Starkey Research & Clinical Blog

On the importance of data logging: hearing aid wearers over-report daily use

How reliable are patients’ estimates of their daily hearing aid use?

Solheim, J. & Hickson L. (2017). Hearing aid use in the elderly as measured by data-logging and self-report. International Journal of Audiology, 56, 472-479.

When the 3M MemoryMate hearing aid was introduced in 1987, it was the first hearing aid to measure and record the number of hours the hearing aid was worn in various memories (data-logging). Despite the fact that today’s instruments are capable of capturing a greater, more extensive array of factual information, many clinicians especially value and rely on the information about duration of usage, as it provides hard data that can help in counseling and adjustment to amplification (Bertoli, et al., 2009; Gaffney, 2008; LaPlante-Levesque, Nielsen, Jensen, & Naylor, 2014; McMillan, Durai, & Searchfield, 2017; Stark & Hickson, 2004).

There are two ways to measure how many hours a hearing aid is being used daily: subjectively, in which the patient is asked to estimate the hours the hearing aids have been worn, and objectively, in which the actual hours of usage are recorded by data-logging within the hearing aids.  Studies that have compared self-reported usage to actual usage determined by data-logging have found a systematic discrepancy; the self-reported hours routinely exceed the objective measurements (Gaffney, 2008; Humes, Halling, & Coughlin, 1996; Laplante-Levesque, Nielsen, Jensen, & Naylor, 2014; Maki-Torkko, Sorr, & Laukli, 2001; Taubman, Palmer, Durrant, & Pratt, 1999). Patients consistently report they have worn the hearing aids for longer periods than the data-logging reveals.

The goals of this study were two-fold.  The first goal was to collect hearing aid use data via objective and subjective means for a group of patients over 60 years of age during the first six months following the hearing aid fitting.    The second goal was to evaluate whether or not patient knowledge of a six-month follow-up appointment affected hearing aid use.  To accomplish this second goal, the patients were randomly divided into two study groups: an intervention group in which patients were given an appointment for a six-month follow-up at the time of the hearing aid fitting, and a control group in which no follow-up appointment was discussed.  All patients received the same sequence of fitting procedures, including clearance by a physician, a comprehensive audiological workup, the hearing aid fitting, and a one-month trial before final acceptance into the study. After six-months, both groups were contacted and informed that their hearing aid usage would be documented during the up-coming appointment, but the methodology for collecting this information was not revealed. Subjective data was collected by asking the patients a simple question: What would you estimate your hearing aid use in hours a day for the last six months to be?

The researchers included 93 patients in the intervention group and 88 patients in the control group. The mean age for the entire cohort was 79.2 years, and slightly more than half were women. The average hearing threshold for the better ear was 49.4 dB; 86.2% of participants were fitted bilaterally, and 55.2% were experienced hearing aid users.

The results were reported two ways. First, for all subjects in the study, including those who did not wear the hearing aids at all, the average usage of hearing aids as recorded by data-logging was 6.12 hours per day, while self-reported daily usage was significantly higher, 8.39 hours per day, an approximate difference of two hours. Second, the average usage for subjects who wore the aids at least 30 minutes each day was 7.24 hours daily as recorded by data-logging, while self-reported daily usage was significantly higher, 9.58 hours per day, an approximate difference of two hours. This study, in agreement with the others mentioned above, clearly indicates that there is a substantial, systematic discrepancy between patients’ estimates and the information provided by data-logging.  These studies revealed that patients tend to overestimate hearing aid use by approximately one to four hours.

As expected, the authors identified several factors that predicted increased hearing aid usage: more severe hearing loss, prior hearing aid experience, and increasing age. Gender and number and type of hearing aid(s) worn were unrelated to usage. Regression analyses indicated that the degree of hearing loss was the strongest predictor of hearing aid use whether measured by self-report or by data-logging.

Finally, advance knowledge of the six-month follow-up appointment had no impact; both the intervention and the control group had similar follow-up appointment attendance rates and similar data-logged and self-reported hearing aid usage. The follow-up attendance rate was approximately 75%; based on this, the authors conclude that patients perceive value in attending a follow-up visit.


Bertoli, S., Staehlin, K., Zemp, E., Schindler, C., Bodmer, D., & Probst, R. (2009). Survey on hearing aid use and satisfaction in Switzerland and their determinants. International Journal of Audiology, 48, 183-195.

Gaffney, P. (2008). Reported hearing aid use versus data-logging in a VA population. Hearing Review, 6.

Humes, L.E., Halling, D., & Coughlin, M. (1996). Reliability and stability of various hearing- aid outcome measures in a group of elderly hearing-aid wearers. Journal of Speech and Hearing Research, 39, 923-935.

LaPlante-Levesque, A., Nielsen, C., Jensen, L.D., & Naylor, G. (2014). Patterns of hearing aid usage predict hearing aid use amount (data-logged and self-reported) and over-report. Journal of the American Academy of Audiology, 25, 187-198.

Maki-Torkko, E.M., Sorr, M.J., & Laukli, E. (2001). Objective assessment of hearing aid use. Scandinavian Audiology, 30, 81-82.

McMillan, A., Durai, M., & Searchfield, G.D. (2017). A survey and clinical evaluation of hearing aid data-logging: A valued but underutilized hearing aid fitting tool. Speech, Language and Hearing, Published Online.

Stark, P. & Hickson, L. (2004). Outcomes of hearing aid fitting for older people with hearing impairment and their significant others. International Journal of Audiology, 43, 309-398.

Taubman, L., Palmer, C.V., Durrant, J.D., & Pratt, S. (1999). Accuracy of hearing aid use time as reported by experienced hearing aid wearers.  Ear and Hearing, 20, 299-305.

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.


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.


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

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.

Hearing Aid Use Decreases Perceived Loneliness

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.

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

Social isolation and loneliness have been linked to increased risk of cognitive decline, cardiovascular disease, increased inflammatory response to stress, depression and other physical and mental health problems (Cacioppo et al., 2000; Hawkley & Cacioppo, 2010; Steptoe et al., 2004).  Estimates suggest that between 10% and 40% of community-dwelling older adults experience social isolation and loneliness, with rural areas having an even higher prevalence of reported loneliness (Nicholson, 2012; Dahlberg & McKee, 2014).

Weinstein and Ventry (1982) were among the first to study the effect of hearing loss on subjective social isolation, finding that self-reported hearing loss was highly correlated with feelings of loneliness and inferiority, reduced interest in leisure activities and withdrawal from others. In a longitudinal study on aging, Pronk and her colleagues found that self-reported hearing loss was associated with increased social and emotional loneliness and they observed that hearing aid users had better scores than non-hearing aid users (Pronk, et al., 2013). These reports raise a number of questions. For instance, if hearing aid use reduces social isolation and loneliness, will associated health problems, such as cognitive decline, be reduced as well? Such an outcome would have widespread implications for the health and well-being of the older population at large.

The goals of the current study by Weinstein and her colleagues were to determine whether first-time hearing aid use reduces social and emotional loneliness. They also examined loneliness in individuals with mild hearing loss and those with moderate to severe hearing loss, before and after intervention with hearing aids, to determine if the effects were dose related.

Forty adults who ranged in age from 62 to 92 years participated in four experimental sessions. At the first session, they completed audiological and speech-in-noise testing, followed by hearing aid selection. Pure tone testing was conducted with standard audiometric procedures and the QuickSIN test (Killion, et al., 2004) was used to evaluate speech recognition in noise. Otoacoustic emission testing was also completed. At the second session, subjects were fitted and trained with binaural hearing aids, real-ear verification measures were conducted and working memory was evaluated with the Reading Span test (Daneman & Carpenter, 1980).  Also at this appointment, the DG Loneliness Scale (DeJong Gierveld & Kamphuis, 1985) was administered, which measures two specific sub-sets of loneliness: emotional loneliness and social loneliness. Subjects returned for a third session one week after the hearing aid fitting and a fourth session at approximately 4-6 weeks after the fitting.

The authors observed a significant decrease in overall loneliness and perceived emotional loneliness after 4-6 weeks of hearing aid use; a reduction in social loneliness that did not achieve statistical significance was also seen.  A sub-group of subjects with more severe hearing loss showed significant decreases in overall loneliness as well as social and emotional loneliness after hearing aid use. This group demonstrated poorer scores pre- and post-fitting, compared to the mild hearing loss group.  There was no significant predictive relationship between age and the measures of social and emotional loneliness and no dose-related effect of hearing loss.  These were not surprising outcomes: health status and functional limitations are more strongly related to social isolation and loneliness than age, and prior studies showed correlations between social isolation/loneliness and perceived hearing loss, as opposed to audiometric thresholds (Hornsby & Kipp, 2016; Pronk et al., 2013).

Subjects were also classified into two groups as “lonely” or “not lonely”, relative to normative data. Prior to hearing aid fitting, 55% were classified as “not lonely” and 45% were classified as “lonely”. After hearing aid use, there was a significant decline in loneliness, with 72.5% of the subjects classified as “not lonely” and 27.5% classified as “lonely”.

The outcomes of this study complement and support our observations as clinical audiologists.  We frequently see the adverse effects of hearing loss on the quality of relationships and social interaction. Hearing loss and subsequent difficulty communicating in groups causes strained conversation and frustration among all participants and increases mental fatigue in the hearing-impaired individual (as reported by Hornsby (2013) and Pronk et al (2013)). This frustration and fatigue often results in avoidance of social interaction.  Therefore, even individuals with a large network of friends and family can experience isolation and loneliness if they struggle to participate in groups or fear that they annoy others with requests for repetition and misinterpretations of conversation.  Most audiologists have heard patients explain that they avoid plays, parties or particular restaurants because they know they will struggle to understand conversation.  Older hearing-impaired adults are even more likely avoid social engagement, because multiple sensory impairments or a decline in cognitive resources may make the use of compensatory strategies like the use of visual cues and context more challenging, thereby increasing frustration and fatigue.

Most clinicians probably discuss social activities and challenges with their new patients in the process of obtaining a detailed initial history. Weinstein and her colleagues suggest that audiologists should also consider implementing a discussion of social network size and a measure of social and emotional loneliness in their evaluation procedures. They suggest the 6-item DG Loneliness Scale, as a brief, yet reliable and valid tool to measure social and emotional loneliness (DeJong Gierveld & Van Tilburg, 2006). It is important to consider both aspects of social activity, as some people with small social networks consider themselves lonely whereas others do not.

Social and emotional loneliness are linked to higher risk of an array of physical and mental health problems, including cognitive decline. Hearing loss is known to increase the risk of social isolation and loneliness, but the results reported by Weinstein and her colleagues suggest that hearing aid use may mitigate this effect, by facilitating more consistent and satisfying social engagement. More study of the potential social and emotional benefits of hearing aid use is needed, especially with regard to how it may reduce the risk of cognitive decline in older adults, by way of a reduction in social and emotional loneliness.



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Dahlberg, L. & McKee, K. (2014). Correlates of social and emotional loneliness in older people: Evidence from an English community study. Aging and Mental Health 18, 504-514.

Daneman, M. & Carpenter, P. (1980). Individual differences in working memory and reading. Journal of Verbal Learning and Verbal Behavior 19, 450-466.

DeJong Gierveld, J. & Kamphuis, F. (1985). The development of a Rasch-type loneliness scale. Applied Psychological Measurement 9, 289-299.

DeJong Gierveld, J. & Van Tilburg, T. (2006). A 6-item scale for overall, emotional and social loneliness. Research on Aging 28, 582-598.

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

Hawthorne, G. (2008). Perceived social isolation in a community sample: Its prevalence and correlates with aspects of peoples’ lives. Social Psychiatry and Psychiatric Epidemiology 43, 140-150.

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.

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

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., 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.

Nicholson, N. (2012). A review of social isolation. The Journal of Primary Prevention 33, 137-152.

Perlman, D. (1987). Further reflections on the present state of loneliness research. Journal of Social Behavior and Personality 2, 17-26.

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.

Steptoe, A., Owen, N., Kunz-Ebrecht, S. & Brydon, L. (2004). Loneliness and neuroendocrine, cardiovascular and inflammatory stress responses in middle-aged men and women. Psychoneuroendocrinology 29, 593-611.

Weinstein, B. & Ventry, I. (1982). Hearing impairment and social isolation in the elderly. Journal of Speech and Hearing Research 25, 593-99.

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

Considerations for music processing through hearing aids

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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



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

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

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

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

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

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

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

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

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

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

Listening gets more effortful in your forties

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

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

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

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

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

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

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


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.

Another piece in the puzzle of hearing aid use and cognitive decline

Amieva, H., Ouvrard, C., Giulioli, C., Meillon, C., Rullier, L. & Dartigues, J.F. (2015) Self-Reported Hearing Loss, Hearing Aids and Cognitive Decline in Elderly Adults: A 25-Year Study. Journal of the American Geriatric Society 63 (10), 2099-2104.

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

Of individuals age 65 or older 30% will have some hearing loss, among those of age 85 or older this proportion is estimated at 70-90% (Chien & Lin, 2012; Weinstein, 2000). Many individuals with hearing loss go without hearing aids, if causal linkage exists between increased risk of cognitive decline or dementia due to untreated hearing loss the implications are of meaningful concern for a large population of older adults. These factors have motivated a swell of interest in relationships among declining of hearing ability, cognition, and memory for our aging population.

Though the ways in which hearing loss is related to cognition and memory deficits are not fully understood, recent evidence suggests that hearing loss may have a meaningful relationship to increased risk of cognitive decline (Deal et al., Lin et al., 2011; Ohta et al., 1981; Granick, et al., 1976; Lindenberger & Baltes, 1994). Some reports also suggest that treatment of hearing loss with the use of hearing aids may slow the progression of cognitive decline, though more study is needed to support this proposition (Valentijn et al., 2005; Lin et al., 2013; Deal et al., 2014).  It is known, however, that hearing loss increases social isolation in the elderly (McCoy et al., 2005; Tun et al., 2009; Weinstein & Ventry, 1982) and social isolation is in turn linked to increased cognitive decline. Whether hearing loss has a direct or circuitous connection to cognitive decline and whether treating hearing loss can slow the rate of cognitive decline is still in question. The purpose of the study reviewed here was to examine self-reports of hearing loss and compare the rates of cognitive decline, or cognitive trajectories among normal hearing and hearing-impaired subjects, and among those who wear hearing aids and those who do not.

Amieva and colleagues completed an analysis of 3,670 subjects, age 65 or older who were participating in a French longitudinal study of aging and the brain. The study began 25 years ago with an initial neuropsychological evaluation, indices of dependency, depression and social interactions, as well as a brief questionnaire about hearing loss. Subsequent visits took place at 2-3 year intervals after the initial visit and again included tests of cognitive performance and complaints, functional ability and symptoms of depression, as well as questions about social interactions and pharmaceutical use. The Mini-Mental State Examination (MMSE; Folstein et al., 1975) was used as a measure of global cognitive performance. To gauge self-perceived hearing loss, subjects were asked “Do you have hearing trouble?” and were instructed to choose one of 3 responses:

1.  “I do not have hearing trouble.”

2.  “I have trouble following conversation with two or more people talking at the same time or in a noisy background.”

3.  “I have major hearing loss.”

In addition to the inquiry about perceived hearing loss, participants were asked if they had a hearing aid.

Participants were divided into three groups based on perceived hearing loss: 2,394 (65%) subjects reported no hearing trouble, 1139 (31%) reported difficulty in groups or noise and only 137 (4%) reported major hearing loss. To examine the effect of hearing loss on the cognitive trajectories, subjects were divided into only two groups: those without perceived hearing loss and those who reported either moderate or severe hearing loss. Of the 1276 subjects who reported hearing loss, 150 used hearing aids. Of the 150 hearing aid users, 89 had self-reported moderate loss and 61 had self-reported severe loss.

Data analysis was comprised of three statistical models. The first model examined the relationship between hearing loss and cognitive decline. After controlling for age, gender and education, the investigators found that hearing loss was significantly related to lower scores on the MMSE and greater decline in cognitive performance over 25 years. The second statistical model examined the relationships among hearing loss, hearing aid use and cognition. At the baseline appointment, both hearing-impaired groups (moderate and major hearing loss) had lower scores on the MMSE than did the subjects with no reported hearing loss. Over the 25 years following the initial visit, there was a significant difference in the rate of cognitive decline between the group of hearing impaired individuals who did not wear hearing aids and the subjects with no reported hearing loss. In contrast, the individuals who did wear hearing aids showed no difference in cognitive trajectory from normal-hearing subjects.  A third statistical model examined hearing aids, hearing loss and cognition, while controlling for several other variables: comorbidities, dependency, dementia and psychotropic drug use. After these factors were controlled, there was no longer a significant difference between the cognitive trajectories of the sub-groups of hearing impaired subjects.

The current study is in agreement with previous reports of a relationship between hearing loss and increased rates of cognitive decline (Lin, 2011; Lin et al., 2013; Deal et al., 2015).  Of particular note is that the individuals who wore hearing aids had similar rates of cognitive decline to normal hearing individuals and slower trajectories than hearing impaired subjects who did not wear hearing aids; this significant difference based on hearing loss disappeared when other variables including depression were controlled. The authors point out that hearing loss has been associated with depression and social isolation in previous studies (Kiely et al., 2013; Li et al., 2014) and that these factors may be the mediate the relationship between hearing loss and cognitive decline.  In other words, the findings of the current study suggest that there may not be a direct relationship between hearing loss and cognitive decline.

It is important to note is that this study used self-report as the measure of hearing loss and hearing aid use. The self-report technique was likely a less expensive and more logistically feasible option, given the magnitude of the study. Additionally, self-reported hearing loss was only measured at the initial visit, so the subjects’ progression of hearing loss was unknown. With particular relevance to the current discussion, cognitive status may indeed affect a person’s perceived ability to communicate in daily activities, particularly in noise. However, individuals who experience difficulty functioning in noise due to cognitive or memory constraints may or may not have elevated pure tone thresholds. Therefore, the self-report measurement may not represent actual hearing loss but could instead reflect other subject characteristics. If audiometric testing is not done, it is unclear how hearing loss may affect performance on measures of cognition.

The evidence presented by Amieva  adds mild insight to our collective understanding of the relationships between hearing status and cognitive ability. Caution must still be maintained when suggesting that treatment of hearing loss may slow or attenuate cognitive decline. Deeper understanding will require additional longitudinal studies with thorough diagnostic routines and randomized, controlled experimental designs. Thankfully this work is underway at universities and hospitals in the United States and Europe. Some pilot outcomes were reviewed in an earlier blog and are available in the original article.


Amieva, H., Ouvrard, C., Giulioli, C., Meillon, C., Rullier, L. & Dartigues, J.F. (2015) Self-Reported Hearing Loss, Hearing Aids and Cognitive Decline in Elderly Adults: A 25-Year Study. Journal of the American Geriatric Society 63 (10), 2099-2104.

Chien, W. & Lin, F. (2012). Prevalence of hearing aid use among older adults in the United States. Archives of Internal Medicine 172, 292-293.

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.

Desjardins, J. & Doherty, K. (2013). The effect of hearing aid noise reduction on listening effort in hearing-impaired adults. Ear and Hearing 35(6), 600-610.

Ferrite, S., Sousa-SantanaII, V. & Marshall, S. (2011). Validity of self-reported hearing loss in adults: performance of three single questions , Revista de Saúde Pública 45(5), 824-30

Folstein, M., Folstein, S. & McHugh, P. (1975). “Mini Mental State”, a practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research 12, 189-198.

Granick, S., Kleban, M. & Weiss, A. (1976). Relationships between hearing loss and cognition in normally hearing aged persons. Journal of Gerontology 31, 434-440.

Kiely, K., Anstey, K. & Luszcz, A. (2013). Dual sensory loss and depressive symptoms: the importance of hearing, daily functioning and activity engagement. Frontiers in Human Neuroscience 7, 837.

Li, C., Zhang, X. & Hoffman, J. (2014). Hearing impairment associated with depression in U.S. adults. National Health and Nutrition Examination Survey 2005-2010. Journal of the American Medical Association, Otolaryngology, Head and Neck Surgery 140, 293-302.

Lin, F.  (2011). Hearing loss and cognition among older adults in the United States.   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.

Lindenberger, U & Baltes, P. (1994). Sensory functioning and intelligence in old age: a strong connection. Psychology of Aging 9, 339-355.

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.

Ohta, R., Carlin, M. & Harmon, B. (1981). Auditory acuity and performance on the mental status questionnaire in the elderly. Journal of the American Geriatric Society 29, 476-478.

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

Uhlmann, R., Larson, E. & Koepsell, T. (1986). Hearing impairment and cognitive decline in senile dementia of the Alzheimer’s type. Journal of the American Geriatrics Society 34, 207-210.

Uhlmann, R., Larson, E., Rees, T., Koepsell, T. & Duckert, L. (1989). Relationship of hearing impairment to dementia and cognitive dysfunction in older adults. Journal of the American Medical Association 261, 1916-1919.

Valentijn, S., Van Boxtel, M. & Van Hoore, S. (2005). Change in sensory functioning predicts change in cognitive functioning. Results from a 6-year follow-up in the Maastricht Aging Study. Journal of the American Geriatric Society 53, 374-380.

Weinstein, B. & Ventry, I. (1982). Hearing impairment and social isolation in the elderly. Journal of Speech and Hearing Research 25, 593-99.

Modern Remote Microphones Greatly Improve Speech Understanding in Noise

Rodemerk, K. & Galster, J. (2015).  The benefit of remote microphones using four wireless protocols. Journal of the American Academy of Audiology, 1-8.

Wireless hearing aids have made remote microphones more accessible, affordable, and easier to use. As a result, use of these systems has become more common. Most hearing aid developers now offer remote microphones that transmit at different wireless frequencies than the comparatively traditional FM system. Some of these system pair directly with hearing aids via 900MHz or 2.4GHz wireless protocols, whereas others communicate via a receiver boot that is physically attached to the hearing aids, or an intermediate device that is worn around the neck or on the lapel, most of these intermediate act as a relay that receives a Bluetooth audio signal from the remote microphone, translating it to a wireless signal that can be received by the hearing aid. The goal of all of these systems is to provide the benefits of a clean speech input; including the ability to overcome distance, reverberation and noise to provide a consistently high-quality speech signal to the listener.

The purpose of the current study was to compare the performance of four commercially available hearing aid/remote microphone systems and to assess their benefits for hearing aid users.  Sixteen hearing-impaired individuals participated in the study. There were ten females and six males and their mean age was 68.5 years with a range from 52-81 years. All subjects had bilateral, symmetrical, sensorineural hearing loss. Ten participants were experienced hearing aid users and six were non-users, though hearing aid experience was not specifically examined in this study.

For the purposes of the study, participants were fitted with three bilateral sets of hearing aids from three different hearing aid manufacturers, paired with four different remote microphone systems. One set of aids communicated directly with a remote microphone via a 900MHz signal, another set communicated directly with the remote microphone via a 2.4GHz signal. The third pair of aids worked with either an FM remote microphone transmitter and FM receiver boot or a remote microphone used with an intermediate Bluetooth receiver that transmitted information to the hearing aids via a magnetic wireless protocol: this set of hearing aids was used in two of the four remote microphone conditions in this study.

Speech recognition was assessed using the HINT test (Nilsson et al., 1994). The HINT sentences were presented with continuous, 55dB speech-shaped noise, delivered through four speakers surrounding the listener at 45, 135, 225 and 315 degrees. Sentence stimuli were presented at a 0-degree azimuth, at levels that were systematically varied to arrive at the level required to achieve a 50% correct score. Twenty sentences were presented in each listening condition and the order of manufacturer and listening conditions were randomized for each participant. Each listening condition was assessed at two talker-listener distances; with the listener seated 6 feet away from the talker loudspeaker and again at 12 feet away from the loudspeaker.

Speech recognition was assessed under four listening conditions:

1.         Unaided

2.         Hearing aid only – omnidirectional

3.         Remote microphone only (hearing aid microphones off)

4.         Remote microphone plus hearing aid microphones (equal contribution from remote and HA microphones)

For the remote microphone only conditions, all four remote microphone systems yielded speech recognition scores that were 11-15dB better than unaided and hearing aid only conditions. There were no significant differences among the four remote microphone systems. This pattern of results was consistent when the listener was seated six feet and twelve feet from the loudspeaker.

Similar results were found for the remote microphone plus hearing aid conditions, in that all four remote microphone conditions were better than the unaided or hearing aid alone conditions. However, only three of the four hearing aid/remote microphone systems were comparable to each other in this condition: the FM, Bluetooth, and 900MHz models. The 2.4GHz model yielded significantly poorer scores than the other systems when the hearing aid microphone was used in combination with the remote microphone. As in the remote microphone only condition, results for the remote microphone plus hearing aid condition were comparable for the listening distances of 6 feet and 12 feet.

All four of the remote microphone systems evaluated in this study improved speech recognition scores from 6 to 16dB, a range comparable to previous reports of performance with FM systems (Hawkins, 1984; Boothroyd, 2004; Lewis, 2008). These results indicate that hearing aid users who experience difficulty understanding speech in noisy environments could expect benefit from any of the systems that were evaluated in this study.  The talker-listener distances examined here are comparable to those examined in previous studies and represent typical situations in which hearing aid users might listen to other conversational participants in everyday situations.

This study showed that when the hearing aid microphone was turned on, providing equal contribution to the remote microphone, the speech recognition benefit was less than that measured with the remote streaming microphone alone, though there was still a significant improvement over unaided and hearing aid only conditions.  This is in agreement with previous studies that reported decreased FM benefit when the hearing aid microphone level was equal to the FM microphone, as compared to FM alone (Boothroyd & Iglehart, 1998). However, many remote microphones allow the hearing aid microphone level to be adjusted in the software. The optimal hearing aid microphone attenuation for remote microphone use requires further examination and may vary with environment and each patients goals for listening.

This study provides compelling support for the benefits of remote microphone systems and lays the groundwork for further examination of remote microphones and how they interact with hearing aid programming parameters and a variety of acoustic environments. Of clinical note was the fact that the research audiologists supporting data collection quickly learned the importance of counseling for successful use of remote microphones. For instance, it was apparent that many participants expected table top placement of a remote microphone would yield benefits similar to those experienced when the remote microphone was place near the talker’s mouth. This point of confusion was clarified through live demonstration of the remote microphone at the time of fitting, during which they will clearly hear that talker’s voice becomes much quieter as the remote microphone is moved away from the talker’s mouth. The remote microphone can be an extremely useful tool but prescription must be accompanied by sufficient counseling and in-office demonstration time.



Boothroyd, A. (2004). Hearing aid accessories for adults: the remote FM microphone. Ear and Hearing 25 (1), 22-23.

Boothroyd, A. & Iglehart, F. (1998). Experiments with classroom FM amplification. Ear and Hearing 19 (3), 202-217.

Hawkins, D. (1984). Comparisons of speech recognition in noise by mildly-to-moderately hearing-impaired children using hearing aids and FM systems. Journal of Speech and Hearing Disorders 49(4): 409-418.

Lewis, D. (2008). Trends in classroom amplification. Contemporary Issues in Communication Sciences and Disorders 35, 122-132.

Nilsson, M., Soli, S. & Sullivan, J. (1994). Development of the Hearing in Noise Test for the measurement of speech reception thresholds in quiet and in noise. Journal of the Acoustical Society of America 95(2), 1085-1099.

Rodemerk, K. & Galster, J. (2015).  The benefit of remote microphones using four wireless protocols. Journal of the American Academy of Audiology, 1-8.

The Christmas Party Problem: Guest Post from Dr. Simon Carlile

 A version of this blog first appeared as an article in the Australian Audiology Today Christmas edition.

One problem with Christmas parties is that there are so many of them and picking which ones to go to can be difficult. Something to influence your decision (other than the quality of the wine on offer) might be where the party is being held. The downtown club with disco music pounding away might be great if you want to dance the night away but that type of venue is not going to help you develop your network with witty conversation and one-liners. Of course, the real Christmas party challenge, even in less busy environments, is hearing and understanding what others are saying at such gatherings; a problem that is virtually insurmountable for those with even a moderate hearing loss.

The Original “Cocktail Party”

Colin Cherry was the first to coin the phrase “the cocktail party problem,” and it seems appropriate to paraphrase that term in regards to this Christmas issue. While most people reading this article have probably come across this term, not many will have the opportunity to read Cherry’s original paper – and what an interesting read it is! His brief, but very influential paper, “Some experiments on the recognition of speech with one and with two ears” first appeared in the Journal of the Acoustical Society in 1953 and is remarkable for a number of reasons.

First, in coining the term the “cocktail party problem,” the question for Cherry was “How do we recognize what one person is saying when others are speaking at the same time?” Two important ideas can be drawn from this, both of which relate to the fact that the conversational environment of the cocktail party involves multiple talkers rather than just one talker and background noise. The first idea is that some talkers will be conveying information that is of interest and also not of interest, i.e. conversation is a multisource listening challenge where focus must quickly switch between sources. The second idea is that many of the talkers’ voices will be what constitutes noise. This is important because the nature of the background sounds are important in terms of the type of masking needed to enable focusing on the sound of interest and the sorts of processing available to the auditory system to ameliorate that masking (see “A primer on masking” below).

Second, Cherry’s paper is mostly about selective attention in speech understanding, the role of the “statistics of language,” voice characteristics and the costs and time course of switching attention. In the Introduction he makes a very clear distinction between the kinds of perceptions that are studied using simple stimuli, such as clicks or pure tones, and the “acts of recognition and discrimination” that underlie understanding speech in the “cocktail party” environment. Cherry’s paper has been cited nearly 1,200 times, but interestingly enough, the greater proportion of those focused on detecting sounds on a background of other sounds used simple stimuli such as tones against broadband noise or other tones. Hardly the rich and complex stimuli that Cherry was talking about. Of course this was very much the bottom-up, reductionist approach of the physicists and engineers in Bell Labs and elsewhere who had had an immense influence on the development of our thinking about auditory perception, energetic masking in particular (See Box – “A primer on masking” and the discussion of the development of the Articulation Index).

An excellent and almost definitive review of this literature is provided by Adelbert Bronkhorst in 2000: “The Cocktail Party Phenomenon: A Review of Research on Speech Intelligibility in Multiple-Talker Conditions.” The research over that period focused on energetic unmasking. For instance: the head shadow producing a “better ear advantage” by reducing the masker level in the ear furthest from the source, the effects of binaural processing or the effects of the modulation characteristics of speech and other maskers. So, on the one hand, the high citation rate for Cherry’s paper is very surprising because there is very little in the original paper that relates to energetic masking. On the other hand, the appropriation of the term “the cocktail party problem” and the reconfiguring of the research question demonstrates the powerful influence of the bottom-up, physics-engineering approach to thinking about auditory perception. This had become the lens through which much thinking and research was viewed. To be fair though, Bronkhorst does point out in his review that there were some data in the literature involving speech-on-speech masking that were not well explained by energetic masking but that this had not been a particular focus of the research.


Informational Masking

The turn of the century was propitious for hearing science as it marked another turning point in our thinking about this “cocktail party” problem. In 1998, Richard Freyman and colleagues reported that differences in the perceived locations of a target and maskers (as opposed to actual physical differences in location) produced a significant unmasking for speech maskers but not for noise. Such a result was not amenable to a simple bottom-up explanation of energetic masking. Thus, Freyman appropriated the term “information masking” which had been previously used in experiments involving relatively simple stimuli. This was the first time it had been applied to something as complex and rich as speech. As we shall see in more detail later, the unmasking produced in this experiment depended on the active, top-down focus of attention. As previously mentioned, Bronkhorst had pointed out that others had noted speech interference of speech understanding seemed to amount to more than the algebraic sum of the spectral energy. Indeed, as early as 1969, Carhart and colleagues had referred to this as “perceptual masking” or “cognitive interference.” Along those lines, information masking in the context of the perceptual unmasking in Freyman’s and later similar experiments came to stand for everything that wasn’t energetic masking.

Over the ensuing 15 years, many studies have been carried out examining the nature of information masking. A number of general observations can be made and some of these are drawn out in the “Primer” below. One very important shift however, was that the “cocktail party problem” became increasingly seen as a particular case of the general problem of auditory scene analysis (ASA). This is the problem of “acoustic superposition” where the energy from multiple concurrent sounds converges on a single encoder; in this case the cochlea of the inner ear. The first task of the auditory system then, is to work out which spectral components belong to which sound sources and to group them together in some way. The second task is how these now segregated components are joined up in time to provide a stream of information associated with a specific sound.


Auditory Scene Analysis

Albert Bregman did much to promote thinking in this area with the publication of Auditory Scene Analysis in 1992, marking a significant return of Gestalt thinking to the study of auditory perception. Although this part of the story is still being worked out, it is clear that much of the grouping and steaming processes underlying ASA are largely automatic, that is bottom-up, and they capitalize on the physical acoustics of sounding bodies – probably not surprising given that the auditory system evolved in a world of physically sounding bodies and “the cocktail party problem” is a common evolutionary challenge for nearly all terrestrial animals. The perceptual outcome of this process is the emergence of auditory objects that usually correspond to the individual physical sources. Indeed, many of the experimental approaches to understanding ASA involved stimuli which created perceptual objects that were in some way ambiguous and also looking at the illusions and/or confusions that such manipulation creates.

In the case of “the cocktail party problem”, the speech from each talker forms a specific stream and the problem becomes more about how we are able to select between each of the streams. In practical terms, the greater the differences between the talkers on some dimension (pitch, timbre, accent, rhythm, location etc.), the less likely we are to confuse the streams. That is, the greater stream variety, the more information unmasking we can expect.

This brings us to the key role of attention in understanding listening in a “cocktail party” scenario. Attention has been thought of as a type of filter that can be focused on a feature of interest, allowing for an up-regulation of the processing of information within that filter with a potential down-regulation of information outside the filter. A physical difference in some aspect of the auditory stream provides the hook onto which the listener can focus their attention. In recognizing the critical role that attention plays in understanding what is happening in a cocktail party scenario, it does move the discussion from “hearing” to “listening” and closer to Cherry’s goals of understanding the “acts of recognition and discrimination” that underlie the understanding of speech.


Auditory Attention

The neuroscience of auditory attention is in its infancy compared what we know about visual attention, although some tentative generalizations can be made:

Attention is a process of biased competition. The moment to moment focus of attention is dependent on competition between (1) top-down, voluntary or endogenous attentional control and (2) bottom-up, saliency driven or exogenous attention. The cognitive capacity to focus attention plays a key role in the sustained attention necessary to process the stream of information from a particular talker. There is evidence that we listen to only one auditory object at a time and selective attention is critical in enabling this. The exogenous competition introduced by concurrent sounds, particularly other talkers (the distractors) means more cognitive effort is required to sustain attention on a particular target of interest. The implication for an ageing population is that any reduction in cognitive capacity to sustain attention will increase the difficulty of understanding the stream of information from a single talker in the presence of other talkers.

Selective attention works at the level of perceptual objects as opposed to a particular physical dimension such as loudness or pitch. That is, attention focuses on the voice or the location of a particular talker (or both simultaneously – see below). While the attentional hook might be a difference on a particular perceptual dimension, the sum total of characteristics that make up the perceptual object are what becomes enhanced. Models of attention suggest that the competition for attention is played out in working memory and the players are the sensory objects contained in working memory at any particular point in time. Indeed, our conscious perception of the world relies on this process.

What this means, is when auditory objects are not well defined then the application of selective attention can be degraded. There are a number of circumstances where this can happen. For instance, when the stimuli themselves are ambiguous and don’t possess the relevant acoustical elements to support good grouping and streaming. Alternatively, the stimuli themselves may possess the necessary physical characteristics; however, poor encoding at the sensory epithelia and/or degraded neural transmission of the perceptual signal can result in a reduced fidelity or absence of the encoded features necessary for grouping or streaming. Implications for hearing impairment are that degradation of sensory encoding, such as that produced by broader auditory filters (critical bands) or poor temporal resolution, will weaken object formation and make the task of selective attention that much harder.

Attention acts as both a gain control and a gate. There is a growing body of evidence that indicates attention modulates the activity of neurones in the auditory system, not only at a cortical level but even earlier in the signal chain, possibly even at the level of the hair cells of the cochlea. In a number of recent and ground-breaking experiments, this process of up-regulation of the attended talker and down-regulation of the maskers has been convincingly demonstrated in the auditory cortex of people dynamically switching their attention between competing talkers (Mesgarani & Chang, 2012; Ding & Simon, 2013). Importantly, the strength of the selective cortical representation of the “attended-to” talker correlated with the perceptual performance of the listener in understanding the targeted talker over the competing talker.

The auditory system engages two different attentional system – one focused on the spatial location of a source and one focused on non-spatial characteristics of the source – which have two different cortical control systems. In a 2013 study, Adrian “KC” Lee and colleagues (Lee et al, 2013) had listeners change their attentional focus while imaging the brain. They found that the left frontal eye fields (FEF) became active before the onset of a stimulus when subjects were asked to attend to the location of a to-be-heard sound. This is part of the so-called dorsal attention pathway thought to generally support goal-directed attention. On the other hand, when asked to attend to a non-spatial attribute of the stimulus such as the pitch, a different pattern of pre-stimulus activation was observed in the left posterior central sulcus, an area also associated with auditory pitch categorization. This suggests that for the hearing impaired, a loss of the ability to localize the source of a sound disables or degrades a significant component of the auditory attention system resulting in an increased reliance on the non-spatial attention system.

Returning to Colin Cherry’s paper, it appears that we have — to paraphrase T.S. Eliot —“arrived where we started and know the place for the first time.”

So much of what Cherry discussed in his seminal paper is where we now find our neuroscientific focus including: the statistics of language in terms of its phonetic and semantic characteristics; the focus of attention and how that is mediated by spatial location and/or vocal or other characteristics; the transitional probabilities of what is being said and so on. The difference now is that we have both the technical and analytical tools to get a handle on how these processes are represented in the brain. With an increasing understanding of the functional plasticity of the brain, we are at a point now where we are making advances in the understanding of human perception and cognition that will have significant ramifications for how we intervene, support and rehabilitate many of the disorders that manifest as hearing impairment.

Further Reading

Cherry, E.C. (1953). “Some experiments on the recognition of speech with one and with two ears” J Acoust Soc Am, 25:975

Bronkhorst, A. (2000). “The cocktail party phenomenon: A review of research on speech intelligibility in multiple-talker conditions” in Acustica 86:117-128.

Lee, A. K. C., et al. (2012). “Auditory selective attention reveals preparatory activity in different cortical regions for selection based on source location and source pitch.” Frontiers in Neuroscience 6: 190-190.

Mesgarani, N. and Chang, E. F. (2012). “Selective cortical representation of attended speaker in multi-talker speech perception.” Nature 485: 233-236.

Ding, N. and Simon, J. Z. (2012). “Emergence of neural encoding of auditory objects while listening to competing speakers.” Proceedings of the National Academy of Sciences of the United States of America 109: 11854-9.


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.


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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.

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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.

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

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

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