Starkey Research & Clinical Blog

Effective communication behavior during hearing aid appointments

Munoz, K., Ong, C., Borrie, S., Nelson, L., & Twohig, M. (2017). Audiologists’ communication behavior during hearing device management appointments. International Journal of Audiology, Early Online, 1-9.

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

The skill of the audiologist in communicating with a patient can significantly impact rehabilitative outcomes. Nowhere is this more evident than when an audiologist in engaged in managing a hearing device fitting. Studies have suggested a lack of patient-centeredness behavior by audiologists in audiologist-patient interactions, including domination of speaking time, a tendency to overemphasize the technical aspects of device care, interruptions of the patient, an inability to deal with emotion-laden aspects of rehabilitation, expressing empathy, and not actively listening, (e.g., Ekberg, 2014;  Grenness, et al, 2014; Grenness, et al, 2015; Knudsen, et, al., 2010; Laplante-Levesque, et al, 2014; Munoz, et al, 2014, and Munoz, et, al, 2015). The counseling tendencies noted above can create a lack of adherence to and understanding of the recommendations and information provided by the audiologist (Robinson, et al, 2008).

Audiologists in training are likely as not to internalize or imitate how their mentors or supervisors interact with patients. Unless their instructors have themselves achieved satisfactory interpersonal communication skills, audiologists may enter the workforce lacking practical counseling and communication skills that may diminish their effectiveness in the clinical setting.

The authors designed this exploratory, longitudinal study to measure audiologist communication behaviors at three time intervals, first, prior to participating in a one-day pre-training workshop, second, at a two-month interval, and third, at a six-month interval. The pre-training workshop focused on the psychosocial aspects of counseling including the use of open-ended questions, validation of emotions, reframing and clarifying patient problems and complaints, methods for increasing motivation, and double-checking patient assumptions. In addition, five one-hour support sessions were offered to the audiologists for a three-month period following the initial workshop, during which topics were discussed such as addressing client barriers, addressing emotions, being present and non-judgmental, and developing reflection/summarizing skills, among others. Attendance ranged from 30% to 90% of participants; one audiologist attended none of the support sessions, but most attended 3-4 sessions.

Ten audiologists actively providing clinical services were evaluated on two rating scales—1) the Behavior Competencies Rating Scale (a 10-item self-rating measure developed by the authors) designed to evaluate the audiologist’s own perception of his/her communication skills, and 2) a modified version of the Counseling Competencies Scale (Swank, et al, 2012), intended to measure counseling skills and behaviors, graded by both the instructor and independently by a psychology graduate student. 53 patients consented to participate and each audiologist-patient interaction was recorded. A set of coding guidelines was developed to recognize and categorize by type the counselling behaviors (interactions) exhibited by the audiologist, as well as the frequency of each of the counseling behaviors. The coding categories for counseling skills included encouragers, questions, listening and reflecting feelings, confrontations, goal setting, focus of counseling, and expressions of appropriate empathy, care, respect and unconditional positive regard.

The article gave examples of expressions and statements during counseling that would fall into  specific coding categories. For example, an open-ended question such as “What do you think is the most challenging part of wearing (or taking care) of your hearing aids?” would be categorized as assessing and addressing barriers and motivation. An audiologist might comment to a patient who mentions they are in the process of moving, “So you have a lot going on,” which would be interpreted as an instance of listening and reflection.  Or the audiologist might suggest, “For homework, I’d like you to work on using a couple of the strategies we discussed,” a statement that would fall into the category of planning for behavior change.

The average length of each recorded counseling session was 46 minutes, from which a selected ten-minute sample was extracted, coded and subjected to analysis. The rate of change of audiologist behaviors, expressed as the percentage frequency of occurrence per session, was measured at the three time intervals mentioned above, baseline, one-month post-training, and at a six-month follow-up.

The authors found that audiologists devoted the greatest amount of clinical interactions throughout the six-month period to general fitting discussions followed by educational and technical instruction. The frequencies of occurrence (interactions) devoted to these two variables increased slightly post workshop, but thereafter decreased. The fewest number of the clinicians’ interactions per session over the six-month period was spent in listening and reflection, clarifying treatment goals, assessing and addressing motivation and barriers, and discussing behavior changes. Although small changes were noted in the frequencies of occurrence of these behaviors over the study period, the authors concluded that the observed changes were so minimal as not to be practically meaningful. Of interest, they also found the time per session devoted to irrelevant conversation and small talk increased linearly from a relatively low point to a higher level throughout the time of the study.

A striking outcome was the significant reduction in personal speaking time of audiologists following a pre-training workshop. When the speaking time of both patients and audiologists were compared (audiologists dominated during pre-training) both were approximately equal after the workshop. Although speaking time was not explicitly stressed in the workshop, these findings suggest a reduction in audiologist verbal dominance after training, suggesting that the training positively impacted this counseling behavior.

Finally, the audiologists, in rating their personal communication behaviors, perceived a marked improvement in their own communication skills on the self-rating scale. This improvement was not entirely supported by the data, as the observer-rated data showed little clinically important changes in psychologically relevant interactions over the study period.

The authors suggest that one of the reasons for lack of meaningful change in clinician communication behavior might have been the complexity of counseling skills taught within a relatively short time frame. The provision of a short workshop on communication skills is insufficient and that the importance of teaching patient-centered communication skills to audiologists-in-training as early as possible cannot be overstated.

Although there was evidence of improvement in audiologists’ counseling skills following the pre-training workshop and with supplementary instruction, it was limited. Hesitation to address patients’ psychosocial concerns, express empathy when appropriate, and address client’s emotions, indicate a possible gap in training and education. The authors recommend that clinical supervisors should be aware of the critical role patient-centered counselling plays in providing positive clinical outcomes. Further, these supervisors should recognize within themselves the need for improving personal counseling skills by furthering their own continuing education.

References

Ekberg, K., Grenness, C. & Hickson, L. (2014). Addressing patients’ psychosocial concerns regarding hearing aids within audiology appointments for older adults. American Journal of Audiology, 23, 337-350.

Grenness, C., Hickson, L., Laplante-Levesque, A., Meyer. C., & Davidson, B (2014). Communication patterns in audiologic rehabilitation history-taking: audiologists, patients, and their companions. Ear and Hearing, 36, 191-204.

Grenness, C., Hickson, L., Laplante-Levesque, A., Meyer. C., & Davidson, B (2015). The nature of communication throughout diagnosis and management planning in initial audiologic rehabilitation consultations. Journal of American Academy of Audiology, 50, 36-50.

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

Laplante-Levesque, A., Hickson, L., & Grenness, C. (2014). An Australian survey of audiologists’ preference for patient-centeredness. International Journal of Audiology, 53, S76-S82.

Munoz, K., Nelson, L., Blaiser, K., Price, T., & Twohig, M. (2015). Improving support for parents of children with hearing loss: provider training on use of targeted communications.

Munoz, K., Preston, E., & Hickens, S. (2014). Pediatric hearing aid use: how can audiologists support parents to increase consistency. Journal of the American Academy of Audiology, 25, 380-387.

Robinson, J.H., Callister, L.C., Berry, J.A., & Dearing, K.A. (2008). Patient-centered care and adherence: definitions and applications to improve outcomes. Journal of the American Academy of Nurse Practitioners, 20, 600-607

Swank, J.M., Lambie, G.W., & Witta, E. L. (2012). An exploratory investigation of the Counseling Competencies Scale: a measure of counseling skills, dispositions, and behaviors. Counselor Education and Supervision, 51, 189-206.

On the Topic of Hearing Loss and Fatigue

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

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

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

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

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

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

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

A presentation summarizing the POMS can be found here

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

The MDFS in long and short form can be found here

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

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

A version of the HHIA can be found here

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Listening gets more effortful in your forties

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

Does hearing aid use slow cognitive decline?

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Hearing Aid Use is Becoming more Accepted

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

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

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

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

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

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

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

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

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

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

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

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

5. Wearing a Bluetooth ear-level telephone headset

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

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

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

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

 

References

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

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

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

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

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

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

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

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

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

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

 

A Pediatric Prescription for Listening in Noise

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

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

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

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

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

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

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

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

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

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

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

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

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

 

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

 

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