Journal Logo

Online Exclusives

Access online-exclusive articles and updates published ahead of print.

Wednesday, April 28, 2021

By John Eichwald, MA; Padmaja Vempaty, MSW, MPH; and Yulia Carroll, MD, PhD

NOTE: This article is published ahead online. 

The Noise Control Act of 19721 directed the Environmental Protection Agency (EPA) to protect the health and welfare of Americans from unregulated noise and formed the EPA Office of Noise Abatement and Control (ONAC). In 1974, ONAC recommended an equivalent sound exposure level of 70 decibels over a 24-hour period to protect the public from hearing loss.2 At that time, ONAC also recommended levels regarding interference or annoyance of 55 and 45 decibels for outside and inside activities, respectively. In 1982, ONAC was defunded, transferring the primary responsibility of regulating noise to state and local governments. An analysis of 491 U.S. noise ordinances in 20163 revealed most communities used multiple standards to regulate noise exposure including nuisance, zoning, audibility decibel levels, time of day, and distance.


Investigators reviewed and classified 60 existing community noise ordinances. Searches were conducted on local government webpages or via legal code databases. The 10 most populated U.S. cities were analyzed as well as 50 community noise ordinances randomly chosen from across the nation. Ordinances were specifically reviewed to identify 22 key aspects of noise ordinances. These included five key noise control measures: audibility, time of day, decibel level, zoning, and specified quiet zones to protect vulnerable communities (e.g. hospitals, schools). Ordinances were also reviewed for legal language identifying the entity or agency responsible for enforcement and the penalties, if any.


Of the 60 jurisdictions reviewed, 32 (53.3%) were small, 16 (26.7%) were medium, and 12 (20.0%) were large. Sound sources that were specified by law were identified in all but two (96.7%) of the ordinances. Time-of-day restrictions were found in 55 (91.7%). Zoning restrictions were used in 53 (88.3%) jurisdictions. Activities deemed to be noise disturbances were specified in 46 (76.7%) ordinances. Disturbing the peace was identified in 50 (75.0%), nuisance/annoyance in 42 (70.0%). Audibility, decibel level, and quiet zones were included in 37 (61.7%), 35 (58.3%), and 29 (48.3%) of the ordinances, respectively. Restrictions on vehicles were found in 52 (86.7%) and noisy animals in 31 (51.7%) of the ordinances.

Law enforcement, e.g., the police or sheriff, was identified as at least one of the designated authorities in charge of the noise ordinance in 31 of the reviewed ordinances. Officials in charge of codes, inspections, and other types of regulations were identified in 12 (20.0%) of the ordinances. Health agencies were listed as having authority in 10 (16.7%). Noise control authorities were clearly specified in 4 (6.7%) ordinances. Jurisdictional administration, such as the city council, and other administrative offices, e.g., housing, animal control, public safety, had authority in 20 (33.3%). In 14 (23.3%) ordinances, the authority of regulation was not identified or unclear.

Figure 1. Penalties and enforcement identified in community noise ordinances.

Fig 1.jpg

Among the 60 communities, 40 (66.7%) included fines in their ordinances. Civil penalties or infractions were found in 21 (35.0%). Charges of misdemeanor were listed as penalties in 18 (30.0%), and 6 (10.0%) stated violation could result in imprisonment. In 21 (35.0%) jurisdictions, local ordinances specified that infractions constituted a civil violation. As shown in Figure 1, seven communities (11.7%) had no penalty and no enforcement clauses in their noise codes, two (3.3%) had enforcement but no penalties, six (10.0%) had penalties but no enforcement, and 45 had both written enforcement and penalties. Among the ordinances reviewed, communities with enforcement and penalties written into their noise ordinances were mostly in the South and coastal States. It should be noted that community noise ordinance might not have penalties or enforcement if a superseding chapter for penalties and enforcement supplants multiple ordinances in the code. Some of the ordinances reference a superseding chapter, others do not. Because only ordinances with the word “noise" in the title were reviewed, cross-referenced penalties and enforcement were not identified. Figure 2 shows the number of key noise control measures identified within the jurisdiction's ordinance. All five of the measures were identified in 14 (23.3%) communities, 17 (28.7%) communities had four of the five, and 29 (48.3%) had three categories or fewer.

Figure 2. Number of key noise control measures (pink ≤ ​3, red = 4, and maroon = 5). Noise control measures: plainly audible, time of day, decibel levels, zoning, and quiet zones (e.g. hospital or school).

Fig 2.png


Exposure to loud sounds puts millions of people in the United States and across the globe at risk not only of hearing loss, but several highly prevalent health effects including ischemic heart disease, hypertension, injuries, anxiety, sleep disruption, stress, and cognitive impairments.4,5,6 While three of four of the jurisdictions reviewed cited annoyance, nuisance, or disturbance as a primary purpose for the noise control ordinance, only slightly more than half cited health as a primary purpose.

Almost all jurisdictional noise ordinances reviewed included time-of-day restrictions, demonstrating that communities recognize excessive noise at certain hours can be more problematic. Half of the jurisdictions listed the police or sheriff's department as the enforcement authority. Only four communities had a noise control officer, or a specific noise control authority identified. As a result, noise enforcement relegated to the responsibility of police departments may not be prioritized as a violation. Of concern is the finding that nearly one-fourth of noise ordinances did not have an enforcement body identified, although a general enforcement statute may be listed elsewhere in the local code. If a community has a noise ordinance and a disturbing the peace ordinance, it may be easier for law enforcement to cite disturbance of the peace, which is likely more subjective and has less stringent legal requirements.

Although the number of key noise control measures in a jurisdiction reveals the variety of methods used, it is not necessarily a measurement of its effectiveness. A community could potentially include all five of the controls but find them ineffective, confusing, and difficult to enforce. Objective measurements might not be available for noise monitoring, or enforcement officials may not have the necessary training to properly utilize noise measurement equipment. In such situations, enforcement officers might be more likely to cite a more subjective ordinance, such as disturbing the peace. This review did not account for noise regulations included in ordinances related to disturbance of the peace, land use or zoning, and in other parts of the local code. These regulations are not always cross-referenced in the noise ordinance.

To help offset the harmful effects noise may have on health, ordinances can incorporate quiet zones into communities. Quiet zones, or noise-sensitive zones, can be designated in areas that should have a lower threshold for noise, such as areas with hospitals or elderly care homes. Issues such as sleep disturbance affect the elderly and persons with chronic illness.7 Schools and daycare centers should also be in quiet zones, as even moderate traffic noise does not detract from academic performance.8


Because local jurisdictions do not have up-to-date federal noise guidelines to follow, local noise ordinances reviewed in this article are varied in terms of their noise control strategies, enforcement, and penalties. With up-to-date guidelines that consider the health implications of noise and recent noise monitoring technology, jurisdictions might be better informed and could follow a set of common standards. State and local governments might consider using the World Health Organization's Environmental Noise Guidelines for the European Region9 as a framework when crafting their legislation to protect health from exposures of environmental noise.

​ABOUT THE AUTHORS: Mr. Eichwald is an audiologist within the Office of Science at the Centers for Disease Control and Prevention (CDC) National Center for Environmental Health (NCEH). Ms. Vempaty is a Public Health Analyst for Policy and Issues Management in the CDC/NCEH Division for Environmental Health Science and Practice. Dr. Carroll is the Associate Director for Science at the CDC/NCEH Division for Environmental Health Science and Practice.

ACKNOWLEDGMENTS: The authors thank Monica S. Hammer, JD and Les Blomberg, MA for their expert advice on noise ordinances; the in-kind support of the CDC Center for State, Tribal, Local, and Territorial Support, Public Health Law Program; and Mahad Gudal, a Morehouse College IMHOTEP program summer intern.

DISCLOSURE: The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention/the Agency for Toxic Substances and Disease Registry. 


  1. Noise Control Act of 1972, 42 USC. §4901–4918 (1988). Available at: Noise Control Act of 1972, 42 USC. §4901–4918 (1988). Available at: Accessed April 26, 2021.
  2. United States Environmental Protection Agency. Information on levels of environmental noise requisite to protect public health and welfare with an adequate margin of safety. No. 2115. US Government Printing Office. EPA Publication No: 550976003 Published March 1974. Accessed April 26, 2021.
  3. Blomberg, L. Preliminary results of an analysis of 491 community noise ordinances. Paper presented at: NOISE-CON; June 13-15, 2016; Providence, Rhode Island. Accessed April 26, 2021.
  4. World Health Organization. World report on hearing. Published March 2021. Accessed April 26, 2021.
  5. Hammer MS, Swinburn TK, Neitzel RL. Environmental noise pollution in the United States: developing an effective public health response. Environmental health perspectives. 2014;122(2):115-9.
  6. Münzel T, Schmidt FP, Steven S, Herzog J, Daiber A, Sørensen M. Environmental noise and the cardiovascular system. Journal of the American College of Cardiology. 2018;71(6):688-97.
  7. Kim R, Van den Berg M. Summary of night noise guidelines for Europe. Noise and health. 2010;12(47):61-63. doi:
  8. Wilensky J, Winter M. Quiet zones for learning. Human Ecology. 2001;29(1):15.
  9. World Health Organization. Environmental noise guidelines for the European region. Available at: Published 2018. Accessed April 26, 2021. 

Wednesday, April 21, 2021

As automated audiometry becomes more widespread for busy clinics and teleaudiometry, GSI interviewed Dr. Robert Margolis to discuss his automated method for testing auditory sensitivity (AMTAS).

GSI: What motivated you to think of automated audiometry?

Dr. Margolis: When I was the director of the University of Minnesota Hospital Audiology Clinic I was dissatisfied by the amount of time my highly-trained, competent staff was spending doing pure-tone audiometry which occupied more of their time than any other billable activity. Two experiences solidified my belief that this was an inappropriate use of professional time.

I performed a hearing evaluation on a highly-educated professional man who, after watching me through the window of the sound booth, said “Why do you have to push those buttons?" It was obvious to him that the procedure was perfectly amenable to automation. Why was I pushing those buttons when Wayne Rudmose said in 1963: The number of audiometric examinations made today has grown to such a magnitude that it is only natural that some of the techniques of measurement should become automated (Rudmose, 1963).

Not long after that, I had an unpleasant meeting with the hospital director who questioned whether my staff was productive enough. When I pointed out that they typically were in the clinic until 6 and then took reports home to write, she offered, "Maybe you need to automate some of those procedures".

Since that time, I have looked outside the walls of the clinic. In the U.S. the number of hearing tests that can be conducted by all the audiologists is less than half of the need (Margolis & Morgan, 2008). Most countries don't have any audiologists. Increasing access to hearing testing has become my major goal in developing automated tests.

GSI: How does automation help audiologists?

Dr. Margolis: Automated audiometry helps audiologists by allowing more efficient use of their time, increasing the accuracy and repeatability of test results, and standardizing our test methods. Perhaps more importantly, automation helps hearing-impaired people by increasing access to audiology services.

GSI: How can audiologists trust the results?

Dr. Margolis: Over time, audiologists become very good at assessing the quality of their test results and modifying their technique to ensure accuracy. The information they use to determine accuracy can be incorporated into an automated test and then the computer can do it better than a human. We developed a method for assessing the quality of automated test results that statistically estimates the accuracy of the test (Margolis 2007). Computer programs for measuring auditory thresholds have been around since the 1960's. When you take the audiologist out of the procedure, it is not the process of selecting and presenting stimuli that is lost, it is the expertise of the audiologist for ensuring accuracy. Quality control is a critical feature of automated clinical tests.

GSI: How does the audiologist build rapport with the patient if he or she isn't doing the pure tones?

Dr. Margolis: That's a riot. One of my favorite audiologists told me that manual pure tone audiometry is important for building rapport with patients. My experience has been when you put someone in a metal room, close the doors, watch them through a dimly lit window, and ask them to listen to sounds they can barely hear, I don't make any friends. I have to build rapport in other ways.

GSI: Has automated audiometry been evaluated in real-world situations?

Dr. Margolis: Automated audiometry trials have been conducted in a variety of clinics in at least four countries and in the homes of hearing-impaired people. There is a growing literature that establishes the validity of automated tests.

GSI: How was the development of automated audiometry funded?

Dr. Margolis: Some equipment manufacturers developed automated protocols for pure-tone audiometry internally. That kind of development is proprietary and manufacturers usually don't share their validation methods and results. AMTAS® was developed with funding from the U.S. National Institutes of Health and the U.S. Department of Veterans Affairs. Validation methods and results are published in the audiology research literature.

GSI: Can you bill for automated hearing tests?

Dr. Margolis: In 2010 the Centers for Medicare & Medicaid Services (CMS) established Category III CPT codes for automated hearing tests (pure-tone and speech audiometry). Category III codes (sometimes called t-codes) are for emerging technologies. It is legal to bill using Category III codes but they may not be reimbursed. The Category III codes for automated audiometry are scheduled to be sunsetted in 2026. There is an effort underway to convert the Category III codes to Category I codes.

GSI: What is the business case for using automated tests if they are not reimbursed?

Dr. Margolis: Clinics should analyze the costs and revenue associated with their services. With the low level of reimbursement for pure-tone audiometry, and the cost of doctoral-level clinicians, the personnel costs probably exceed the revenue. It is not cost-effective to use doctoral-level practitioners to perform routine tests that can be automated if the reimbursement doesn't exceed the cost of the professional time. That time can be used more profitably for clinical services that require the skills of audiologists.

GSI: What other audiologic tests can be automated.

Dr. Margolis: Some already are. The first clinical instrument for tympanometry required the user to set the ear-canal air pressure manually and acquire the tympanogram point by point. It was quickly automated (without a peep from the audiology community). Several electrophysiologic tests are automated or semi-automated. Automated speech recognition tests have been developed and validated. Speech-recognition thresholds and word recognition scores can be obtained by an automated forced-choice procedure that requires the patient to select the response from a set of alternatives presented visually. The test scores differ from those we obtain with open set tests because chance performance is 1 over the number of alternatives (25% if there are four alternatives). Forced-choice tests have been around for decades and are a perfectly legitimate way to measure speech recognition ability but require different interpretation guidelines than our usual open-set tests.

GSI: If Rudmose said we should automate our hearing tests in 1963 why has it taken so long?

Dr. Margolis: When I started working on automated hearing test in 2000, I was shocked at the resistance from many audiologists. I expected audiologists to see what my patient and hospital administrator saw – an opportunity to move our profession forward with technology. Some sources of that backlash are

  • Fear of losing audiology jobs. We published our analysis of the capacity and need for hearing testing to address this fear (Margolis & Morgan 2008). In 2000, all the audiologists working full time on basic hearing testing could not deliver half the need. That gap will continue to increase at the baby-boomers age.
  • Fear of giving up an important part of our scope of practice. When I started this work, the AuD conversion was just beginning. Now that we are a doctoral profession, we need to view our roles like other doctors. By automating basic testing audiologists' time can be focused on activities that require doctoral skills.
  • Reimbursement. Although the costs of performing basic tests by doctoral practitioners usually exceeds the reimbursement, clinicians are reluctant to perform clinical procedures that are not reimbursable. It is important that we establish the value of automated tests and establish Audiology as the profession that is best able to interpret the results and formulate treatment plans.
  • Training programs. Our clinical training programs have been slow to incorporate the teaching of the methods and advantages of automation.
  • Inertia. In every profession, there is a reluctance to change methodologies especially when current methods produce the needed results. There is an interesting parallel with optometry, a profession that is transitioning to automated tests faster than we are. The refraction needed in our lenses is being determined entirely by physical measurements, abandoning the “Which is clearer, this or this" procedure (although some optometrists are reluctant to change just like us.) One of the reasons for the change in optometry is the finding that manual testing requiring subjective responses is subject to bias, which has also been shown to be the case in audiometry (Margolis et al., 2015, 2016).


Margolis, R.H., Morgan, D.E.. Automated Pure-Tone Audiometry: An Analysis of Capacity, Need, and Benefit. Amer J Audiology, 17, 109-113, 2008.

Margolis, R.H., Saly, G., Le, C., Laurence, J. Qualind. A Method for Assessing the Accuracy of Automated Tests. J. Amer. Acad. Audiol., 18, 78-89, 2007.

Margolis, R.H., Wilson, R.H., Popelka, G.R., Eikelboom, R.H., Swanepoel, D.W. Distribution characteristics of normal pure-tone thresholds. Int. J. Audiology, 54, 796-805, 2015.

Margolis, R.H., Wilson, R.H., Popelka, G.R., Eikelboom, R.H., Swanepoel, D.W. Distribution Characteristics of Air-Bone Gaps: Evidence of Bias in Pure-Tone Audiometry. Ear & Hearing, 37, 177-188, 2016.

Rudmose, W. (1963) Automatic audiometry. in J.Jerger, (ed): Modern Developments in Audiology, New York, Academic Press, 30-75.

Thursday, March 11, 2021

By Richard S. Tyler, PhD; Najlla O. Burle; and Patricia C. Mancini, PhD

               Patients with tinnitus often seek help from audiologists when it disrupts their thoughts and emotions, sleep, concentration and/or hearing. This can have dramatic effects on their quality of life.1 Some of the patients can be very distressed, and there are no pills or surgery for sensorineural tinnitus.  Unfortunately, many healthcare professionals choose not to help them.  Audiologists are qualified to provide counseling, but reimbursement is difficult. The evidence is sufficient for reimbursement for counseling— period. Counseling can also be performed by a wide variety of other health care professionals, including physicians, nurse practitioners, and psychologists.


​Tinnitus patients usually have hearing difficulties, caused by a hearing loss or their tinnitus.  We can provide counseling as our training in aural rehabilitation includes the understanding of the psychological consequences of hearing loss, tinnitus, and other such auditory conditions. If emotional consequences become severe, we can and should refer to other professionals.  Sometimes we receive referrals from psychiatrists and psychologists who need our help with their tinnitus patients. While these professionals can help with the emotional aspects, they are not trained regarding tinnitus and hearing loss and their consequences. Thus, many refer their patients for an audiologist's help.

Audiologists can help patients understand their tinnitus—how it affects their hearing, thoughts and emotions, sleep and concentration, and how they react to it. We suggest ways they might change their behavior to help manage challenges. In fact, several counseling and sound therapy treatments were designed by and taught by audiologists.2-7


As most of these patients have hearing loss, we can be reimbursed for diagnosing and measuring their hearing loss, and provide hearing aids 8.  This is an important contribution.  Which services get reimbursed and the reimbursement rate is influenced by many factors, including lobbying by organizations. Audiology services are valuable and should be reimbursed appropriately. However, because of the limited reimbursement, many cannot justify helping tinnitus patients.


The effectiveness of any counseling largely depends on the individual interactions between the patient and the clinician. Systematic strategies can help, but the outcome is strongly influenced by the individual clinician. Even following the same counseling protocol, some patients will see relief of symptoms, but not others. Recently, a study challenged the effectiveness of cognitive behavior therapy (CBT)9 generally, and this has been applied to CBT for tinnitus 10.  The study concluded that the individual clinician is the main factor in the outcome of the CBT. Each patient with tinnitus experiences different symptoms, and research should focus on individuals, not groups. 11 Unfortunately, this rarely occurs in research studies.

               Many argue in favor of the need for “evidence-based" research studies to support treatments. With respect to tinnitus, CBT is promoted as a strategy to treat patients.12 Interestingly, for smoking cessation and weight management, evidence (and reimbursement) is available for counseling, but it does not have to be CBT counseling for weight management; it is just “counseling for weight management."

               While some tinnitus patients benefit from reading online information or those from patient handouts, others may not find these resources helpful and need personalized.4,6 Millions of dollars can be spent on each study documenting each variation of counseling for tinnitus patients. The government should direct money for counseling.

               While some patients may choose to independently seek out health care information on tinnitus, many ask for individualized, patient-centered care. Unfortunately, the current health care reimbursement landscape is a barrier to audiologists performing this type of patient-centered care.

               In the evolution of Tinnitus Activities Treatment (TAT), we saw the benefit of (and included) Progressive Muscle Relaxation and Guided Imagery.6 Audiologists ask patients to focus, at the moment, without judging. Our TAT includes helping patients to “accept," “own" their tinnitus. 


Hearing loss is not just about hearing but also about how we use our hearing for communicating, interacting with friends, enjoying life, and planning for the future. Smoking cessation and weight management are reimbursed by government health care and insurance agencies—so why not hearing loss and tinnitus? Audiology services are valuable and should be reimbursed appropriately. Clinicians must make choices about how to spend their time and resources. Because of the limited reimbursement, many cannot justify helping tinnitus patients.

Our professional organizations need to work collaboratively with legislators and take this on as the most important focus of our profession. It is necessary to conduct solid research not only on the effectiveness of counseling but also its cost-benefit analysis compared to other reimbursable procedures. We need the help of audiology professional organizations to speak up—as well as the help of people with tinnitus, including those committees and legislatures, since they can appreciate the consequences and be helpful.

               Audiologists can provide counseling to help with the psychological consequences of hearing loss and tinnitus (and hyperacusis). A reasonable, driving force, behind our ability to help these patients depends on reimbursement for our audiological services.

ABOUT THE AUTHORS: Richard Tyler, PhD, is a professor of otolaryngology–head and neck surgery and of communication sciences and disorders at the University of Iowa. Najlla O. Burle is a speech therapist affiliated with the Post-Graduate Program in Speech Therapy Sciences at the Universidade Federal de Minas Gerais in Brazil, where Patricia C. Mancini, PhD, is an associate professor in the university's department of speech-language pathology and audiology.

1. Tyler, R., Perreau, A., Mohr, A. M., Ji, H., & Mancini, P. C. (2020). An Exploratory Step Toward Measuring the 'Meaning of Life' in Patients with Tinnitus and in Cochlear Implant Users. Journal of the American Academy of Audiology, 31(4), 277-285. doi: 10.3766/jaaa.19022.
2. Sweetow, R. W. (1984). Cognitive-behavioral modification in tinnitus management. Hearing Instruments, 35, 14-52.
3. Tyler, R. S., & Erlandsson, S.  (2003). Management of the tinnitus patient.  In L.M. Luxon, J.M. Furman, A. Martini, and D. Stephens. (Eds.), Textbook of Audiological Medicine (pp. 571-578).  London, England: Taylor & Francis Group.
4. Henry, J., & Wilson, P. (2001). The Psychological Management of Chronic Tinnitus: A Cognitive-Behavioral Approach. Needham Heights, MA: Allyn & Bacon.
5. Mohr, A. M., & Hedelund, U.  (2006). Tinnitus Person-Centered Therapy. In R.S.Tyler (Ed.), Tinnitus Treatment: Clinical Protocols (pp. 198-216).  New York: Thieme.
6. Tyler, R. S., Gehringer, A. K., Noble, W., Dunn, C. C., Witt, S. A., & Bardia, A. (2006). Tinnitus activities treatment. In R. S. Tyler (Ed.), Tinnitus treatment: Clinical protocols. (pp. 116-132). New York, NY: Thieme.
7. Tyler, R. S., Gogel, S. A., & Gehringer, A. K. (2007) Tinnitus activities treatment. Progress in Brain Research, 166, 425-434.
8. Tyler, R., Jilla, A. M., & Von Dollen, S. (2020) Coding and Reimbursement Specialty Series:  Tinnitus. Audiology Today,  March/April.
9. Johnsen, T.J., & Friborg, O. (2015) The effects of cognitive behavioral therapy as an anti-depressive treatment is falling: A meta-analysis. Psychological Bulletin, 141(4), 747-768.
10. Tyler, R. S., & Mohr, A. M. (2017). Is CBT for tinnitus overemphasized? The Hearing Journal, 70(2), 8-10.
11. Tyler, R. S., Oleson, J., Noble, W., Coelho, C., & Ji, H. (2007).  Clinical trials for tinnitus: Study populations, designs, measurement variables, and data analysis. Progress in Brain Research, 166, 499-509.
12. Tunkel, D. E., Bauer, C. A., Sun, G. H., Rosenfeld, R. M., Tyler, RS,…et al. (2014) Clinical Practice Guideline:  Tinnitus. Otolaryngology-Head and Neck Surgery, 151(2S), S1-S40.

Tuesday, February 23, 2021

By Jay R. Lucker, Ed​D, CCC-A/SLP, FAAA; Cydney Fox, AuD; and Bea Braun, AuD

​Download the pdf version here​.

Auditory processing disorders (APD) in school-age children can lead to learning problems.1 Audiologists may determine the presence or absence of APD in this population and make specific therapeutic recommendations. However, many therapies for APD have not been peer-reviewed or examined with statistical analysis to determine efficacy. The present study evaluates a therapeutic option called CAPDOTS-Integrated (also referred to as CAPDOTS), an online training program that aims to improve dichotic listening problems.

               According to Jerger, “Dichotic listening (DL) tests are at the core of the diagnostic evaluation of auditory processing disorder... [Such tests] have been used for decades both as screening tools and as diagnostic tests in APD evaluation."2 Thus, audiologists evaluating school-aged children for APD may find these children to have dichotic listening problems. Those diagnosed with dichotic listening problems are recommended to try a dichotic listening training program, such as CAPDOTS-Integrated.1 Presently, CAPDOTS-Integrated claims to treat binaural auditory integration problems known as dichotic listening difficulties. CAPDOTS is a treatment program requiring access to the internet and good quality headphones. It can be completed in the trainer's office, at school, or home.1 On their website, it states that CAPDOTS–Integrated “improves the ability to follow complex, multi-step directions, listening attention especially in distracting background noise, inferential listening and understanding of group instructions, auditory memory, academic performance especially for reading comprehension and spelling."1 However, one may ask what evidence supports these claims?


               The CAPDOTS website cites two conference handouts on the changes after CAPDOTS training. Carol A. Lau discussed mid-latency, electrophysiological responses in two subjects.2 The 27-year-old subject identified as diagnosed with “binaural integration deficit" only completed 80 percent of the CAPDOTS training. The 16-year-old subject is reported diagnosed with a binaural integration (dichotic listening) auditory processing disorder.  Results of mid-latency electrophysiological measures revealed increased response findings after these subjects completed CAPDOTS.  Qualitative data discusses the Na-Pa amplitude changes and percent differences for each subject.  Statistical analyses were not used so there is no indication whether these changes were significant.

               The second conference presentation, also by Carol Lau, used only three subjects, all identified as having auditory processing problems based on scores from the SCAN and Dichotic Digits Tests.4 Number values are the only results provided. There were no statistical analyses discussed.

               A third reference is an article published in The Hearing Journal on a study involving three adults identified with co-morbid peripheral hearing loss and CAPD.5 Each subject was used as a single case study presenting results of auditory processing tests pre- and post- CAPDOTS training. Findings identified improvements after CAPDOTS training, but no statistical analyses were provided.

               Another study published in 2013 used individual case presentations including one of a second-grade boy with auditory processing problems, an older adult who suffered a head injury, a preteen with no history of educational or auditory processing concerns, two girls identified by qualitative observations having problems following verbal interactions and directions, and an adult with a history of learning disabilities.6 All subjects were evaluated using the SCAN test as well as Dichotic Digits. Data compared pre- and post- CAPDOTS training with improvements noted, but no statistical analyses were provided.


               Studies have looked at changes in auditory processing after completing CAPDOTS training.  However, none provides statistical analyses, and all use small samples. The present looked at a large sample of children and adolescents diagnosed with APD.  The researchers measured what changes occur when statistical analyses are used.  The research questions asked whether auditory processing test findings revealed significant improvements after completing CAPDOTS training, and for what specific measures, as well as investigating the degree of improvement following training.


Forty-six children and adolescents (26 males and 20 females) participated in the study. Two of the authors (CF and BB), both licensed clinical audiologists, evaluated participants identifying all having significant APD. Participants ranged from 6 years to 18 years with a mean age of 10.0 years (standard deviation of 2.70 years). Participants had normal hearing which would not influence their auditory processing or treatment.

Auditory Processing Tests: Auditory processing was assessed via the SCAN-3 C for children7 and A for adolescents8 Dichotic Digits (DDT),9 Frequency Patterns and Duration Patterns,10 Competing Sentences,11 Low Pass Filtered Speech,12 SSW Test,13 and the Phonemic Synthesis Test (PST),14 using the specific scores for each ear or individual measure on the tests.

CAPDOTS Training: CAPDOTS is a web-based therapy based on a Staggered Dichotic Listening Training (SDLT) paradigm, using words, digits, and syllables. SDLT uses a difference in the timing presentation of targets in each ear starting with a gap between stimuli presented to each ear with the gap decreasing until the two stimuli are presented simultaneously.

               Participant CAPDOTS therapy at home with monitoring by the audiologist. Upon completion of CAPDOTS training, the auditory processing evaluation was re-administered. Thus, pre- and post- training scores were available and were compared allowing for statistical analyses of the data.

Data Analyses: A paired (also called dependent) samples t-test was calculated for each measure. Performance for the right ear was compared separately from performance for the left ear when appropriate.  -test scores yielding probability values less than 0.05 identified that the change was significant.  Significant t-test findings were then evaluated by Cohen's d.


               A total of 24 measures were completed in the study. Table 1 presents the results of the pre- and post- CAPDOTS measures, indicating large differences between the post- and pre-therapy findings. 

Table 1 full.jpg

Table 2 shows the t-test statistical analyses results, which reveal significant findings for 16 of the 24 measures.  Only one dichotic measure (Right Ear for Competing Words-Directed Ear Right Ear First) was not significant.  The remaining 7 dichotic measures were significant.  Thus, significant findings were found for 7 of the 8 dichotic measures of auditory processing.

Table 2.JPG

               Surprisingly, CAPDOTS training not only significantly improved dichotic listening but also made significant improvements in other areas of auditory processing. Nine measures were also found to have significant t-test results.  Therefore, the present study indicates that CAPDOTS therapy can show significant improvements in dichotic listening as well as in other areas of auditory processing.

               To determine how much change was found after CAPDOTS training, a post hoc analysis was conducted for the 16 significant measures found.  This analysis determined the effect size of change using Cohen's d for dependent samples. Table 3 presents the results of these calculations.

Table 3.JPG

               Cohen's d values range from no significant effect (less than .20) to a small effect (from .20 to .49), a medium effect (from .50 to .79), a large effect (from .80 to 1.0), and very large effect (greater than 1.0).  These values are measures of the number of standard deviation change occurring.  A review of Table 3 indicates that only one measure revealed a small improvement and three showed a medium improvement. Large improvements were found for six measures and very large improvements for six other measures. Thus, the majority of improvements were found to be large and very large.


               Results of the present study indicated significant improvements in dichotic listening with 7 of the 8 measures showing large and very large effect sizes.  What was surprising was that 9 non-dichotic measures of auditory processing revealed significant changes after CAPDOTS therapy.  Thus, the conclusion drawn is that CAPDOTS therapy not only can make a significant improvement in dichotic listening but can also change other auditory processing factors.  Changes were found in auditory temporal processing (Frequency Patterns and Time Compressed Sentences for each ear), understanding distorted speech (Filtered Speech), and auditory phonological processing (Phonemic Synthesis).  Thus, professionals recommending CAPDOTS training for children and adolescents with auditory processing disorders should not only expect improvement in dichotic listening and auditory integrative processing but also in other areas identified above.

               One important outcome from the present study relates to what is stated on the CAPDOTS website1 that CAPDOTS training should lead to improvements in a variety of areas which include listening in noise, understanding verbal messages in groups (which may relate to listening in noise), reading and spelling abilities.  The findings from the present evaluation indicated significant improvements in auditory phonological processing which would be related to reading decoding and spelling abilities.  However, no improvements were evidenced for listening in noise.  Results of the Auditory Figure-Ground measures used were not significant (p>0.05) for either the right or left ears. Although the CAPDOTS website does not address auditory temporal processing skills which are important, the results of the present study indicate significant improvements in this auditory process.  Perhaps this is due to the subject's ability to more rapidly process (time compression) and be better able to understand the prosodic features of the verbal message (frequency pattern processing).  Additionally, integrative processing (dichotic listening) can contribute to other aspects of listening because it involves processing the meaning of what is spoken to the listener.


               As with any study, there can be limitations leading to recommendations for further research.  One limitation could be considered the number of participants used.  This study included 46 participants all identified with auditory processing disorders.  However, the specifics of each participant's auditory processing issues were not identified. Some participants might have had additional issues such as attention disorders (ADHD) or autism spectrum disorder (ASD) that were not identified.  However, if participants had ADHD or ASD, findings from the present study suggest that even for people with these diagnoses, CAPDOTS therapy might be beneficial in improving their auditory processing abilities.  Further research may wish to look at subjects diagnosed with ADHD or with ASD and compare these participants with subjects not found to have neither of these psychological issues.

               Another limitation of the present study relates to the specific auditory processing tests used.  Other tests used by audiologists may need to be statistically evaluated to determine if there are significant changes following CAPDOTS training.  Areas such as auditory (listening) attention were not evaluated.  It would be interesting to see what changes might occur on a measure of auditory attention such as the Auditory Continuous Performance Test (ACPT)15 following CAPDOTS therapy.

               One other limitation of the present study relates to age.  Participants were children from 6 to 12 years old as well as adolescents from 13 to 18 years old.  It is likely that children performed differently from adolescents.  Thus, future research may wish to compare findings for children versus adolescents to determine if differences may be found based on age.

               Whatever the limitations, the present investigation looked carefully at a large group of children and adolescents who completed CAPDOTS-Integrated training online.  The findings indicated very significant changes in dichotic listening as well as in other areas of auditory processing not specific to dichotic listening. The conclusion is that after CAPDOTS training, one should expect to see significant improvements in various aspects of auditory processing.

ABOUT THE AUTHORS: JAY R. LUCKER, EDD, CCC-A/SLP, FAAA, is a professor and the director of the five-year accelerated master's degree program in the department of communication sciences and disorders at Howard University in Washington, DC. He also has a part-time private practice specializing in issues related to auditory processing disorders. CYDNEY FOX, AUD, is a member of the Craniofacial Team at Orthopaedic Hospital and a full-time audiologist at Audiology Solutions LA. BEA BRAUN, AUD, is a clinical audiologist and a credentialed educational audiologist. She founded the Auditory Processing Center of Pasadena.           

1. Capdots.
2. Jerger J. (2007).  Editorial: Dichotic Listening in the Evaluation of APD. Journal of the American Academy of Audiology, 18(1).
3. Lau C (2016a). Auditory Mid-Latency Response (AMLR) Pre- and Post- Dichotic ListeningTraining Using CAPDOTS.
4. Lau C (2016b). Long-Term Maintenance of Dichotic Listening Skills Following Training Using CAPDOTS. content/uploads/2017/10/DichoticListening_HandoutWeb.pdf
5. Lau C.A. (2016). Staggered dichotic listening training using CAPDOTS-Integrated in Hearing-Impaired Adults. Hearing Journal.
7. Keith RW (2009b).  SCAN-3 A: Tests for Auditory Processing Disorders in Adolescents and Adults.  New York, NY: Pearson Publishers.
8. Keith RW (2009a).  SCAN-3 C: Tests of Auditory Processing Disorders in Children.  New         York, NY: Pearson Publishers.
9. Musiek F. (1983). Assessment of central auditory dysfunction: The Dichotic Digit Test revisited. Ear and Hearing, 4(2), 79–83.
10.   Musiek FE (1994).  Frequency (pitch) and duration pattern tests.  Journal of the American
Academy of Audiology, 5(4), 265-268.
11.   Auditec, Inc. (2015a).  Competing Sentences.
12.   Auditec, Inc. (2015b) Low Pass Filtered Speech.
13.   12. Katz, J. (1968). The SSW test: An interim report. Journal of Speech and Hearing Disorders, 33, 132-146.
14.   13. Katz, J., & Harmon, C. (1981). Phonemic Synthesis: Diagnostic and Training Program. In R. Keith (Ed.), Central Auditory and Language Disorders in Children. Houston: CollegeHill Press.
15.   Keith RW (1994).  Auditory Continuous Performance Test (ACPT).  London: Pearson. 

Wednesday, February 17, 2021

​By Jan-Willem A. Wasmann and Dennis L. Barbour, MD, PhD

Editor's note: This article is published ahead of print. Access the pdf here.​  

Article 2 image.jpg

Since the standardization of hearing tests in the 1940s, the procedure and core equipment functionality for basic audiometry have evolved very little.1 In the digital era, however, change is imminent. Mobile telephones are the most rapidly spreading technology in history, with 6 billion in use after 30 years. Smartphones are driving digital health care in many medical fields. For instance, smartphones can be used to evaluate symptom severity and how symptoms are experienced by patients with Parkinson's disease.2 Given the broad use cases for smartphones today, one might almost forget that telephones were specifically designed to deliver sounds. Artificial Intelligence software running directly on modern smartphones or on internet-accessible cloud servers can be combined with calibrated sound delivery to promote accessible hearing assessments across the world.


A key advantage of using mobile phones in health care delivery is their very mobility. The average time spent by a patient for outpatient care includes 35 minutes traveling to a clinic and 42 minutes waiting for an appointment, while the appointment itself requires 70 minutes. These estimated times are based on a survey of 60,000 Americans about time spent on medical care.3 Hearing health care likely requires similar time commitments. Therefore, remote data collection using mobile phones and follow-up care delivery are promising opportunities for lowering a key barrier to care for patients.4 Also, other barriers including the need for low-touch audiology and/or constraints in low-resource environments can be reduced by remote data collection technologies.5,6

Remote data collection that saves a patient's time could be tele-supervised by a trained clinician or technician.4 Separate development work, however, has focused on automating data collection procedures to save clinicians' time, thereby empowering them to evaluate and treat more patients.7–10 Automating common and low-stakes decisions using algorithms tailored to specific questions or as part of asynchronous services are appropriate methods to preserve clinician efforts for more important decisions. These automations can also be applied to remote data collection for a further streamlined process.11 Automated methods do not typically create a faster test for patients. However, through properly designed machine learning methods, they can deliver faster overall tests with more detailed assessments12 in clinical13 or remote settings.7

Advances in machine learning, internet connectivity, and new data collection tools have the potential to build a system of distributed human and algorithmic expertise we refer to as computational audiology.14 The merit of distributed expertise is that health care resources can be allocated efficiently based on patient needs using scalable procedures. Diagnostic data are collected remotely or by care providers and shared within a computational infrastructure with experts in specialized centers. The clinical question at hand or the patient's need determines which level of diagnostic accuracy is called for and whether remote data streams alone are sufficient. Clinical decision support systems can guide the most useful next steps in a workup, while consumer-grade hardware provides the tools to collect the requisite data flexibly. The result is a hearing health care system organized to provide higher accuracy where needed and greater efficiency where allowed.

Article 2 Fig 1.JPG 

Figure 1. Modular approach to managing the complexity of health data streams combined from n-health domains with identified key factors. Linkages between the modules Aij are based on standards and protocols that facilitate exchanges similar to the network layers in information technology.26

As exciting as these distinct advances are, their synergistic potential will not be realized without concerted management of their associated complexities. Therefore, we suggest a modular approach to control for complexity while maintaining the performance advantage of integrating data streams from multiple sources. Patient-centric care can distinguish itself by considering outcomes across multiple domains in order to provide a better context for making the optimal clinical decision for a specific individual. In Figure 1, each domain is depicted as a column containing three vertical nodes: (1) clinical care management ("why"), (2) computational process/methodology ("how"), and (3) flexible hardware and software ("what"). All nodes within the domain are required to provide adequate care within a discipline, yet linkages to adjacent cross-disciplinary nodes are needed for optimal patient-centric care. Following a modular approach, one can upgrade to a new prediction model without a complete overhaul of the clinical pathway. For example, online machine learning audiometry delivers the same stimuli and addresses the same questions as conventional audiometry, so it can substitute for conventional audiometry with no loss of functionality. What it adds is additional capability to incorporate new questions (e.g., about language skills, cognitive processing, visual perception, etc.) addressed by a variety of data-collecting devices, providing more patient information in the process.12


In patient-centric care, the patient must be empowered to prioritize what is most relevant for his/her well-being and everyday function.15 Information needs to be tailored not only to professionals but, more importantly, to patients and their relatives for them to contribute to informed decisions. To this end, the Ida Institute, a non-profit organization that promotes patient-centric hearing care, is working with hearing experts to design new tools that make hearing test outcomes easier to understand.16 Clinical judgment is required to determine the most appropriate scenario to provide to a patient. At the level of clinical care management, the patient and the clinician should not worry about adequately applying underlying computational methodology (e.g., calculating the optimal audiometric masking procedure) or programming the software. Patient-centric hearing treatment includes monitoring hearing status and checking aided performance, along with technical integrity of hearing aids or cochlear implants (processed at the hardware level), and reporting daily problems.17 Based on large data-sets (processed at the methodology level), predictions can be made when a patient is at risk of suboptimal care, leading to timely interventions (at the patient level) based on emerging needs and increased uncertainty of clinical status. This scenario exists in contrast to the conventional procedure of obtaining periodic check-ups at fixed intervals that leads to unnecessary visits in cases of stable performance.

One can imagine that audiometry, for example, becomes layered in with other patient-executed hearing tests, including localization performance assessed in a lab brought to the patient18 or in virtual environments,19 loudness tests,20 dead region determination in the cochlea,21 auditory nerve integrity testing,22 and speech-in-noise assessment.23 A test battery that integrates hearing tests on a single tablet could enable this workflow. To our knowledge, such test batteries are currently only available to researchers and not yet applicable in clinical use.24 Tests from other disciplines, including language and cognitive tests,25 could be used in combination with hearing tests via the methodology and hardware layers to aid clinicians in selecting the right treatment for the right person at the right time.

We envision that computational audiology has great potential to improve access, accuracy, and efficiency of patient-centric hearing care worldwide.14 The major effort this emerging discipline needs to undertake is to devise interoperability standards that manage the dependencies between nodes. Embracing a modular approach to assessment and intervention within this framework will allow for scalable efforts to improve patient outcomes as new data streams are incorporated. These efforts will ultimately yield patient-centric benefits well beyond audiology.

ABOUT THE AUTHORS: Jan-Willem A. Wasmann is an audiologist at the ENT department of the Radboud University Medical Center Nijmegen in the Netherlands. His recent work includes AI-guided CI fitting techniques, simulated directional hearing based on neuralnetworks, and remote care. Learn more about computational audiology at L. Barbour, MD, PhD, is an associate professor of biomedical engineering at Washington University in St. Louis and the chief executive officer of Bonauria. His recent work involves developing next generation diagnostic procedures for computational audiology.

1. Hughson, W. & Westlake, H. Manual for program outline for rehabilitation of aural casualties both military and civilian. Trans Am Acad Ophthalmol Otolaryngol 48, 1–15 (1944).
2. Taylor, K. I., Staunton, H., Lipsmeier, F., Nobbs, D. & Lindemann, M. Outcome measures based on digital health technology sensor data: data- and patient-centric approaches. Npj Digit. Med. 3, 1–8 (2020).
3. Russell, L. B., Ibuka, Y. & Carr, D. How Much Time Do Patients Spend on Outpatient Visits? Patient Patient-Centered Outcomes Res. 1, 211–222 (2008).
4. Ratanjee-Vanmali, H., Swanepoel, D. W. & Laplante-Lévesque, A. Digital Proficiency Is Not a Significant Barrier for Taking Up Hearing Services With a Hybrid Online and Face-to-Face Model. Am. J. Audiol. 29, 785–808 (2020).
5. Swanepoel, D. W. & Hall, J. W. Making Audiology Work During COVID-19 and Beyond. Hear. J. 73, 20–22 (2020).
6. Swanepoel, D. W. & Clark, J. L. Hearing healthcare in remote or resource-constrained environments. J. Laryngol. Otol. 133, 11–17 (2019).
7. Barbour, D. L. et al. Online Machine Learning Audiometry. Ear Hear. 40, 918–926 (2019).
8. Charih, F., Bromwich, M., Mark, A. E., Lefrançois, R. & Green, J. R. Data-Driven Audiogram Classification for Mobile Audiometry. Sci. Rep. 10, 3962 (2020).
9. Eikelboom, R. H., Swanepoel de, W., Motakef, S. & Upson, G. S. Clinical validation of the AMTAS automated audiometer. Int J Audiol 52, 342–9 (2013).
10.  Bastianelli, M. et al. Adult validation of a self-administered tablet audiometer. J. Otolaryngol. - Head Neck Surg. J. Oto-Rhino-Laryngol. Chir. Cervico-Faciale 48, 59 (2019).
11.  Swanepoel, D. W. & Hall, J. W. Making Audiology Work During COVID-19 and Beyond. Hear. J. 73, 20–22 (2020).
12.  Barbour, D. L. & Wasmann, J.-W. A. Performance and Potential of Machine Learning Audiometry. Hear Journal. 2021;74(3):40,43.
13.  Song, X. D. et al. Fast, Continuous Audiogram Estimation Using Machine Learning. Ear Hear. 36, e326-335 (2015).
14.  Wasmann, J.-W. et al. Computational Audiology: New Approaches to Advance Hearing Health Care in the Digital Age. Ear Hear. (2021). DOI: 10.1097/AUD.0000000000001041. In press.
15.  International Classification of Functioning, Disability and Health (ICF).
16.  Klyn, N. A. M., Rutherford, C., Shrestha, N., Lambert, B. L. & Dhar, S. Counseling with the Audiogram. Hear. J. 72, 12 (2019).
17.  Remote Hearing Care and Teleaudiology. Hearing Tracker
18.  Wasmann, J. A., Janssen, A. M. & Agterberg, M. J. H. A mobile sound localization setup. MethodsX 7, 101131 (2020).
19.  Stecker, G. C. Using Virtual Reality to Assess Auditory Performance. Hear. J. 72, 20 (2019).
20.  Schlittenlacher, J. & Moore, B. C. Fast estimation of equal-loudness contours using Bayesian active learning and direct scaling. Acoust. Sci. Technol. 41, 358–360 (2020).
21.  Schlittenlacher, J., Turner, R. E. & Moore, B. C. A hearing-model-based active-learning test for the determination of dead regions. Trends Hear. 22, 2331216518788215 (2018).
22.  Wasmann, J.-W. A., van Eijl, R. H., Versnel, H. & van Zanten, G. A. Assessing auditory nerve condition by tone decay in deaf subjects with a cochlear implant. Int. J. Audiol. 57, 864–871 (2018).
23.  Potgieter, J.-M., Swanepoel, D. W. & Smits, C. Evaluating a smartphone digits-in-noise test as part of the audiometric test battery. South Afr. J. Commun. Disord. Suid-Afr. Tydskr. Vir Kommun. 65, e1–e6 (2018).
24.  Shapiro, M. L., Norris, J. A., Wilbur, J. C., Brungart, D. S. & Clavier, O. H. TabSINT: open-source mobile software for distributed studies of hearing. Int. J. Audiol. 59, S12–S19 (2020).
25.  Anguera, J. A. et al. Video game training enhances cognitive control in older adults. Nature 501, 97–101 (2013).
26.  Alani, M. M. Guide to OSI and TCP/IP models. (2014).