more benefit from visual cues. These correlations were significant in quiet and noisy conditions. For quiet conditions, there was a positive correlation between verbal processing speed and benefit from visual cues, with better verbal processors showing more benefit from visual cues. There were no correlations between background noise and any of the measured listener traits.

The overall findings that visual cues and hearing aid use had positive effects and background noise had a negative effect on speech perception performance were not surprising. Similarly, the findings that hearing aid benefit was reduced for AV conditions versus AO conditions and for noisy versus quiet conditions were consistent with previous reports (Cox & Alexander, 1991; Walden et al., 2001; Duquesnoy & Plomp, 1983).  Because hearing aid use improves audibility, visual cues might not have been needed as much as they were in unaided conditions and the presence of noise may have counteracted the improved audibility by masking a portion of the speech cues needed for correct understanding, especially with the omnidirectional, linear instruments used in this study.

The ability of hearing aids to decrease listening effort was significant, in keeping with previously published results, but the improvements were lesser than than those reported in some previous studies. This could be related to the non-simultaneous timing of the tasks in the dual-task paradigm, but the authors surmise that it could be related to the way their subjects’ hearing aids were programmed. In most previous studies, individuals used their own hearing aids, set to individually prescribed and modified settings. In the current study, all participants used the same hearing aid circuit set to linear, unmodified targets. Advanced features like directionality and noise reduction, which are likely to impact listening effort (Sarampalis, 2009), speech discrimination ability and perceived ease of listening in everyday situations, were turned off.

There was a significant relationship between verbal processing speed and hearing aid benefit, in that subjects with slower processing speed were more likely to benefit from hearing aid use.  Sensory input decays rapidly and requires additional cognitive effort when it is mismatched with long-term memory stores. Any factor that improves the sensory input may facilitate the matching process. The authors posited that slow verbal processors might benefit more from amplification because hearing aids improved the quality of the sensory input, thereby reducing the cognitive effort and time that would otherwise be required to match the input to long-term memory templates.

On average, the availability of visual cues did not have a significant effect on listening effort. This may be a surprising result given the well-known positive effects of visual cues for speech recognition. However, there was high variability among subjects and it was apparent that better lipreaders were more able to make use of visual cues, especially in quiet conditions without hearing aids. Working memory capacity was negatively correlated with benefit from visual cues, indicating that subjects with better working memory capacity derived less benefit from visual cues on average. The relationship between these variables is unclear, but the authors suggest that individuals with lower working memory capacities may be more susceptible to changes in listening effort and therefore more likely to benefit from additional sensory information such as visual cues.

Understanding how individual traits affect listening effort and susceptibility to noise is important to audiologists for a number of reasons, partly because we often work with older individuals. Working memory declines as a result of the normal aging process and may begin in middle age (Wang, et al., 2011).  Similarly, the speed of cognitive processing slows and visual impairment becomes more likely with increasing age (Clay, et al., 2009). Many patients seeking audiological care may also suffer from these deficits in working memory, verbal processing, and visual acuity. Though more research is needed to understand how these variables relate to one another, they should be considered in clinical evaluations and hearing aid fittings.  Advanced hearing aid features that counteract the degrading effects of noise and reverberation may be particularly important for elderly or visually impaired hearing aid users. As shown in the reviewed study, these patients will benefit significantly from face-to-face conversation, slow speaking rates and reduced environmental distractions. Counseling sessions should include discussion of these issues so that patients and family members understand how they can use strategic listening techniques, in addition to hearing aids, to improve speech recognition and reduce cognitive effort.

References

Clay, O., Edwards, J., Ross, L., Okonkwo, O., Wadley, V., Roth, D. & Ball, K. (2009). Visual function and cognitive speed of processing mediate age-related decline in memory span and fluid intelligence. Journal of Aging and Health 21(4), 547-566.

Cox, R.M. & Alexander, G.C. (1991).  Hearing aid benefit in everyday environments. Ear and Hearing 12, 127-139.

Downs, D.W. (1982). Effects of hearing aid use on speech discrimination and listening effort. Journal of Speech and Hearing Disorders 47, 189-193.

Duquesnoy, A.J. & Plomp, R. (1983). The effect of a hearing aid on the speech reception threshold of hearing impaired listeners in quiet and in noise. Journal of the Acoustical Society of America 73, 2166-2173.

Erber, N.P. (1975). Auditory-visual perception of speech. Journal of Speech and Hearing Disorders 40, 481-492.

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.

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

Gatehouse, S. & Gordon, J. (1990). Response times to speech stimuli as measures of benefit from amplification. British Journal of Audiology 24, 63-68.

Grant, K.W., Walden, B.F. & Seitz, P.F. (1998).  Auditory visual speech recognition by hearing impaired subjects. Consonant recognition, sentence recognition and auditory-visual integration. Journal of the Acoustical Society of America 103, 2677-2690.

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.

Hornsby, B.W.Y. (2013).  The Effects of Hearing Aid Use on Listening Effort and Mental Fatigue Associated with Sustained Speech Processing Demands. Ear and Hearing, in press.

Meyer, D.E. & Schvaneveldt, R.W. (1971). Facilitation in recognizing pairs of words: Evidence of a dependence between retrieval operations. Journal of Experimental Psychology 90, 227-234.

Milberg, W. & Blumstein, S.E. (1981). Lexical decision and aphasia: Evidence for semantic processing. Brain and Language 14, 371-385.

Picou, F.M., Ricketts, T.A. & Hornsby, B.W.Y (2011). Visual cues and listening effort: Individual variability. Journal of Speech, Language and Hearing Research 54, 1416-1430.

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, in press.

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

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

Sumby, W.H. & Pollock, I. (1954). Visual contribution to speech intelligibility in noise. Journal of the Acoustical Society of America 26, 212-215.

Unsworth, N., Heitz, R.P. & Schrock, J.C. (2005). An automated version of the operation span task. Behavioral Research Methods 37, 498-505.

Van Rooij, J.C., Plomp, R. & Orlebeke, J.F. (1989).  Auditive and cognitive factors in speech perception by elderly listeners. I: Development of test battery. Journal of the Acoustical Society of America 86, 1294-1309.

Walden, B.F., Grant, K.W. & Cord, M.T. (2001). Effects of amplification and speechreading on consonant recognition by persons with impaired hearing. Ear and Hearing 22, 333-341.

Wang, M., Gamo, N., Yang, Y., Jin, L., Wang, X., Laubach, M., Mazer, J., Lee, D. & Arnsten, A. (2011). Neuronal basis of age-related working memory decline. Nature 476, 210-213.

Can hearing aids reduce listening fatigue?

Hornsby, B.W.Y. (2013). The Effects of Hearing Aid Use on Listening Effort and Mental Fatigue Associated with Sustained Speech Processing Demands. Ear and Hearing, Published Ahead-of-Print.

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

A patient recently told me that he wanted to put on his glasses so he could hear me better.  He was joking, but was correct in understanding that visual cues help facilitate speech understanding. When engaged in conversation, a listener uses many sources of information to supplement the auditory stimulus. Visual cues from lip-reading, gestures and expressions as well as situational cues, conversational context and the listener’s knowledge of grammar all help limit the possible interpretations of the message. Conditions that degrade the auditory stimulus, such as reverberation, background noise and hearing loss cause increased reliance on other cues in order for the listener to “fill in the blanks” and understand the spoken message. The use of these additional information sources amounts to an increased allocation of cognitive resources, which has also been referred to as increased “listening effort” (Downs, 1982; Hick & Tharpe, 2002; McCoy et al., 2005).

Research suggests 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). This may be of particular concern to elderly people and those with cognitive, memory or other sensory deficits. The increased listening effort caused by hearing loss is associated with self-reports of stress, tension and fatigue (Copithorne 2006; Edwards 2007). In a study of factory workers, Hetu et al. (1988) reported that individuals with difficulty hearing at work needed increased attention, concentration and effort, leading to increased stress and fatigue. It is reasonable to conclude that listening effort as studied in the laboratory should be linked to subjective associations of hearing loss with mental fatigue, but the relationship is not clear. Dr. Hornsby points out that laboratory studies typically evaluate short-term changes in resource allocation as listening ease is manipulated in the experimental task. However, perceived mental fatigue is more likely to result from sustained listening demands over a longer period of time, e.g., a work day or social engagement lasting several hours (Hetu et al., 1988; Kramer et al., 2006).

The purpose of Dr. Hornsby’s study was to determine if hearing aids, with and without advanced features like directionality and noise reduction, reduce listening effort and subsequent susceptibility to mental fatigue. He also investigated the relationship between objective measures of speech discrimination and listening effort in the laboratory with subjective self-reports of mental fatigue.

Sixteen adult subjects participated in the study. All had bilateral, symmetrical, mild-to-severe sensorineural hearing loss. Twelve subjects were employed full-time and reported being communicatively active about 65% of the time during the day. The remaining subjects were not employed but reported being communicatively active about 61% of the day. Twelve subjects were bilateral hearing aid users and four subjects were non-users. Subjects were screened to rule out cognitive dysfunction. All participants were fitted with bilateral behind-the-ear hearing aids with slim tubes and dome ear tips.  Hearing aids were programmed in basic and advanced modes. In basic mode, the microphones were omnidirectional and all advanced features except feedback suppression were turned off. In advanced mode, the hearing aids were set to manufacturer’s defaults with automatically adaptive directionality, noise reduction, reverberation reduction and wind noise reduction. All subjects wore the study hearing aids for at least 1-2 weeks before the experimental sessions began.

For the objective measurements of listening effort, subjects completed a word recognition in noise task paired with an auditory word recall task and a measure of visual reaction time.  Subjects heard random sets of 8 to 12 monosyllabic words preceded by the carrier phrase, “Say the word…” They were asked to repeat the words aloud and the percentage of correct responses was scored. In addition, subjects were asked to remember the last 5 words of each list. The end of the list was indicated by the word “STOP” on a screen in front of the speaker. Subjects were instructed to press a button as quickly as possible when the visual prompt appeared. Because the lists varied from 8 to 12 items, subjects never knew when to expect the visual prompt.  To control for variability in motor function, visual reaction time was measured alone in a separate session, during which subjects were instructed to simply ignore the speech and noise.

Subjective ratings of listening effort and fatigue were obtained with a five-item scale, administered prior to the experimental sessions. Three questions were adapted from the Speech Spatial and Qualities of Hearing Questionnaire (SSQ: Gatehouse & Noble, 2004) and the remaining items were formulated specifically for the study. Questions were phrased to elicit responses related to that particular day (“Did you have to put in a lot of effort to hear what was being said in conversation today?”, “How mentally/physically drained are you right now?”).  The final two questions were administered before and after the dual-task session and measured changes in attention and fatigue due to participation in the experimental tasks.

The word recognition in noise test yielded significantly better results in both aided conditions than in the unaided condition, though there was no difference between the basic and advanced aided conditions. The differences between unaided and aided scores varied considerably, suggesting that listening effort for individual subjects varied across conditions.  Unaided word recall was significantly poorer than basic or advanced aided performance. There was a small, significant difference between the two aided conditions, with advanced settings outperforming basic settings. In follow-up planned comparison tests, the aided vs. unaided difference was maintained though there was not a significant difference between the two aided conditions.

The reaction time measurement also assessed listening effort or the cognitive resources required for the word recognition test.  Reaction times were analyzed according to listening condition as well as block, which compared the first three trials (initial block) to the last three trials (final block).  Increases in reaction time by block represented the effect of task-related fatigue.  Analysis by listening condition showed that unaided reaction times increased more than reaction times for the advanced aided condition but not the basic aided condition. In other words, subjects required more time to react to the visual stimulus in the unaided condition than they did in the advanced aided condition. There was no significant difference between the two aided conditions.  There was a significant main effect for block; reaction times increased over the duration of the task. There was no interaction between listening condition and block; changes in performance over time were consistent across unaided and aided conditions.

One purpose of the study was to investigate the effect of hearing aid use on mental fatigue. Interestingly, comparison of initial and final blocks indicated that word recognition scores increased about 1-2% over time but improvement over time did not vary across listening conditions. There was no decrease in performance on word recall over time, nor did changes in performance over time vary significantly across listening conditions.  But reaction time did increase over time for all conditions, indicating a shift in cognitive resources away from the reaction time task and toward the primary word recognition task. Though the effect of hearing aid use was not significant, a trend appeared suggesting that fewer aided listeners had increased reaction.

The questionnaires administered before the session probed perceived effort and fatigue throughout the day, whereas the questions administered before and after the task probed focus, attention and mental fatigue before and after the test session. In all listening conditions there was a significant increase in mental fatigue and difficulty maintaining attention after completion of the test session. A non-significant trend suggested some difference between unaided and aided conditions.

To identify other factors that may have contributed to variability, correlations for age, pure tone average, high frequency pure tone average, unaided word recognition score, SNR during testing, employment status and self-rated percentage of daily communicative activity were calculated with the subjective and objective measurements. None of the correlations were significant, indicating that none of these factors contributed substantially to the variability observed in the study.

Cognitive resource allocation is often studied with dual-task paradigms like the one used in this study. Decrements in performance on the secondary task indicate a shift in cognitive resources to the primary task. Presumably, factors that increase difficulty in the primary task will increase allocation of resources to the primary task.  In these experiments, the primary task was a word recognition test and the secondary tasks were word recall and reaction time measurements. Improved word recall and quicker reaction times in aided conditions indicate that the use of hearing aids made the primary word recognition task easier, allowing listeners to allocate more cognitive resources to the secondary tasks. Furthermore, reaction times increased less over time in aided conditions than in unaided conditions.  These findings specifically suggest that decreased listening effort with hearing aid use may have made listeners less susceptible to fatigue as the dual-task session progressed.

Though subjective reports in this study showed a general trend toward reduced listening effort and concentration in aided conditions, there was not a significant improvement with hearing aid use. This contrasts with previous work that has shown reductions in subjective listening effort with the use of hearing aids (Humes et al., 1999; Hallgren et al., 2005; Noble & Gatehouse, 2006). The author notes that auditory demands vary widely and that participants were asked to rate their effort and fatigue based on “today”, which didn’t assess perceptions of sustained listening effort over a longer period of time may not have detected subtle differences among subjects.  For instance, working in a quiet office environment may not highlight the benefit of hearing aids or the difference between an omnidirectional or directional microphone program, simply because the acoustic environment did not trigger the advanced features often enough. In contrast, working in a school or restaurant might show a more noticeable difference between unaided listening, basic amplification and advanced signal processing. Though subjects reported being communicatively active about the same proportion of the day, this inquiry didn’t account for sustained listening effort over long periods of time, or varying work and social environments. These differences would likely affect overall listening effort and fatigue, as well as the value of advanced hearing aid features.

Clinical observations support the notion that hearing aid use can reduce listening effort and fatigue.  Prior to hearing aid use, hearing-impaired patients often report feeling exhausted from trying to keep up with social interactions or workplace demands. After receiving hearing aids, patients commonly report being more engaged, more able to participate in conversation and less drained at the end of the day. Though previous reports have supported the value of amplification on reduced listening effort, Hornsby’s study is the first to provide experimental data for the potential ability of hearing aid use to reduce mental fatigue.

These findings have important implications for all hearing aid users, but may have particular importance for working individuals with hearing loss as well as elderly hearing impaired individuals.  It is important for any working person to maintain a high level of job performance and to establish their value at work. Individuals with hearing loss face additional challenges in this regard and often take pains to prove that their hearing loss is not adversely affecting their work.  Studies in workplace productivity underscore the importance of reducing distractions for maintaining focus, reducing stress and persisting at difficult tasks (Clements-Croome, 2000; Hua et al., 2011). Studies indicating that hearing aids reduce listening effort and fatigue, presumably by improving audibility and reducing the potential distraction of competing sounds, should provide additional encouragement for employed hearing-impaired individuals to pursue hearing aids.

 

References

Baldwin, C.L. & Ash, I.K. (2011). Impact of sensory acuity on auditory working memory span in young and older adults. Psychology of Aging 26, 85-91.

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

Clements-Croome, D. (2000). Creating the productive workplace. Publisher: London, E & FN Spon.

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].

Downs, M. (1982). Effects of hearing aid use on speech discrimination and listening effort. Journal of Speech and Hearing Disorders 47, 189-193.

Edwards, B. (2007). The future of hearing aid technology. Trends in Amplification 11, 31-45.

Gatehouse, S. & Noble, W. (2004). The speech, Spatial and Qualities of Hearing Scale (SSQ). International Journal of Audiology 43, 85-99.

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.

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.

Hua, Y., Loftness, V., Heerwagen, J. & Powell, K. (2011). Relationship between workplace spatial settings and occupant-perceived support for collaboration. Environment and Behavior 43, 807-826.

Humes, L.E., Christensen, L. & Thomas, T. (1999). A comparison of the aided performance and benefit provided by a linear and a two-channel wide dynamic range compression hearing aid. Journal of Speech, Language and Hearing Research 42, 65-79.

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

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.

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. (2011). Visual cues and listening effort: Individual variability. Journal of Speech, Language and Hearing Research 54, 1416-1430.

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).

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

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.

Valente, M. & Mispagel, K. (2008) Unaided and aided performance with a directional open-fit hearing aid. International Journal of Audiology 47(6), 329-336.

Evidence for the Value of Real-Ear Measurement

Abrams, H.B., Chisolm, T.H., McManus, M., & McArdle, R. (2012). Initial-fit approach versus verified prescription: Comparing self-perceived hearing aid benefit. Journal of the American Academy of Audiology, 23(10), 768-778.

Audiology best practice guidelines state that probe microphone verification measures should be done to ensure that hearing aid gain and output characteristics meet prescribed targets for the individual. In the American Academy of Audiology’s Guidelines for the Audiologic Management of Adult Hearing Impairment, an expert task force recommends that “prescribed gain from a validated prescriptive method should be verified using a probe microphone approach that is referenced to ear canal SPL” (Valente, et al., 2006). Similarly, the Academy’s Pediatric Amplification Protocol (AAA, 2003) states that hearing aid output characteristics should be verified with real-ear measures or with real-ear-to-coupler-difference (RECD) calculations when lengthy adjustments subsequent to real-ear measurement are not possible.

In contrast to these recommendations, the majority of hearing aid providers are not routinely conducting real-ear verification measures. In a survey of audiologists and hearing instrument specialists, Mueller and Picou (2010) found that respondents used real-ear verification only about 40% of the time and Bamford (2001) reported that only about 20% of individuals fitting pediatric patients used real-ear measures. The reasons most often cited for skipping probe microphone measures are based on financial, time, or space constraints.

When probe microphone measures are not conducted, other verification techniques may be used such as aided word recognition, but these not likely to provide reliable information (Thornton & Raffin, 1978).  Or, verification may not be attempted at all, with fitting parameters being chosen based on the manufacturer’s initial-fit specifications. Although most fitting software allows for entry of age, experience and acoustic information such as canal length and venting characteristics, their predictions are based on average data and cannot account for individual ear canal effects.

Numerous studies have shown that initial-fit algorithms often deviate significantly from prescribed targets, usually underestimating required gain, especially in the high frequencies. Hawkins & Cook (2003) found that simulated fittings from one manufacturer’s initial-fit algorithm over-estimated the coupler gain and in-situ response by as much as 20dB, especially in the low and high frequencies.  Bentler (2004) compared the 2cc coupler response from six different hearing aids programmed with initial-fit algorithms and found that the responses were different for each manufacturer and deviated from prescriptive targets by as much as 15dB, usually falling below prescribed targets. Similarly, Bretz (2006) studied three manufacturers’ pediatric first-fit algorithms and found that the average output varied by about 20dB and initial-fit gain values were below both NAL-NL1 and DSL (i/o) targets. This is of particular concern because pediatric patients may be less able than adults to provide subjective responses to hearing aid settings, rendering objective measures such as real-ear verification even more important.

These studies and others illuminate the potential difference between first-fit hearing aid settings and those verified by objective measures, but it is not well known how this affects the user’s perceived benefit.  Some early reports using linear amplification targets indicated that verification did not predict perceived benefit (Nerbonne et al., 1995; Weinstein et al., 1995), but more recent work indicates that adults fit to DSL v5.0a targets demonstrated benefit as measured by the Client Oriented Scale of Improvement (COSI, Dillon & Ginis, 1997). A recent survey by Kochkin et al. (2010) found that patients whose fittings were verified with a comprehensive protocol including real-ear verification reported increased hearing aid usage, benefit and satisfaction. Furthermore, these respondents were more likely to recommend their hearing care professional to friends and family than were the respondents who were not fitted with real-ear verification.

The purpose of the study discussed here was to determine if perceived hearing aid benefit differed based on whether the user was fitted with an initial-fit algorithm only or with modified settings based on probe-microphone verification. Twenty-two experienced hearing aid users with mild to moderately-severe hearing loss participated in the study. All were fitted with binaural hearing aids, though a variety of hearing aid styles and manufacturers were represented.  Probe microphone measurements were conducted on all subjects, but  those in the initial-fitting group did not receive adjustments based on the verification measures.

Perceived hearing aid benefit was measured using the Abbreviated Profile of Hearing Aid Benefit (APHAB, Cox & Alexander, 1995). The APHAB consists of 24 items in four subscales: ease of communication (EC), reverberation (RV), background noise (BN) and aversiveness of sounds (AV).  In addition to subscale scores, an average global score can be calculated, as well as a benefit score which represents the difference between unaided and aided responses.

Prior to being fitted with their hearing aids, participants completed the APHAB questionnaire. Because all were experienced hearing aid users, they were asked to base their answers on their experiences without amplification.  Hearing aid fittings and probe microphone verification were then conducted on all subjects, but half of the subjects received adjustments to match prescribed targets and half of the subjects maintained their first-fit settings. Efforts were made to ensure that subjects were not aware of the difference between the initial-fit and verified fitting methods. The only adjustments that subjects in the initial-fit group received were based on issues that could affect their willingness to wear the hearing aids, such as loudness discomfort or feedback.

One month following the first appointment, subjects returned to the clinic and were administered the APHAB again. They were given their initial “unaided” APHAB responses to use as a comparison. After completion of the APHAB, the subjects who had been fitted with the initial-fit algorithms were switched to verified fittings and those had been fitted to prescribed targets were switched to the manufacturer’s initial-fit settings. All subjects were re-tested with probe microphone measures and those with loudness or feedback complaints received minor adjustments.

One month after the second appointment, subjects returned to complete the APHAB and were again allowed to use their original APHAB responses as a basis for comparison. They were not allowed to view their responses to the APHAB that was administered after the first hearing aid trial. Participants were also asked to indicate which fitting method (Session 1 or Session 2) they preferred and would want permanently programmed into their hearing aids.

Analysis of the probe microphone measurements indicated, not surprisingly, that the verified fittings were more closely matched to prescriptive targets than the fittings based on the first-fit algorithms, even after minor adjustments based on comfort and user preferences.  For three of the APHAB subscales – ease of communication, reverberation and background noise – scores obtained with verified fittings were superior to those obtained with the initial-fit approach and the main effect of fitting approach was found to be statistically significant. There was no interaction between fitting approach and APHAB subscale, indicating that the better outcomes obtained with verified fittings were not related to any specific listening environment.

When asked to indicate their preferred fitting method, 7 of the 22 participants selected the initial-fit approach, whereas more than twice as many subjects, 15 out of 22, selected the verified fitting. For all but 5 subjects, the global difference score on the APHAB predicted their preferred fitting method, and the relationship between global score and final preference was statistically significant.

The findings of this study and of related reports bring up some philosophical and practical considerations for audiologists. One of our primary goals is to provide effective rehabilitation for hearing-impaired patients and this is most often accomplished by fitting and dispensing quality hearing instruments. Clinical and research data repeatedly indicates the importance of probe microphone verification. It serves the best interest of our patients to offer them the most effective fitting approach, so it follows that probe microphone verification measures should be a routine, essential part of our clinical protocol.

The reports that a minority of hearing aid fittings are being verified with real-ear measures indicates that many clinicians are not following recommended best practices. Indeed, Palmer (2009) points out that failure to follow best practice guidelines is a departure from the ethical standards of professional competence. Failure to provide the recommended objective verification for hearing aid fittings does run counter to our clinical goals and as Palmer suggests may even be damaging to our “collective reputation” as a profession.

Philosophical arguments notwithstanding, there are also practical reasons to incorporate real-ear measures into the fitting protocol. In the MarkeTrak VIII survey, Kochkin reported that hearing aid users who received probe microphone verification testing as part of a detailed fitting protocol were more satisfied with their hearing instruments and were more likely to refer their clinician to friends. In the current field of hearing aid service provision, it is important for audiologists to consider ways that they can meaningfully distinguish themselves from online, mail-order and big-box retail competitors. Hearing aid users are becoming well-informed consumers and it is clear that establishing a base of satisfied patients who feel they have received comprehensive, competent care is crucial for growing a private practice. Probe microphone verification is a brief yet effective part of ensuring successful hearing aid fittings and it benefits our patients and our profession to provide this essential service.

References

Abrams, H.B., Chisolm, T.H., McManus, M., & McArdle, R. (2012). Initial-fit approach versus verified prescription: Comparing self-perceived hearing aid benefit. Journal of the American Academy of Audiology, 23(10), 768-778.

American Academy of Audiology (2003). Pediatric Amplification Protocol. www.audiology.org, (accessed 3-3-13).

Bamford,  J., Beresford, D., Mencher, G.(2001). Provision and fitting of new technology hearing aids: implications from a survey of some “good practice services” in UK and USA. In: Seewald, R.C., Gravel, J.S., eds. A Sound Foundation Through Early Amplification: Proceedings of an International Conference. Stafa, Switzerland: Phonak AG, 213–219.

Bentler, R. (2004). Advanced hearing aid features: Do they work? Paper presented at the convention of the American Speech-Language-Hearing Association, Washington, D.C.

Bretz, K. (2006). A comparison of three hearing aid manufacturers’ recommended first fit to two generic prescriptive targets with the pediatric population. Independent Studies and Capstones, Paper 189. Program in Audiology and Communication Sciences, Washington University School of Medicine. http://digitalcommons.wustl.edu/pacs_capstones/189.

Cox, R. & Alexander, G. (1995). The abbreviated profile of hearing aid benefit. Ear and Hearing 16, 176-183.

Dillon, H. & Ginis, J. (1997). Client Oriented Scale of Improvement (COSI) and its relationship to several other measures of benefit and satisfaction provided by hearing aids. Journal of the American Academy of Audiology 8: 27-43.

Hawkins, D. & Cook, J. (2003). Hearing aid software predictive gain values: How accurate are they? Hearing Journal 56, 26-34.

Kochkin, S., Beck, D., & Christensen, L. (2010). MarkeTrak VIII: The impact of the hearing health care professional on hearing aid user success. Hearing Review 17, 12-34.

Mueller, H., & Picou, E. (2010). Survey examines popularity of real-ear probe-microphone measures. Hearing Journal 63, 27-32.

Nerbonne, M., Christman, W. & Fleschner, C. (1995). Comparing objective and subjective measures of hearing aid benefit. Poster presentation at the annual convention of the American Academy of Audiology, Dallas, TX.

Palmer, C.V. (2006). Best practice: it’s a matter of ethics. Audiology Today, Sept-Oct.,31-35.

Thornton, A. & Raffin, M. (1978) Speech-discrimination scores modeled as a binomial variable. Journal of Speech and Hearing Research 21, 507–518.

Valente, M., Abrams, H., Benson, D., Chisolm, T., Citron, D., Hampton, D., Loavenbruck, A., Ricketts, T., Solodar, H. &  Sweetow, R. (2006). Guidelines for the Audiological Management of Adult Hearing Impairment. Audiology Today, Vol 18.

Weinstein, B., Newman, C. & Montano, J. (1995). A multidimensional analysis of hearing aid benefit. Paper presented at the 1st Biennial Hearing Aid Research & Development Conference, Bethesda, MD.

Can Aided Audibility Predict Pediatric Lexical Development?

Stiles, D.J., Bentler, R.A., & McGregor, K.K. (2012). The speech intelligibility index and the pure-tone average as predictors of lexical ability in children fit with hearing aids. Journal of Speech Language and Hearing Research, 55, 764-778.

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

Despite advances in early hearing loss identification, hearing aid technology, and fitting and verification tools, children with hearing loss consistently demonstrate limited lexical abilities compared to children with normal hearing.  These limitations have been illustrated by poorer performance on tests of vocabulary (Davis et al., 1986), word learning (Gilbertson & Kamhi, 1995; Stelmachowicz et al., 2004), phonological discrimination, and non-word repetition (Briscoe et al., 2001; Delage & Tuller, 2007; Norbury, et al., 2001).

There are a number of variables that may predict hearing-impaired children’s performance on speech and language tasks, including the age at which they were first fitted with hearing aids and the degree of hearing loss.  Moeller (2000) found that children who received earlier aural rehabilitation intervention demonstrated significantly larger receptive vocabularies than those who received later intervention.  Degree of hearing loss, which is typically defined in studies by the pure-tone average (PTA) or the average of pure-tone hearing thresholds at 500Hz, 1000Hz, and 2000Hz (Fletcher, 1929), has been significantly correlated with speech recognition (Davis et al., 1986; Gilbertson & Kamhi, 1995), receptive vocabulary (Fitzpatrick et al., 2007; Wake et al., 2005), expressive grammar, and word recognition (Delage & Tuller, 2007) in some studies comparing hearing-impaired children to those with normal hearing.

In contrast, other studies have reported that pure-tone average (PTA) did not predict language ability in hearing-impaired children.  Davis et al. (1986) tested hearing-impaired subjects between five and18 years of age and found no significant relationship between PTA and vocabulary, verbal ability, reasoning, and reading.  However, all subjects scored below average on these measures, regardless of their degree of hearing loss.  Similarly, Moeller (2000) found that age of intervention affected vocabulary and verbal reasoning, but PTA did not.  Gilbertson and Kamhi (1995) studied novel word learning in hearing-impaired children ranging in age from  seven to 10 years and found that neither PTA nor unaided speech recognition threshold was correlated to receptive vocabulary level or word learning.

At a glance, it seems likely that degree of hearing loss should affect language development and ability, because hearing loss affects audibility, and speech must be audible in order to be processed and learned.  However, the typical PTA of thresholds at 500Hz, 1000Hz, and 2000Hz does not take into account high-frequency speech information beyond 2000Hz.  Some studies using averages of high-frequency pure-tone thresholds (HFPTA) have found a significant relationship between degree of loss and speech recognition (Amos & Humes, 2007; Glista et al., 2009).

Because most hearing-impaired children now benefit from early identification and intervention, their pure-tone hearing threshold averages (PTA or HFTPA) might not be the best predictors of speech and language abilities in every-day situations.  Rather, a measure that combines degree of hearing loss as well as hearing aid characteristics might be a better predictor of speech and language ability in hearing-impaired children.  The Speech Intelligibility Index (SII; ANSI,2007), a measure of audibility that computes  the importance of different frequency regions based on the phonemic content of a given speech test, has proven to be predictive of performance on speech perception tasks for adults and children (Dubno et al., 1989; Pavlovic et al., 1986; Stelmachowicz et al., 2000).  Hearing aid gain characteristics can be incorporated into the SII algorithm to yield an aided SII, which has been reported to predict performance on word repetition (Magnusson et al., 2001) and nonsense syllable repetition ability in adults (Souza & Turner, 1999).  Because an aided SII includes the individual’s hearing loss and hearing aid characteristics into the calculations, it better represents how audibility affects an individual’s daily functioning.

The purpose of the current study was to evaluate the aided SII as a predictor of performance on measures of word recognition, phonological working memory, receptive vocabulary, and word learning.  Because development in these areas establishes a base for later achievements in language learning and reading (Tomasello, 2000; Stanovich, 1986), it is important to determine how audibility affects lexical development in hearing-impaired children.  Though the SII is usually calculated based on the particular speech test to be studied, the current investigation used aided SII values based on average speech spectra.  The authors explained that vocabulary acquisition is a cumulative process, and they intended to use the aided SII as a measure of cumulative, rather than test-specific, audibility.

Sixteen hearing-impaired children with hearing aids (CHA) and 24 children with normal hearing (CNH) between six and nine years of age participated in the study.  All of the hearing-impaired children had bilateral hearing loss and had used amplification for at least one year.  All participants used spoken language as their primary form of communication.  Real-ear measurements were used to calculate the aided SII at user settings.  Because the goal was to evaluate the children’s actual audibility as opposed to optimal audibility, their current user settings were used in the experiment whether or not they met DSL prescriptive targets (Scollie et al., 2005).

Subjects participated in tasks designed to assess four lexical domains.  Word recognition was measured by the Lexical Neighborhood Test and Multisyllabic Lexical Neighborhood Test (LNT and MLNT; Kirk & Pisoni, 2000).  These tests each contain “easy” and “hard” lists, based on how frequently the words occur in English and how many lexical neighbors they have.  Children with normal lexical development are expected to show a gradient in performance with the best scores on the easy MLNT and poorest scores on the hard LNT.  Non-word repetition was measured by a task prepared specifically for this study, using non-words selected based on adult ratings of “wordlikeness”.  In the word recognition and non-word repetition tasks, children were simply asked to repeat the words that they heard.  Responses were scored according to the number of phonemes correct for both tasks.  Additionally, the LNT and MLNT tests were scored based on number of words correct.  Receptive vocabulary was measured by the Peabody Picture Vocabulary Test (PPVT-III; Dunn & Dunn, 1997) in which the children were asked to view four images and select the one that corresponds to the presented word.  Raw scores are determined as the number of items correctly identified and norms are applied based on the subject’s age.  Novel word learning was assessed using the same stimuli from the non-word repetition task, after the children were given sentence context and visual imagery to teach them the “meaning” of the novel words.  Their ability to learn the novel words was evaluated in two ways: a production task in which they were asked to say the word when prompted by a corresponding picture and an identification task in which they were presented with an array of four items and were asked to select the item that corresponded to the word that was presented.

On the word recognition tests, the children with hearing aids (CHA) demonstrated poorer performance than the children with normal hearing (CNH) for measures of word and phoneme accuracy, though both groups demonstrated the expected gradient, with performance improving in parallel fashion from the hard LNT test through the easy MLNT test.  There was a correlation between aided SII and word recognition scores, but PTA and aided SII were equally good at predicting performance.

On the non-word repetition task, which requires auditory perception, phonological analysis, and phonological storage (Gathercole, 2006), CHA again demonstrated significantly poorer performance than CNH, and CNH performance was near ceiling levels.  PTA and aided SII scores were correlated with non-word repetition scores.  Beyond the effect of PTA, it was determined that aided SII accounted for 20% of the variance on the non-word repetition task, which was statistically significant.

The receptive vocabulary test yielded similar results; CHA performed significantly worse than CNH and both PTA and aided SII accounted for a significant proportion of the variance.

The only variable that predicted performance on the word learning tasks was age, which only yielded a significant effect on the word production task.  On the word identification task, both the CHA and CNH groups scored only slightly better than chance and there were no significant effects of group or age.

As was expected in this study, children with hearing aids (CHA) consistently showed poorer performance than children with normal hearing (CNH), with the exception of the novel word learning task.  The pattern of results suggests that aided audibility, as measured by the aided SII, was better at predicting performance than degree of hearing loss as measured by PTA.  Greater aided SII scores were consistently associated with more accurate word recognition, more accurate non-word repetition, and larger receptive vocabulary.

Although PTA or HFPTA may represent the degree of unaided hearing loss, because the aided SII score accounts for the contribution of the individual’s hearing aids, it is likely a better representation of speech audibility and auditory perception in everyday situations.  The authors point out that depending on the audiometric configuration and hearing aid characteristics, two individuals with the same PTA could have different aided SIIs, and therefore different auditory experiences.

The results of this study underscore the importance of audibility for lexical development, which in turn has significant implications for further development of language, reading, and academic skills.  Therefore, the early provision of audibility via appropriate and verifiable amplification appears to be an important step in the development of speech and language.  The SII, which is already incorporated into some real-ear systems or is available in a standalone software package, is a verification tool that should be considered a standard part of the fitting protocol for pediatric hearing aid patients.

 

References

American National Standards Institute (2007). Methods for calculation of the Speech Intelligibility index (ANSI S3.5-1997[R2007]). New York, NY: Author.

Amos, N.E. & Humes, L.E. (2007). Contribution of high frequencies to speech recognition in quiet and noise in listeners with varying degrees of high-frequency sensorineural hearing loss. Journal of Speech, Language and Hearing Research 50, 819-834.

Briscoe, J., Bishop, D.V. & Norbury, C.F. (2001). Phonological processing, language and literacy: a comparison of children with mild-to-moderate sensorineural hearing loss and those with specific language impairment. Journal of Child Psychology and Psychiatry 42, 329-340.

Davis, J.M., Elfenbein, J., Schum, R. & Bentler, R.A. (1986). Effects of mild and moderate hearing impairments on language, educational and psychosocial behavior of children. Journal of Speech and Hearing Disorders 51, 53-62.

Delage, H. & Tuller, L. (2007). Language development and mild-to-moderate hearing loss: Does language normalize with age? Journal of Speech, Language and Hearing Research 50, 1300-1313.

Dubno, J.R., Dirks, D.D. & Schaefer, A.B. (1989). Stop-consonant recognition for normal hearing listeners and listeners with high-frequency hearing loss. II: Articulation index predictions. The Journal of the Acoustical Society of America 85, 355-364.

Dunn, L.M. & Dunn, D.M. (1997). Peabody Picture Vocabulary Test – III. Circle Pines, MN: AGS.

Fitzpatrick, E., Durieux-Smith, A., Eriks-Brophy, A., Olds., J. & Gaines, R. (2007). The impact of newborn hearing screening on communications development. Journal of Medical Screening 14, 123-131.

Fletcher, H. (1929). Speech and hearing in communication. Princeton, NJ: Van Nostrand Reinhold.

Gilbertson, M. & Kamhi, A.G. (1995). Novel word learning in children with hearing impairment. Journal of Speech and Hearing Research 38, 630-642.

Glista, D., Scollie, S., Bagatto, M., Seewald, R., Parsa, V. & Johnson, A. (2009). Evaluation of nonlinear frequency compression: Clinical outcomes. International Journal of Audiology 48, 632-644.

Kirk, K.I. & Pisoni, D.B. (2000). Lexical Neighborhood Tests. St. Louis, MO:AudiTEC.

Magnusson, L., Karlsson, M. & Leijon, A. (2001). Predicted and measured speech recognition performance in noise with linear amplification. Ear and Hearing 22, 46-57.

Moeller, M.P. (2000). Early intervention and language development in children who are deaf and hard of hearing. Pediatrics 106, e43.

Norbury, C.F., Bishop, D.V. & Briscoe, J. (2001). Production of English finite verb morphology: A comparison of SLI and mild-moderate hearing impairment. Journal of Speech, Language and Hearing Research 44, 165-178.

Pavlovic, C.V., Studebaker, G.A. & Sherbecoe, R.L. (1986). An articulation index based procedure for predicting the speech recognition performance of hearing-impaired individuals. The Journal of the Acoustical Society of America 80, 50-57.

Scollie, S.D., Seewald, R., Cornelisse, L., Moodie, S., Bagatto, M., Laurnagary, D. & Pumford, J. (2005). The desired sensation level multistage input/output algorithm. Trends in Amplification 9(4), 159-197.

Souza, P.E. & Turner, C.W. (1999). Quantifying the contribution of audibility to recognition of compression-amplified speech. Ear and Hearing 20, 12-20.

Stanovich, K.E. (1986). Matthew effects in reading: Some consequences of individual differences in the acquisition of literacy. Reading Research Quarterly 21, 360-407.

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

Tomasello, M. (2000). The item-based nature of children’s early syntactic development. Trends in Cognitive Sciences 4, 156-163.

Wake, M., Poulakis, Z., Hughes, E.K., Carey-Sargeant, C. & Rickards, F.W. (2005). Hearing impairment: A population study of age at diagnosis, severity and language outcomes at 7-8 years. Archives of Disease in Childhood 90, 238-244.

 

The Tinnitus Functional Index (TFI): A New and Improved way to Evaluate Tinnitus

Meikle, M.B., Henry, J.A., Griest, S.E., Stewart, B.J., Abrams, H.B., McArdle, R., Myers, P.J., Newman, C.W., Sandridge, S., Turk, D.C., Folmer, R.L., Frederick, E.J., House, J.W., Jacobson, G.P., Kinney, S.E., Martin, W.H., Nagler, S.M., Reich, G.E., Searchfield, G., Sweetow, R. & Vernon, J.A. (2012). The Tinnitus Functional Index:  Development of a new clinical measure for chronic, intrusive tinnitus. Ear & Hearing 33(2), 153-176.

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

The practice of clinical audiology can arguably be described as having two primary goals: the diagnosis of auditory and vestibular disorders, followed by verifiable, effective treatment and rehabilitation. There are well established, objective diagnostic tests for hearing and vestibular disorders and their treatment methods can be verified with objective and subjective tools. The evaluation and treatment of tinnitus, though equally important, is more complicated. There are test protocols for matching perceived tinnitus characteristics, but the impact of tinnitus on the individual must be measured subjectively with self-assessment questionnaires.  There are several published questionnaires to evaluate tinnitus severity and the impact it has on an individual’s activities, emotions and relationships. However, most of these questionnaires were not designed specifically to measure the effect of tinnitus treatments (Kamalski et al., 2010), so their value as follow-up measures is unknown.

Tinnitus affects as many as 50 million Americans and can have disabling effects including: sleep interference, difficulty concentrating and attending, anxiety, frustration and depression (for review see Tyler & Baker, 1983; Stouffer & Tyler, 1990; Axelsson, 1992; Meikle 1992; Dobie, 2004b). There are numerous methods of treatment available, including hearing aids, tinnitus maskers, tinnitus retraining therapy, biofeedback, counseling and others. Because there is currently no standard assessment tool to evaluate tinnitus treatment outcomes, the effectiveness of tinnitus treatment methods is difficult to verify and compare. The Tinnitus Functional Index (TFI) was developed as a collaborative effort among researchers and clinicians to develop a validated, standard questionnaire that can be used clinically for intake assessments and follow-up measurements and in the laboratory as a way of comparing treatment efficacy and identifying subject characteristics.

The developers of the TFI aimed for this inventory to be used in three ways:

1. As an intake evaluation tool to identify individual differences in tinnitus patients.
2. As a reliable and valid measurement of multiple domains of tinnitus severity.
3. As an outcome measure to assess treatment-related change in tinnitus.

The study, supported by a grant from the Tinnitus Research Consortium (TRC), had three stages. The first stage involved consultation with 21 tinnitus experts, including audiologists, otologists and hearing researchers. The panel of experts evaluated 175 items from nine previously published tinnitus questionnaires and judged them based on their relevance to 10 tinnitus negative impact domains as well as their expected responsiveness, or ability to measure treatment-related improvement. After analyzing the content validity, relevance and potential responsiveness of the 175 items (Haynes et al., 1995), 43 items were selected for the first questionnaire prototype. The TRC initially required that 10 domains of negative tinnitus impact be covered by the TFI but this expert panel added three additional domains so that the first prototype of the TFI covered 13 domains of tinnitus impact. The TRC also recommended avoiding overly negative items (such as those referring to suicidal thoughts or feeling victimized or helpless), items referring to hearing loss without mentioning tinnitus and items referring to more than one subtopic. Each domain had at least three or four items, based on recommendations for achieving adequate reliability (Fabrigar et al., 1999; Moran et al., 2001).  Each questionnaire item probed respondents for a rating on a scale of 0 to 10, based on how they experienced their tinnitus “over the past week”. For example, a typical question read, “Over the past week, how easy was it for you to cope with your tinnitus?” with potential responses from 0 being “very easy to cope” and 10 being “impossible to cope”.

During the second stage of the study, TFI Prototype 1 was tested on 326 tinnitus patients at five independent clinical sites.  The goals for the second stage were to determine the responsiveness of items or their ability to reflect changes in tinnitus status, to evaluate the 13 tinnitus impact domains and to determine the TFI’s ability to scale tinnitus severity. The questionnaire was administered at the initial intake assessment, after 3 months and after 6 months.  In addition to completing the TFI, at the initial encounter the subjects completed a brief tinnitus history questionnaire, The Tinnitus Handicap Inventory (THI; Newman et al., 1996) and the Beck Depression Inventory-Primary Care (BDEI-PC; Beck et al., 1997).  The TFI was re-administered to 65 subjects after 3 months and again to 42 subjects after 6 months.

The researchers found that subjects had very few problems responding to the 43 selected items and that most questionnaires were fully completed. There were no floor or ceiling effects, indicating that there were no items for which responses clustered at either end of the scale, reducing the potential responsiveness of the item.  The TFI had very high convergent validity, which means it agreed well with other published scales of tinnitus severity, such as the THI.  There were large effect sizes, demonstrating that the Prototype 1 items had good responsiveness for treatment-related change and supporting use of the TFI as an outcome measure. Factor analysis of the 13 initial tinnitus impact domains yielded 8 clearly structured domains, which were retained for the second prototype.

The third stage of the study involved development and evaluation of TFI Prototype 2, which was modified based on validity and reliability measurements from the first prototype. Prototype 2 included the 30 best-functioning items from the first version, categorized according to 8 tinnitus impact domains. It was administered to 347 new participants at the initial assessment. Follow-up data were obtained from 155 participants after 3 months and from 85 participants after 6-months. Encouragingly, the results from clinical evaluation of Prototype 2 again showed good performance for all of the validity and reliability measurements, supporting its use for scaling tinnitus severity.

The best performing items from Prototype 2 were used to create the final version of the TFI, which contains 25 items in 8 domains or sub-scales: Intrusive, Sense of Control, Cognitive, Sleep, Auditory, Relaxation, Quality of Life and Emotional. Seven of the domains contain 3 items and the Quality of Life domain contains 4 items.

When used during the initial assessment, the TFI categorizes tinnitus severity according to five levels: not a problem, a small problem, a moderate problem, a big problem or a very big problem.  As a screening tool, this allows a clinician to determine the overall severity of the tinnitus to help formulate a treatment plan and consider whether referrals to other clinical professionals are needed. For example, an individual who scores in the “not a problem” level may require only brief reassurance and counseling and may be asked to follow-up only if symptoms progress. In contrast, an individual who scores in the “big problem” or “very big problem” categories will likely need referrals for additional diagnostic and therapeutic services right away.

The developers of the TFI acknowledge that their study is preliminary and more research is needed to determine the TFI’s value as an outcome measurement tool. However, based on their analyses they recommend that a change in TFI score of 13 should be considered meaningful. In other words, a decrease of 13 or more indicates an improvement based on treatment recommendations or an increase in 13 or more indicates a significant exacerbation of symptoms.

Most tinnitus self-report questionnaires were designed to assess tinnitus impact but do not specifically measure treatment outcomes. The Tinnitus Handicap Inventory (THI; Newman et al., 1996), however, has shown some promise as an initial evaluation tool and as a way to measure treatment outcome.  After formulation of the final version of the TFI, the effect sizes of the TFI were compared to the THI. Overall, the TFI had greater responsiveness, indicating that it could potentially yield statistically significant differences with fewer subjects than the THI would require. Evaluation of subs-scale domains yielded some differences between the TFI and THI, primarily related to the “Catastrophic” subscale of the THI. Most of these items were not included in the TFI, based on the TRC’s recommendations to avoid questions dealing with negative ideation. The TRC recommended against inclusion of items relating to despair inability to escape tinnitus and fear of having a terrible disease, because they may suggest to people with mild tinnitus that they will eventually have these concerns, creating feelings of negativity before treatment has started.  Because these items on the THI correlated only moderately with the more neutrally worded items on the TFI, the authors suggested that the THI Catastrophic subscale might measure a different severity domain than the TFI and may be useful in combination with the THI as an outcome measure.

The Tinnitus Functional Index (TFI), like other previously published tinnitus questionnaires, shows promise as a tool to measure and classify tinnitus severity. It is easy for respondents to understand the test items and can be administered briefly at or prior to the initial appointment. An additional benefit of the TFI appears to be its validity as an outcome measure of treatment effectiveness. This is critically important for guiding clinical decisions and modifying ongoing treatment plans. It also suggests that the TFI could be useful in laboratory research as a standardized way to evaluate and compare tinnitus treatment methods or to identify subject characteristics for inclusion in treatment groups. For instance, if a treatment is expected to affect the negative emotional impact of tinnitus more than the functional impact, the TFI could be useful in identifying appropriate subject candidates who are experiencing strong emotional reactions to their tinnitus. The Tinnitus Functional Index (TFI) is one of the most systematically validated methods of assessing a patient’s reaction to their tinnitus. Ease of application and interpretation place the TFI among the most compelling assessment options for clinicians working with tinnitus patients.

If you would like to use the TFI. It is now available on a website posted by Oregon Health & Science University (OHSU). OHSU owns the copyright to the TFI and permission is required by OHSU to use the TFI. The request form takes 3 minutes to complete and allows you access to the TFI form and instructions. You will then be able to use the TFI with no fee.

http://www.ohsu.edu/xd/health/services/ent/services/tinnitus-clinic/tinnitus-functional-index.cfm

References

Axelsson, A. (1992). Conclusion to Panel Discussion on Evaluation of Tinnitus Treatments. In J.M. Aran & R. Dauman (Eds) Tinnitus 91. Proceedings of the Fourth International Tinnitus Seminar (pp. 453-455). New York, NY: Kugler Publications.

Beck, A.T., Guth, D. & Steer, R.A. (1997). Screening for major depression disorders in medical in patients with the Beck Depression Inventory for Primary Care. Behavioral Research and Therapy 35, 785-791.

Dobie, R.A. (2004b). Overview: Suffering From Tinnitus. In J.B. Snow (Ed) Tinnitus: Theory and Management (pp.1-7). Lewiston, NY: BC Decker Inc.

Fabrigar, L.R., Wegeners, D.T. & MacCallum, R.C. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods 4, 272-299.

Kamalski, D.M., Hoekstra, C.E. & VanZanten, B.G. (2010). Measuring disease-specific health-related quality of life to evaluate treatment outcomes in tinnitus patients: A systematic review. Otolaryngology Head and Neck Surgery 143, 181-185.

Meikle, M.B. (1992). Methods for Evaluation of Tinnitus Relief Procedures. In J.M. Aran & R. Dauman (Eds.) Tinnitus 91: Proceedings of the Fourth International Tinnitus Seminar (pp. 555-562). New York, NY: Kugler Publications.

Meikle, M.B., Henry, J.A., Griest, S.E., Stewart, B.J., Abrams, H.B., McArdle, R., Myers, P.J., Newman, C.W., Sandridge, S., Turk, D.C., Folmer, R.L., Frederick, E.J., House, J.W., Jacobson, G.P., Kinney, S.E., Martin, W.H., Nagler, S.M., Reich, G.E., Searchfield, G., Sweetow, R. & Vernon, J.A. (2012). The Tinnitus Functional Index:  Development of a new clinical measure for chronic, intrusive tinnitus. Ear & Hearing 33(2), 153-176.

Moran, L.A., Guyatt, G.H. & Norman, G.R. (2001). Establishing the minimal number of items for a responsive, valid, health-related quality of life instrument. Journal of Clinical Epidemiology 54, 571-579.

Newman, C.W., Jacobson, G.P. & Spitzer, J.B. (1996). Development of the Tinnitus Handicap Inventory. Archives of Otolaryngology Head and Neck Surgery 122, 143-148.

Stouffer, J.L. & Tyler, R. (1990). Characterization of tinnitus by tinnitus patients. Journal of Speech and Hearing Disorders 55, 439-453.

Tyler, R. & Baker, L.J. (1983). Difficulties experienced by tinnitus sufferers. Journal of Speech and Hearing Disorders 48, 150-154.

The Top 5 Audiology Research Articles from 2012

2012 was an impressive year for scientific publication in audiology research and hearing aids. Narrowing the selection to 15 or 20 articles was far easier than selecting 5 top contenders. After some thought and discussion, here is our selection of the top 5 articles published in 2012.


1. Implications of high-frequency cochlear dead regions for fitting hearing aids to adults with mild to moderately severe hearing loss

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, 573-587.

This article is the second in a series that investigated relationships between cochlear dead regions and benefits received from hearing aids. A sample of patients, diagnosed with high-frequency cochlear dead regions, demonstrated superior outcomes when prescribed hearing aids with a broadband response; as compared to a response that limited audibility at 1,000 Hz. These findings clearly illustrate that patients with cochlear dead regions benefit from—and prefer—amplification at frequencies similar to those with diagnosed cochlear dead regions.

http://journals.lww.com/ear-hearing/Abstract/2012/09000/Implications_of_High_Frequency_Cochlear_Dead.2.aspx

2. The speech intelligibility index and the pure-tone average as predictors of lexical ability in children fit with hearing aids

Stiles, D.J., Bentler, R.A., & McGregor, K.K. (2012). The speech intelligibility index and the pure-tone average as predictors of lexical ability in children fit with hearing aids. Journal of Speech Language and Hearing Research, 55, 764-778.

The pure-tone threshold is the most commonly referenced diagnostic information when counseling families of children with hearing loss. This study compared the predictive value of pure-tone thresholds and the aided speech intelligibility index for a group of children with hearing loss. The aided speech intelligibility index proved to be a stronger predictor of word recognition, word repetition, and vocabulary. These observations suggest that a measure of aided speech intelligibility index is useful tool in hearing aid fitting and family counseling.

http://jslhr.asha.org/cgi/content/abstract/55/3/764

3. NAL-NL2 Empirical Adjustments

Keidser, G., Dillon, H., Carter, L., & O’Brien, A. (2012). NAL-NL2 Empirical Adjustments. Trends in Amplification, 16(4), 211-223.

The NAL-NL2 relies on several psychoacoustic models to derive target gains for a given hearing loss. Yet, it is well understood that these models are limiting and do not account for many individual factors. The inclusion of empirical adjustments to the NAL-NL2 highlights several factors that should be considered for prescribing gain to hearing aid users.

http://tia.sagepub.com/content/16/4/211.abstract

4. Initial-fit approach versus verified prescription: Comparing self-perceived hearing aid benefit

Abrams, H.B., Chisolm, T.H., McManus, M., & McArdle, R. (2012). Initial-fit approach versus verified prescription: Comparing self-perceived hearing aid benefit. Journal of the American Academy of Audiology, 23(10), 768-778.

While the outcomes of this study were not surprising, similar data had not been published in the refereed literature. The authors show that patients fit to a prescriptive target (i.e. NAL-NL1) report significantly better outcomes than patients fit to the lower gain targets that are offered in fitting softwares as ‘first-fit’ prescriptions. This study is a testimonial to the importance of counseling patients regarding audibility and the necessity of real-ear measurement to ensure audibility.

http://aaa.publisher.ingentaconnect.com/content/aaa/jaaa/2012/00000023/00000010/art00003

5. Conducting qualitative research in audiology: A tutorial

Knudsen, L.V., Laplante-Levesque, A., Jones, L., Preminger, J.E., Nielsen, C., Lunner, T., Hickson, L., Naylor, G., & Kramer, S.E. (2012). Conducting qualitative research in audiology: A tutorial. International Journal of Audiology, 51, 83-92.

A substantive majority of the audiologic research literature reports on quantitative data, discussing group outcomes and average trends. The challenges faced in capturing individual differences and clearly documenting field experiences require a different approach to data collection and analysis. Qualitative analysis leverages data from transcribed interviews or subjective reports to probe these anecdotal reports. This tutorial paper described methods for qualitative analysis and cites existing studies that have used these analyses.

http://informahealthcare.com.ezproxylocal.library.nova.edu/doi/abs/10.3109/14992027.2011.606283

The Tinnitus Handicap Inventory (THI): A quick and reliable method for measuring tinnitus outcomes

Newman, C.W., Sandridge, S.A. & Jacobson, G.P. (1998). Psychometric adequacy of the Tinnitus Handicap Inventory (THI) for evaluating treatment outcome. Journal of the American Academy of Audiology 9, 153-160.

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

Tinnitus affects approximately 40-50 million people in the United States and an estimated 10-12 million people seek treatment for it (ATA, 2011; AAA, 2000). Though tinnitus has many potential causes, it often coincides with sensorineural hearing loss. In some cases medical or surgical treatment may be an option, but more often than not an individual with hearing loss and tinnitus will seek hearing aids. Therefore, clinical audiologists frequently encounter patients who suffer from tinnitus.

Because of the potentially disruptive effects of tinnitus on a patient’s ability to function and their sense of well-being, it is important for audiologists to include some estimation of tinnitus handicap in their overall clinical evaluation. Comprehensive diagnostic testing, including tinnitus pitch and loudness matching, should be supplemented with tinnitus self-report measures.  Self-report questionnaires elucidate the effect that the tinnitus has on the individual’s daily life. For instance, tinnitus can disrupt sleep and the ability to concentrate at work or in social interactions and can cause depression, irritability, frustration, stress and feelings of helplessness (Kochkin & Tyler, 1990). Examination of the emotional and social impact of tinnitus and how much it disturbs an individual’s daily activities is essential for determining the course of treatment.

There are a number of potential treatment approaches for tinnitus, including but not limited to: hearing aids, tinnitus maskers, combination hearing aid/masking devices, tinnitus retraining therapy, cognitive therapy, psychological counseling and stress management. Because any of these approaches may succeed with some patients and not others, it is essential to tailor the tinnitus rehabilitation program to each individual and to measure the efficacy of treatment to determine when a change in strategy is indicated.  Though a number of tinnitus questionnaires exist, many of them are limited in scope, difficult to score and interpret, or lack data to support their reliability and validity (Tyler, 1993). Tinnitus handicap questionnaires that are broad in scope and easy to administer and interpret are beneficial because clinicians are often working under time constraints. Test-retest reliability is particularly important if tinnitus self-report questionnaires are to be used to measure treatment outcomes.

The Tinnitus Handicap Inventory was developed as a brief, easily administered way to evaluate the disabling consequences of tinnitus (THI; Newman et al., 1996). It has potential for use in an initial evaluation of handicap or later as well as a way to measure treatment outcome. In the paper discussed here, Newman, Sandridge and Jacobson measured the test-retest reliability and repeatability of the THI, then used their findings to develop categories for the severity of perceived tinnitus handicap.

The THI is a 25-item questionnaire with items that are grouped into three subscales: functional, emotional and catastrophic responses.  The functional subscale items reflect the effect of tinnitus on mental, social, occupational and physical functioning. The emotional subscale items probe the individual’s emotional reactions to the tinnitus and the catastrophic response items address whether tinnitus makes the respondent feel desperate, trapped, hopeless or out of control.  A “yes” response is given 4 points, a “sometimes” response is given 2 points and a “no” response is given 0 points. The questionnaire yields scores for each subscale and a total score that ranges from 0 and 100, with high scores indicating a greater handicap.

Twenty-nine adult subjects, ranging in age from 23 to 87 years old, participated in the study. Subjects were patients at two outpatient Audiology clinics. All subjects presented with tinnitus as their primary complaint and most had gradually sloping, high-frequency, sensorineural hearing losses. The mean length of time that patients reported having tinnitus was 6 years and the mean length of time they had been “bothered” by the tinnitus was 3 years. Eleven participants reported unilateral tinnitus, whereas 18 reported bilateral tinnitus.  The participants reported, on average, that their tinnitus was present 90% of the time during waking hours.

Subjects completed the THI and a tinnitus case history questionnaire (modified from Stouffer and Tyler, 1990) following the scheduling of their initial appointment.  These forms were returned by mail prior to the visit. The second administration of the THI took place approximately 20 days later. This investigation was intended to measure test-retest reliability, which is the magnitude of agreement between two scores when the interval between them is short. The authors cited three reasons for this time frame. First, because many of the subjects were distressed by their tinnitus, they needed to be clinically evaluated and treated as soon as possible. Second, because tinnitus can fluctuate they wanted patients to make all of their judgments within a limited window of time. Third, the interval between initial clinical assessment and evaluation of treatment is often short. For instance, evaluation of the benefit of a tinnitus masker or hearing aid must be completed within the 30-day or 45-day trial period and one goal of the study was to assess the clinical value of the THI.

Results showed that the mean scores and standard deviations were comparable between the two THI administrations. Participants also maintained their relative standing on total and subscale scores from initial test to retest, as indicated by correlations ranging from .84 to .94. Repeatability was measured via calculation of difference scores and plots of their deviation from a difference score of zero. The THI was deemed to have acceptable repeatability because 95% of the difference scores fell within +/- 2 standard deviations from zero.  The repeatability measures allowed the investigators to determine how much of a difference in score would indicate a true difference in status for an individual patient. They found that the total THI scores on two separate administrations would have to differ by at least 20 points in order to be considered a true change. In other words, a clinician using the THI as a tool to measure treatment efficacy would have to see a decrease of at least 20 points to consider the treatment to be successful.

Following these analyses, quartiles were calculated from the mean total THI scores in order to assign scores to one of four handicap categories. On repeat administrations over time, movement from one category to another would indicate a change in tinnitus handicap status, either related to deterioration in the patient’s condition or an improvement based on treatment. The four handicap categories were as follows:

Quartile           Category                       Total THI Score

1st                   No handicap                       0-16

2nd                  Mild handicap                     18-36

3rd                   Moderate handicap              38-56

4th                   Severe handicap                 58-100

Self-reported scales are already widely used to illuminate a patient’s perceived hearing handicap and as a method of evaluating hearing aid fitting outcome or other aural rehabilitation efforts.  One of the primary goals of Newman, Sandridge and Jacobson’s study was to determine if the THI could be used as a clinical tool to evaluate tinnitus treatment outcomes. The reliability and repeatability of the THI suggests that it could be used in this way and it is a straightforward scale that is easy to administer and score. The authors suggest that the THI could be combined with other 25-item scales like the Hearing Handicap Inventory for Adults (HHIA, Newman et al., 1990) or Hearing Handicap Inventory for the Elderly (HHIE, Ventry & Weinstein, 1982) and the Dizziness Handicap Inventory (DHI, Jacobson & Newman, 1990) as a self-report inventory battery to evaluate initial handicap and efficacy of audiological and otological rehabilitation efforts. 

Tyler and Kochkin (1990) reported that 60% of tinnitus sufferers report benefit from the use of hearing aids and that 88% of hearing care professionals treat tinnitus with hearing aids.  Surr, et al. (1999) administered the THI before and six weeks after hearing aid fitting and reported that 90% of their participants demonstrated a significant reduction in THI scores. Because of the co-occurrence of tinnitus and sensorineural hearing loss, clinical audiologists frequently encounter tinnitus sufferers and may often be the first or only health professional to discuss tinnitus management options with the patient. It is important for audiologists to be familiar with tinnitus etiologies, evaluation techniques, treatment options and efficacy measures so they can provide proper guidance to their patients. Clinical appointments are often subject to time constraints, but the clinician is accountable for treatment outcomes, so brief but robust self-report inventories like the THI can be valuable clinical tools.

References

American Academy of Audiology (2000).  Audiologic guidelines for the evaluation and management of tinnitus.  AAA website, http://www.audiology.org/resources/documentlibrary/Pages/TinnitusGuidelines.aspx.

American Tinnitus Association (2011). As cited in Beck, D., Hearing aid amplification and tinnitus: 2011 overview. Hearing Journal 64 (6), 12-13.

Jacobson, G.P. & Newman, C.W. (1990). The development of the Dizziness Handicap Inventory. Archives of Otolaryngology Head and Neck Surgery 116, 424-427.

Kochkin, S. & Tyler, R.S. (2008). Tinnitus treatment and the effectiveness of hearing aids – hearing care professional perceptions. Hearing Review 15(13), 14-18.

Newman, C.W., Weinstein, B.E., Jacobson, G.P & Hug, G.A. (1990). The Hearing Handicap Inventory for Adults: psychometric adequacy and audiometric correlates. Ear and Hearing 11, 176-180.

Newman, C.W., Jacobson, G.P. & Spitzer, J.B. (1996). Development of the Tinnitus Handicap Inventory. Archives of Otolaryngology Head and Neck Surgery 122, 143-148.

Newman, C.W., Sandridge, S.A. & Jacobson, G.P. (1998). Psychometric adequacy of the Tinnitus Handicap Inventory (THI) for evaluating treatment outcome. Journal of the American Academy of Audiology 9, 153-160.

Stouffer, J.L. & Tyler, R.S. (1990). Characterization of tinnitus by tinnitus patients. Journal of Speech and Hearing Disorders 55, 439-453.

Surr, R.K., Kolb, J.A., Cord, M.T. & Garrus, N.P. (1999). Tinnitus handicap inventory (THI) as a hearing aid outcome measure. Journal of the American Academy of Audiology 10(9), 489-495.

Tyler, R.S. (1993). Tinnitus disability and handicap questionnaires. Seminars in Hearing 14, 377-384.

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