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.

References

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.

References

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

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

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

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

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

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

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

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

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

On the Topic of Hearing Loss and Fatigue

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

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

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

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

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

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

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

A presentation summarizing the POMS can be found here

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

The MDFS in long and short form can be found here

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

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

A version of the HHIA can be found here

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

 

References

Cacioppo, J., Ernst, J., Burleson, M., McClintock, M., Malarkey, W., Hawkley, L. & Berntson, G. (2000). Lonely traits and concomitant physiological processes: the MacArthur social neuroscience studies. International Journal of Psychophysiology 35, 143-154.

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.

 

References

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

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

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

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

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

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

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

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

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

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

Listening gets more effortful in your forties

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

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

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

A Digital Finger on a Warm Pulse: Wearables and the future of healthcare

 

Taken together, blood pressure, glucose and oxygenation levels, sympathetic neural activity (stress levels), skin temperature, level of exertion and geo-location provide a very informative, in-the-moment picture of physiological status and activity. All provided today by a clinical grade smart monitors used in medical research projects around the world. Subtle changes in patterns over time can provide very early warnings of many disease and dysfunctional states (see this article in the Journal Artificial Intelligence in Medicine).

It is well established that clinical outcomes are highly correlated with timely diagnosis and efficient differential diagnosis. In the not too distant future your guardian angel is a medic-AI using machine learning to individualize your precise clinical norms matched against an ever-evolving library of norms harvested from the Cloud. You never get to your first cardiac event because you take the advice of your medic-AI and make subtle (and therefore very easy) modifications to your diet and activity patterns through your life. If things do go wrong, then the paramedics arrive well before your symptoms! The improvements in quality of life and the savings in medical costs are (almost) incalculable. This is such a hot topic in research at the moment that Nature had a recent special news feature on wearable electronics.

There are, however, more direct ways in which your medic-AI can help manage your physiological status. Many chronic conditions today are dealt with using embedded drug delivery systems, but they need to be coupled with periodic hospital visits for blood tests and status examinations. Wirelessly connecting your embedded health management system (which includes an array of advanced sensors) to your medic-AI avoids all that. And in fact, the health management system can be designed to ensure that a wide range of physiological parameter remain within their normal ranges despite the occasional healthy living lapse of its host.

For me as a neuroscientist, the most exciting developments in the areas of sensor technology are in the ambulatory measurement of brain activity. Recent work in a number of research laboratories have used different ways to measure the brain activity of people listening to multiple talkers in conversations, not unlike the cocktail party scenario. What they have found is nothing short of amazing. Using relatively simple EEG recordings with scalp electrodes and the audio streams of the concurrent talkers together with rather sophisticated machine learning and decoding, these systems are able to detect which talker the listener is attending to. Some research indicates that not only the person but the spatial location can be decoded from the EEG signal and that this process is quite resistant to acoustic clutter in the environment.

This is a very profound finding as it shows how we can follow the intention of the listener in terms of how they are directing their attention and how this varies over time. This provides important information that we can use to direct the signal processing produced by the hearing aid to focus on the spatial location of the listeners and to enhance the information being processed that the listener wants to hear – effectively defining for us what is signal and what is noise when the environment is full of talkers of which only one is of interest at any particular instance in time.

Other very recent work has been demonstrating just how few EEG electrodes are needed to get robust signals for decoding once the researchers know what to look for. Furthermore, the recordings systems themselves are now sufficiently miniaturized so that these experiments can now be performed outside the laboratory while the listeners are actually engaged in real-world listening activities. One group of researchers at Oxford University actually have their listeners cycling around the campus while doing the experiments!

These developments demonstrate that the bio-sensors necessary are sufficiently mature in principal to cognitively control signal processing to produce targeted hearing enhancement. This scenario also provides a wonderful example of how the hearing instrument can share the processing load depending on the time constraints of the processing. The decoding of the EEG signals will require significant processing but this processing is not time dependent – a few 100 ms is neither here nor there – a syllable or two in the conversation. The obvious solution is that the Cloud takes the processing load and then sends the appropriate control codes back to the hearing aid either directly or via its paired smartphone. As the smartphone is also listening into the same auditory scene as the hearing aid, it can also provide another access point for sound data that could also provide additional and timelier processing capability for other more time critical elements.

But no one is going to walk around wearing an EEG cap with wires and electrodes connected to their hearing aid. A lot of sophisticated industrial design goes into a hearing aid but integrating such a set of peripheral so that they are acceptable to wear outside the laboratory could well defeat the most talented designers. So how do we take the necessary technology and incorporate it into a socially, friendly and acceptable design? We start by examining developments in the world-wide trend of wearables and examine some mid-term technologies that could well play into the market as artistic and symbols of status.

 

Sources:

 

 

Ubiquitous computing a.k.a. The Internet of Things

In The Fabric of Tomorrow, I spoke briefly about Mark Weiser’s influential article in Scientific America where he coined the term “ubiquitous computing.” As with many great ideas, this has a long and illustrious lineage and indeed has continued to evolve. Alan Turing wanted his computers to communicate with each other as well as humans (1950); Marshall McLuhan (1964) identified electric media as the means by which “all previous technologies – including cities – will be transformed into information systems” and in 1966 the computing pioneer Karl Steinbuch declared that “In a few decades time computers will be interwoven into almost every industrial product”. Of course, things really got going when DARPA invested in ARPAnet (1969) and TCP/IP was implemented in the early 1970’s (see http://postscapes.com/internet-of-things-history for a great timeline).

Weiser points out that “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” This disappearing act demonstrates how technology has seamlessly become an essential part of our everyday lives. Devices have become part of the process of engaging in particular activities. We would miss them if they were gone – as anyone knows when they are separated from their smartphone! But when present, they are invisible.

Mark Weiser’s particular goals at Xerox’s Palo Alto Research Center (PARC) were about augmenting human interaction and problem solving, and he conceived three classes of smart devices. (i) Tabs – wearable (inch) sized devices such smart badges etc; (ii) Pads hand held devices (feet) the size of a writing pad  and (iii) Yard sized devices for interaction and display (e.g. smart boards). These are all macro devices and since his initial ideas others have incorporated device classes on sub millimetre scales. These include (iv) Dust– mm and sub mm sized micro-electro mechanical systems (MEMS) and Smart Dust which are minute wirelessly enabled sensors; (v) Skin – fabrics based on light emitting polymers and flexible organic devices such as OLED displays and (vi) Clay – ensembles of MEMS devices that can form configurable three dimensional shapes and act as so called tangible interfaces that people can interact with (see https://en.wikipedia.org/wiki/Hiroshi_Ishii_(computer_scientist)). Critically, these latter classes of devices usher in new ways of thinking about the interactions between devices and users and the environment. The early thinking was a straightforward reflection of the existing tools for interaction and collaboration, but the latter classes take this thinking down paths untraveled – no doubt some will be blind alleys but others could add motifs and methods that have yet to be conceived.

The term “Internet of Things” (IoT) has been attributed to Kevin Ashton (1999) who had been looking at the ways in which RFID tags, together with the Internet, could be used for advanced supply chain management. Here we see the focus on the sensor and the identity of that which is sensed: This begins to fill out our analogy of the peripheral nervous system of the Cloud. But more importantly, is also begins to inform how we might exploit these ideas in the development of the next generation of hearing technologies. For instance, in Starkey Research we have a project that combines the listening and analytical capabilities of a smart phone to analyse a particular acoustic environment and also to record via Bluetooth, the hearing aid settings selected by the user in that environment. By uploading that information to the Cloud, we can then “crowd source” user preferences for different environment classifications thereby enabling better adaptive pre-sets and controls.

The wireless connection of the smart phone to the hearing instrument is only the first step along the road enabled by the IoT. The hearing aid is connected not just to the phone but to anything the phone can connect too, including the Cloud. In the example above, the hearing instrument off-loads the processing around the environmental classification to the phone, which in turn uploads the data to the Cloud. It is the offline analysis of the data amassed from a wide range of users that provides the associations between environments and the settings, that is, the knowledge that can then inform our designs. On the other hand, there is no reason, in principle, why the data in the Cloud can’t also be used to modify, on the fly, the processing in the hearing instrument.

The point is that, under the new paradigm, the hearing aid is no longer an isolated instrument that is set and forget. It can be updated and modified on the fly using machine level or human interaction or a combination of the two. The user, the health professional, the manufacturer, the crowd can all be leveraged to increase the performance of the instrument. The instrument itself becomes a source of data that can be used to optimize its own operation, or in aggregate with the crowd, the operation of classes of instruments.

The capacity to optimize the instrument at the level of the individual will be dependent, in part, on the nature and quality of the data it can provide.

Read Informed Dreaming here.

Read The Fabric of Tomorrow here.

Read The Power of the Cloud here.

The Power of the Cloud

 

In “The Fabric of Tomorrow,” I laid out a rather high level road map for the ensuing discussion. Now it is time to start digging a bit more into the details and more importantly, understanding how these developments can be leveraged effectively by what we do at Starkey Research.

Let’s start with the Cloud! First the inputs: Ubiquitous computing and seamless interconnectivity are like the peripheral nervous system to the Cloud. Through them, the Cloud receives all its sensory data – the “afferent” information about the world. Data that covers so many more realms than that of the human senses and with a precision and rate that eclipses the sum of all information in previous human history.

Second the outputs: This peripheral nervous system also takes the “efferent” signal from the Cloud to the machines and the displays that will effect the changes in the world – the physical, the intellectual and the emotional worlds we inhabit. We will come back to the peripheral nervous system and its sensors and effectors later – for the moment let’s focus on the Cloud.

People’s expectations and predictions about technology are replete with fails seen in predictions like:

“I think there is a world market for maybe five computers.” — Thomas Watson, chairman of IBM, 1943

“There is no reason anyone would want a computer in their home.” — Ken Olson, president, chairman and founder of Digital Equipment Corp., 1977.

“640K ought to be enough for anybody.” — Attributed to Bill Gates, 1981.

The future is indeed, very hard to foresee. On the other hand, for what we do in Starkey Research, we need to temper our enthusiasm or optimism to properly position our work to deliver in 5 or 10 years time into the real and not the imagined. In contrast to the unbridled excitement of Ray Kerzwiel’s visions of the future, in Starkey Research we have to build and deliver real things that solve real problems!

So with those cautions in mind, what can we say about the Cloud? Electronics Magazine solicited an article from Gordon Moore in 1965 where he made the observation and prediction that the number of components on an integrated circuit board would continue to double each year for at least the next 10 years (he later revised the doubling period to two years). Dubbed by Carver Mead as “Moore’s law”, this came to represent not just a prediction about the capacity of chip foundries and lithographers to miniaturise circuits but a general rubric for improvements in computing power (i.e. Moore’s Law V2.0 & V3.0).

The Cloud, while still based on the chips described by Moore’s law, presents as a virtually unlimited source of practical computing power. The single entity computational behemoths will likely live on in the high security compounds of the world’s defense and research agencies, but for the rest of us, server farms provided by Amazon (AWS), Google (GCE), Windows (Azure) and the like can provide a virtually unlimited source of processing power. No longer are we tied to the capacity of the platform we are using. As long as that platform can connect to the Cloud then the device can share its processing needs with this highly scalable service.

But this comes at a price and that price is time. Although fast, network communications have delays that relate to the switching and routing of the message packets, the request itself is queued and the processing itself takes a finite interval of time before the results are sent back along the network to the requesting device.  At the moment, with a fast network and a modest processing request, the time taken amounts to about the time it takes to blink (~350 ms). For hearing technology this is a very important limitation as the ear is exquisitely sensitive to changes over time. For instance, when sounds are taken in through the ear, there is a delay between processing and comprehension, a delay that can detrimentally influence not only how the sound is interpreted but also a person’s ability to successfully maintain a conversation. This means that we need to find ways to locally processes those elements that are time sensitive and to off-load those processes where a hundred milliseconds or so are not important.

Of course the Cloud is more than just processing power, it also represents information – or more correctly data. Estimating, let alone comprehending, the amount of data currently transmitted across this peripheral nervous system and potentially stored in the Cloud is no mean feat. It requires the use of numbers that are powers of 1000 (terabyte 10004; petabyte 10005; exabyte 10006; zettabyte 10007 and so on). An estimate of traffic can be derived from Cisco’s published forecast figures in 2013 for 2012–17 which indicate that the annual global IP traffic will pass the zettabyte threshold by the end of 2016 and by 2017 global mobile data traffic will reach 134 exabytes annually; growing 13-fold from 2012 to 2017. As for storage, estimate place Google’s current storage at between 10-15 exabytes and Google is but one of the players here – it would be very difficult to determine, for instance, the storage capability of the NSA and other worldwide governmental agencies.

Of course these numbers are mindboggling and there is a point where the actual numbers really don’t add anything more to the conversation. This is just Big Data! What these imply however, is that a whole new range of technologies and tools need to be developed to be able to manipulate these data to derive information. Big Data and Informatics in general have huge implications for the way we conceive how we manage hearing impairment and deliver technologies to support listening in adverse environments.

 

 

 

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.

References

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.

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