Starkey Research & Clinical Blog

On the Topic of Hearing Loss and Fatigue

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

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

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

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

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

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

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

A presentation summarizing the POMS can be found here

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

The MDFS in long and short form can be found here

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

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

A version of the HHIA can be found here

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

3.              Conversation on the phone

4.              Speech listening – live talker

5.              Speech listening – media

6.              Little or no conversation

There were five environment categories:

1.              Outdoors – traffic

2.              Outdoors – other than traffic

3.              Home – 10 people or fewer

4.              Indoors other than home – 10 people or fewer

5.              Crowd of people (more than 11 people)

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

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

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

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

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

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

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

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

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

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

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

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

 

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Evidence for the Value of Real-Ear Measurement

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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