Acoustic-Reflex Dynamics and Temporary Threshold Shift in Octave-Band Noise Exposures

ACOUSTIC-REFLEX DYNAMICS AND TEMPORARY THRESHOLD SHIFT IN OCTAVE-BAND NOISE EXPOSURES

MICHAEL JAY MOUL

A DISSERTATION PRESENTED TO THE GRADUATE COUNCIL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

1985 ACKNOWLEDGEMENTS

I would like to thank those people whose help and cooperation facilitated this research. Major Ernest

Hepler provided the conceptual background. His constant encouragement and friendship made the project feasible.

Dr. Kenneth Gerhardt, chairman of my supervisory committee, had the patience and expertise needed to keep the project going over many rough spots. His teaching, guidance and friendship were such that I will always look to him as my mentor.

My supervisory committee, Drs . Alice Holmes, Joseph

Kemker, Patricia Kricos and Alan Agresti not only helped with this research effort, but also made my years on campus much more rewarding. Their support as well as that of my fellow students is very much appreciated.

I am very grateful to the United States Army Medical

Service Corps for my selection and funding for this educational experience. My military academic preceptor.

Lieutenant Colonel Roy Sedge, supported my application for training and continued to support me throughout my schooling.

n I owe my greatest thanks to my wife, JoAnne, and to my daughters, Michele and Marcia. Their love makes every project worthwhile and made this one possible.

in TABLE OF CONTENTS Page

ACKNOWLEDGEMENTS ii

ABSTRACT vi

CHAPTER

I BACKGROUND AND PURPOSE 1

Introduction 1 Review of Literature 5 Temporary Threshold Shift 5 Measuring Temporary Threshold Shift ... 10 The Acoustic Reflex 16 Measuring the Acoustic Reflex 19 Acoustic Reflex Threshold 23 Acoustic Reflex Magnitude 27 Acoustic Reflex Latency 29 Acoustic Reflex Adaptation 31 The Acoustic Reflex and Temporary .... Threshold Shift 35 Susceptibility to Noise 40 Statement of Purpose 46

II METHODS 4 8

Subjects 48 Instrumentation and Procedures 49 Exposure 49 Behavioral Hearing Threshold Measurements 52 Acoustic Reflex Measurements 57 Calibration 6 4 Data Analysis 68

III RESULTS 69

Behavioral Threshold Shift 69 Acoustic Reflex Properties and TTS 80 Acoustic Reflex Threshold 80 Acoustic Reflex Magnitude 84 Acoustic Reflex Latency 92

IV CHAPTER Page

III RESULTS (Continued)

Acoustic Reflex Adaptation 96 Demographic Data and TTS 99 Pre-Exposure Audiometric Threshold and TTS 102 Multiple Regression Modeling 103

IV DISCUSSION 110

Interpretation of Results 110 Behavioral Threshold Shift 110 Acoustic Reflex Threshold 113 Acoustic Reflex Magnitude 114 Acoustic Reflex Latency 117 Acoustic Reflex Adaptation 119 Ear Canal Volume/Resonance 121 Demographic Differences 123 Predictive Modeling 124 Summary 127

REFERENCES 129

BIOGRAPHICAL SKETCH 146

v Abstract of Dissertation Presented to the Graduate Council of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

ACOUSTIC-REFLEX DYNAMICS AND TEMPORARY THRESHOLD SHIFT IN OCTAVE-BAND NOISE EXPOSURES

Michael Jay Moul

May, 1985

Chairman: Kenneth J. Gerhardt, Ph.D. Major Department: Speech

Clinical and experimental observations indicate that there are individuals uniquely susceptible to noise-induced shifts in auditory sensitivity. Experimentally induced temporary threshold shift (TTS) varies widely in subjects exposed to the same noise source. The acoustic reflex (AR) provides a protective function that also varies widely in individuals. The AR therefore may be one of the complex factors that relate to individual susceptibility to TTS.

This study examined the relation of dynamic properties of the AR to the TTS that developed in thirty human subjects. The following experimental question was asked.

Can pre-exposure measurements of AR activity (threshold, magnitude, latency and adaptation) be used to predict the

vi .

TTS that develops in normal hearing subjects following two, separate, two-hour octave-band noise exposures (centered at

0.5 kHz and 3.0 kHz).

All subjects were exposed to both noises. Significant

TTS occurred that was more pronounced for the high- frequency exposure. The audiometric pattern of TTS differed for the two exposures. Pre-exposure behavioral hearing threshold and several AR parameters were found to be significantly correlated to the TTS that resulted from both noise exposures.

Multiple regression modeling showed AR adaptation, onset latency and magnitude (as elicited with a broad-band noise stimulus) to be only slightly predictive of the TTS that resulted from the low-frequency noise exposure. Other elicitor/variable combinations were less predictive particularly for TTS resultant from the high-frequency noise

These results suggest that the AR is a factor in the development of TTS. Only a slight predictive relationship exists however. Previously reported data on variation in both the AR and in TTS according to the spectral content

of noise were confirmed.

vix .

CHAPTER I BACKGROUND AND PURPOSE

Introduction

to Loss of hearing sensitivity caused by exposure problem. noise is a significant economic and social States Expenditures by both private industry and the United year government total hundreds-of-millions of dollars per costs in disability payments alone. The additional care involved in funding for diagnostic and rehabilitative performance, are equally staggering. The reduction in job

increase in absenteeism and employee retraining/replacement are costs associated with reduced communication ability

incalculable. No dollar figure can be placed on the

personal and social impact of this problem. Hearing loss it not only effects the impaired individual's life style,

also changes that of family, co-workers and friends.

Annoyance, fear, embarrassment, loss of self-esteem and

gradual withdrawal from society often accompany hearing

loss. All these costs, both monetary and human, make

noise-induced hearing loss one of our nation's most serious

occupational issues (Myklebust, 1964; Kryter, 1970; Hepler,

Moul and Gerhardt, 1984)

1 .

Hearing conservation measures have been mandated in several federal government documents. Program success has been constantly undermined by a lack of worker education and by industry's reaction to implementation costs and perceptions of excessive governmental control. A United

States Court of Appeals decision on November 8, 1984 (in response to industry's complaints) virtually invalidated the Occupational Safety and Health Administration's hearing conservation standard and will adversely effect efforts to deal with this problem nationwide (Department of Defense,

1978; Code of Federal Regulations, 1981; Occupational

Safety and Health Administration, 1983; Cherow, 1985)

However, even if such programs could be effectively implemented there is still an unresolved issue of individual susceptibility to the damaging effects of noise.

Clinical experience and data from military and industrial noise-exposed populations indicate that there are individuals who are uniquely susceptible to noise-

induced hearing loss (Burns, 1973; Hepler, Moul and

Gerhardt, 1984) . Such individuals develop a loss of hearing sensitivity after relatively limited exposure to noise. The identification of "at risk" individuals prior

to their placement in jobs involving noise exposure (or prior to induction into military service) could significantly

lower the incidence of noise-induced hearing loss.

Unfortunately, no single predictive index of this 3

susceptibility has been identified (Jerger and Carhart,

1956; Simmons , 1963; Ward, 1965; Michael and Bienvenue,

1977; Humes, 1977). Research efforts in the area of individual susceptibility have therefore been focused upon the identification of patterns or relations between various auditory and non-auditory indices that might improve prediction. This "test battery" approach must include measures of all factors that potentially protect an individual's auditory system from damage, or conversely, that might predispose it to damage.

The acoustic reflex (AR) is a contraction of middle- ear muscles in response to intense acoustic stimulation.

The AR reduces the intensity of sound transmitted through the middle-ear system and may therefore provide some degree of protection to the inner-ear structures known to be damaged by noise. The extent of this protection is varied and appears to be dependent upon the nature of the noise and upon certain dynamic properties of the individual's

AR. The implication is that individuals with, for example, an AR response that adapts quickly will be more susceptible to damage than those with AR responses that are sustained for longer time periods.

The AR/susceptibility literature is replete with inconsistent results due, in part, to the wide range of equipment and noise exposure paradigms that have been used

(Coles, 1969; Johansson, Kylin and Langfy, 1967; Popelka,

1984). Many studies reporting no demonstrated 4

AR/susceptibility relation were accomplished using short- term exposure to noise (minutes) . Other research involving long-term exposure to noise (hours) suggests that the AR may be a factor in individual susceptibility (Borg and

Odman, 1979; Gerhardt, Melnick and Ferraro, 1979; Gerhardt

and Hepler, 1983) .

Documenting individual variation in susceptibility to noise as related to AR parameters required that a series of coordinated studies be completed. Hepler (1984) provided data on AR properties for a noise exposure (broad-band noise at 90 db SPL for two hours) that was chosen to closely approx- imate the actual uninterrupted daily exposures of military and industrial workers. That study supported the hypothesized relation between specific AR properties and the auditory threshold shift demonstrated following noise exposure. There is evidence from other researchers that the spectral content of noise alters the portion of the cochlear partition affected. This changes the extent of hearing threshold shift that results. It also contributes to the

AR being frequency specific in terms of the amount of attenuation it provides (Karlovich and Wiley, 1974; Mills, Adkins

and Gilbert, 1981; Gerhardt and Hepler, 1983) . In order to further explore the frequency specific characteristics of the AR it was necessary to obtain data on octave-band noise exposures using extensions of methodology developed in the Hepler (1984) project. It was anticipated .

that obtaining such data would more clearly define the relative importance of various properties of the AR that appear to be related to susceptibility to noise-induced auditory threshold shift. Ultimately such information could be useful in developing an AR adjunct to a noise- induced hearing loss risk inventory.

Review of the Literature

Temporary Threshold Shift

Francis Lord Bacon first described the general auditory effects of noise exposure in 1627. He noted relationships that now seem intuitive in our modern world of intense noise (Ward, 19 79) . Noise can interfere with and mask other sounds, making communication difficult. It can also cause both temporary and permanent changes in auditory sensitivity that continue after the noise has ceased.

Measures of behavioral hearing prior to and following noise exposure define such threshold shifts. Temporary threshold shift (TTS) can last from seconds to days, whereas permanent threshold shift (PTS) is not resolved over time

Laboratory animals can be subjected to noise at intensities and for durations that result in PTS. Research 6

of the temporary on animals has provided direct comparisons (Ward, 1973; and permanent effects of noise exposure 1977; Durrant, Duvall, Ward and Luhala, 1974; Hunter-Duvar , 1982). Human 1978; Ward, 19 80b; Liberman and Mulrov , to comparisons auditory research has been ethically limited rather than of TTS and PTS data that are retrospective has been prospective. Controlled laboratory induced TTS have worked in compared to existing PTS for subjects who 1973, 1978; noise for periods of years (Ward, 1973; Kraak, 1981). Such comparisons assume that Melnick , 1978; Kraak, underlie TTS and the same basic physiological processes given exposure PTS, and that the physical properties of a and time (i.e., frequency spectrum, intensity, duration, both pattern) relate similarly to the development of known similarities that may (Melnick, 19 79) . There are morpho- validate these assumptions. In both TTS and PTS, partition. It logical changes occur along the cochlear of stereocilia has been reported that TTS is the consequence (following disarray particularly in outer hair cells. PTS

extended exposure to the same noise) may represent a with eventual continuation of this same histopathology ,

stereocilia fusion and/or destruction of both outer Duvall, and inner hair cells (Eldredge and Coveil, 1958; and Ward and Luhala, 1974; Hunter-Duvar, 1977; Robertson

Johnston, 1980; Ward, 1980). The maximum effect of in both temporary and permanent noise-induced change is .

the 3.0 to 6.0 kHz auditory hair cell region. This phenomenon may relate to physical properties of the auditory

system such as the point-of-maximum displacement in

cochlear fluid movement, the relative vascular and struc-

tural integrity of the apical region and the resonant

characteristics of the middle ear (Schuknecht and Tonndorf,

1960; Elliott and Fraser, 1970; Tonndorf, 1976; Chung, 1980;

Pickles, 1982). Audiometrically , PTS frequency patterns

from long term exposures are the same as those shown in

TTS produced by similar noise (Miller, Watson and Covell,

1963; Kraak, 1981) . Generally, PTS and TTS grow linearly

with the logarithm of time (Ward, 1965) , and both measures

are widely variable among individuals exposed to the same

noise source (Melnick, 1978)

These observed similarities in PTS and TTS resulted in

several hypotheses. Early investigators proposed that TTS

might be used to predict the PTS that would result from a

more intense exposure (Pyser, 1930; Temkin, 1933). Ward

(1973) suggested that PTS may result if the TTS from one

day of noise exposure has not resolved prior to the next

day of work. Furthermore, he speculated that the TTS

produced by eight hours of noise exposure can be used to

infer the PTS that would be caused by ten years of

exposure (Ward, 1965) . Ward also proposed that the best

predictor of PTS may be the TTS resulting from the specific

noise to which the individual is routinely exposed. Mills, .

Gengel, Watson and Miller (1970) and Mills, Adkins and

Gilbert (1981) similarly suggested that TTS reaches an asymptotic level after about eight hours of exposure that is indicative of the maximum PTS that can be produced by exposure to that same noise. Kraak (1981) described results from an unpublished German dissertation that support these hypothesized PTS/TTS similarities, at least for TTS integrated over time in forty-five weaving mill workers.

Unfortunately, the relation between TTS and PTS is not as clearly defined as the above body of literature suggests. There does appear to be good agreement between

TTS and PTS data as averaged for groups (and this has allowed the development of damage risk criteria for industry) but this predictive power does not extend to individual comparisons (Jerger and Carhart, 1956; Burns and Robinson, 1970; Melnick, 1978; Kraak, 1981). Mills

(1984) reported little success in preliminary comparisons of TTS and PTS in an ongoing study of large carefully matched populations of various animal species. Attempts to directly predict expected human PTS from observed TTS for individuals (with the 'exception of the German work cited above) have been mostly unsuccessful (Miller, 1963;

Sataloff, Vassallo and Menduke , 1965; Harris, 1977;

Hamernik, 1980; Melnick, 1984). The reported lack of agreement in TTS/PTS data for individuals may relate to several factors. There is evidence that the underlying cochlear processes may be different (Miller, 1963) 9

Liberman and Mulroy (1982) compared acute noise-

induced cochlear morphologic change with chronic changes

in cats that were sacrificed within a few hours of exposure

or one month to two years following exposure. The animals

were matched in terms of degree of threshold shift as

measured with single-auditory-nerve-fiber tuning curves.

The chronically exposed animals (assumed to have suffered

PTS ) demonstrated various degrees of stereocilia disarray,

fusion, and loss as well as some total destruction of

sensory hair cells. The acutely exposed animals (assumed

to have suffered TTS) were found to have micropathological

changes in the neuropil at the synaptic pole of the hair

cells (in and around the sensory cell base) with no observed

change to the stereocilia. Ward (1973; 1980a; 1980b) has

repeatedly pointed out potential differences in the

physiological bases for TTS and PTS particularly when the

time over which PTS occurs is considered.

The argument that TTS data cannot be used to directly

predict PTS data (for individuals) can be acknowledged without invalidating the need to gather further information

about both these physiologic events for at least two primary reasons. First, TTS is the process feasible for

studies involving human subjects. Second, although probably

anatomically and biochemically different, the extent to which both processes occur is dependent upon the sound pressure level reaching the cochlea. Factors that control 10

the duration, spectrum and intensity of that damaging

sound pressure may be the same in both cases. Consequently, efforts to predict TTS may provide useful information

regarding the processes underlying PTS and may ultimately

lead to procedures that do allow the prediction of both phenomena.

Measuring Temporary Threshold Shift

Observation of TTS involves the inspection of data obtained prior to and following exposure to noise. In animal studies direct physiological indices of reduced

auditory sensitivity (e.g., cochlear microphonic and single-unit auditory nerve fiber responses) can be monitored throughout the entire TTS process. Chronically

implanted transtympanic or intracochlear electrodes

facilitate such measurements and allow a comprehensive

inspection of the growth and resolution of TTS over time

(Wever and Lawrence, 1955; Carder and Miller, 1972;

Gerhardt, Melnick and Ferraro, 1979, 1980; Liberman and

Mulroy, 1982).

In humans the detailed investigation of TTS is

limited, by comparison, for ethical and practical reasons.

A measure of behavioral threshold shift must usually be employed rather than the direct monitoring of a physiologic 11

event. This places constraints upon the amount of informa-

tion that can be recorded over time. It has been argued

that human TTS data should also be measured at intervals

over the entire growth and recovery time course. The

obtained data can then be mathematically expressed as

Integrated Temporary Threshold Shift (Kraak, 1978; 1981).

Such a procedure involves extensive subject contact and

continued incentives for participation. The subject's

voluntary response must be repeatedly elicited and recorded.

Many factors such as subject response time, subject fatigue

and subject availability preclude the hour-by-hour inspec-

tion of resolving TTS. However, this comparative inability

to realistically gather time- locked TTS information can be balanced by less comprehensive monitoring that does allow

larger subject numbers.

A time-locked response characteristic that must be

controlled for in TTS observations is the early polyphasic

recovery pattern shown (Hirsh and Ward, 1952) . Within the

first two post-exposure minutes a complex phenomenon

occurs. As shown in Figure 1, there is an immediate

rapid recovery of sensitivity, followed by a short increase

in TTS and then, a return to a pattern that is monotonic

(Elliott and Fraser, 1970; Ward, 1973). This "bounce" in

the TTS recovery pattern relates to the short-term effect

of auditory adaptation or sensitization. TTS involves .

Figure 1. Polyphasic recovery pattern from temporary threshold shift (adapted from Hirsh and Ward, 1952) .

processes of auditory fatigue that extend well beyond the cessation of the fatigue producing stimulus (noise)

Adaptation is a perstimulus phenomenon with only very short post-exposure effect. To preclude measuring this initial effect of adaptation, testing must be delayed until two minutes have elapsed after cessation of the noise. The term TTS has been adopted to refer to such measurements 2 and is widely used as a uniform reference to express the magnitude of auditory fatigue (Melnick, 1984).

In order to maximize the amount of information gained per unit of testing time it is necessary to consider the spatial distribution of TTS to be expected from exposure to a particular noise. The spectrum, intensity and duration of the fatigue producing noise have a combined effect upon the pattern of TTS that results. For exposure intensities common to industrial settings and typical of human experi- mentation (85 to 105 dB SPL) the audiometric frequencies one-half to one octave above the exposure frequency are most effected (Dishoeck, 1948; Hirsh and Bilger, 1955;

Hood, Poole and Freedman, 1976; Melnick, 1984). For exposure to broad-band noise this results in maximum TTS for those frequencies in the 3.0 to 6.0 kHz range

(Environmental Protection Agency, 1971; Ward, Cushing and

Burns, 1976; Mills, Adkins and Gilbert, 1981; Hepler, 1984).

For exposures to filtered bands of noise the maximum TTS .

frequency range can be predicted based upon the center frequency of the particular noise band. Such calculations can then be used to limit audiometric protocols to the frequencies that will be involved in TTS for the specific exposure (Ward, Glorig and Sklar, 1958; Melnick, 1974;

Zakrisson, 1975; Mills, Gilbert and Adkins, 1979; Kraak,

1981) .

An overriding consideration in studies of TTS is that the exposure intensity and duration be limited to a level that will not result in permanent hearing loss. As previously outlined, the lack of a direct relation between TTS and PTS makes the prediction of either difficult. However, Ward,

Glorig and Sklar (1959) suggested that "moderate" levels of exposure (e.g., 95 dB SPL) in which TTS (at any given fre- 2 quency) does not exceed 40 dB (SPL) will not result in PTS.

Exposure levels of 80 to 105 dB SPL with durations of less than eight hours result in TTS patterns of growth that are 2 linear with increasing intensity (Melnick, 1979) . Duration of exposure and intensity of exposure have been considered together in establishing hearing damage risk criteria for industry (Occupational Safety and Health Administration, 1983)

TTS growth is more rapid for high frequency exposures and, in general, the higher the exposure frequency the greater

the TTS that will result (Ward, 1973) . However, it has also been documented that the recovery from TTS at low audiometric frequencies (below 1.0 kHz) is slower than .

that for TTS at higher frequencies (Hamernick, Henderson,

Coling and Slepecky, 1980).

By considering the "effective quiet" level (at which

no shift in sensitivity can be expected regardless of

exposure duration) maximum TTS can be predicted for various

exposure frequencies (Ward, Cushing and Burns, 1976).

Melnick (1978) used this concept to show that approximately

25 dB of TTS can be predicted from a two-hour, low-frequency,

octave-band exposure. An equivalent two-hour exposure to

a high-frequency, octave-band noise would be expected to

cause 32 dB of TTS.

The current investigation involved two separate two-

hour exposures to a 90 dB SPL octave-band noise for each

subject. Complete recovery from TTS caused by one exposure

is thought to indicate that similar exposures can occur without an additive effect. There have been several

investigations involving octave-band noise exposure at

higher intensities and for longer time periods. Gerhardt

and Hepler (1983) and Patterson (1977) reported complete

recovery from TTS in studies where subjects were exposed

for four hours to a 95 dB SPL octave-band noise centered at

1.0 kHz. Octave-band exposures for periods of 16 to 24 hours at similar (90 to 95 dB SPL) exposure levels have not resulted in any permanent hearing damage (Melnick and

Maves , 1974; Melnick, 1974; Mills, Gilbert and Adkins,

1979) .

The Acoustic Reflex

The peripheral auditory system has three functional divisions: the outer, middle and inner ears. The outer ear provides slight amplification of the sound energy that it directs to the tympanic membrane. The middle ear is primarily an impedance matching mechanism. It provides a mechanical interface between the outer and inner ear structures. This interface is necessary to allow a transfer of acoustic energy between the medium of air in the outer ear and that of fluid in the inner. Much more energy is required to propagate sound through a fluid.

The middle ear selectively provides increases in sound intensity of up to 1,000 to 1 to overcome this mismatch of impedances (Yost and Nielsen, 1977; Pickles, 1982).

Physical properties of the middle ear structures

(inertia, elasticity and mass) determine the range of frequencies that can most effectively be transferred to the inner ear. Overall, there is a relative enhancement of the mid-frequency components of a given acoustic signal.

Frequencies in the low and high spectral ranges are not as efficiently transferred (Yost and Nielsen, 1977)

Factors that alter the physical properties of the middle ear (such as the effusion of fluid into the middle-ear space in certain pathologies, or the elicitation of the AR by intense acoustic stimulation) reduce the efficiency of transfer and lower the sound pressure energy reaching the .

inner ear (Moller, I960, 1974; Wofford, 1981; Pickles,

1982; Moller, 1984; Borg, Counter and Rosier, 1984).

An articulated chain of three bones spans the middle- ear space and carries acoustical vibrations from the tympanic membrane to the oval window. Elicitation of the

AR causes the contraction of two striated muscles which attach to this ossicular chain. Both these muscles are encapsulated in such a way that their contraction exerts tension without causing extensive displacement of the ossicles. The tensor tympani attaches to the upper end of the manubrium of the malleus. It is encased by the bony canal of the eustachian tube. The stapedius attaches to a tendon that holds the stapes in position within the middle ear. The tendon is attached to a muscular process at the neck of the stapes. The stapedius is completely encased in a bony canal adjoining that of the facial nerve

(Zemlin, 1981)

Both middle ear muscles contract when the AR occurs and they do so bilaterally. The effect of this contraction is a reduction in the elasticity of the middle ear mechanism. The tensor tympani exerts a medial and anterior tension on the manubrium, thus increasing the stiffness of the tympanic membrane. The stapedius exerts a lateral tension on the stapes footplate, stiffening the entire ossicular chain. The vibratory patterns of both the tympanic membrane and the stapes footplate are altered in .

addition to the increased stiffness property (Moller, 1960;

Dallos , 1964; Moller, 1974; Zemlin, 1981). In man the stapedius muscle has been shown to contribute the most

1967). to the AR ( Jepsen , 1955; Klockhoff, 1961; Djupesland,

According to Borg, Counter and Rosier (1984) , the neuroanatomy of the AR was relatively unknown until the late 1800s when Politzer first demonstrated that the tensor tympani was innervated by the trigeminal nerve and the stapedius by the facial nerve. The AR pathway has since been studied mainly in rabbits (Borg, 1973; Moller,

1984) . Such experiments involved creating lesions at various points in the brainstem and observing changes in the AR. The pathway is known only to the extent that generalizations based on these animal data can be made.

In response to acoustic stimulation this muscle reflex arc for the stapedius is thought to comprise a three to four neuron net. Impulses from cochlear hair cells are carried in the auditory nerve to the ventral cochlear nucleus. A second neuron passes through the trapezoidal body to directly contact with ipsilateral stapedius-muscle motor neurons in the motor nucleus of the facial nerve.

Interneurons in the medial superior olive relay the impulse to the contralateral facial nerve. The pathway for the tensor tympani is similar up to the level of the trapazoidal body. A motor neuron then follows the mandibular branch of the trigeminal nerve to the middle ear (Borg, 1973) .

The above pathway is considered to be a secure, relatively short latency reflex arc. As with other brainstem-mediated reflexive functions there may be numerous less secure, longer, multisynaptic pathways involving the extrapyramidal system or reticular formation. The extent or purpose of such additional AR pathways is unknown at this time (Courville, 1966; Borg, 1973; Borg and Moller,

1975; Moller, 1984). However, there are important secondary pathways that are partially known. Tactile stimulation of facial areas and even facial movement in anticipation of noise can result in contraction of both middle ear muscles.

This probably results from a feedback loop involving the trigeminal nerve. Its presence has obvious experimental control implications. An experimenter must be certain that an observed response was actually elicited by acoustic energy and not some other stimulus (Dallos, 1964; Klockhoff,

1961; Djupesland, 1967; Zakrisson, 1974)

Measuring the Acoustic Reflex

Contraction of the middle-ear muscles, as elicited by acoustic stimuli, alters the signal transmitted to the inner ear. In general, a reduction in sound pressure at the stapes footplate occurs. Until relatively recently contraction of these muscles was directly observed or ,

recorded using invasive techniques (Moller, 1984) . In

animals, and some human subjects, the tension of the muscles

has been directly measured (Lorente de No, 1935) . Electro-

myographic recordings of muscle-fiber discharge rates have

also been used to study this event (Perlman and Case, 1939;

Fisch and Schulthess, 1963; Borg and Zakrisson, 1975).

Recordings of the cochlear microphonic potential have been

accomplished to infer the AR through time-locked observa-

tions of changes in cochlear output (Galambos and Rupert,

1959; Price, 1963; McPherson and Thompson, 1977; Gerhardt

Melnick and Ferraro, 1979, 1980).

For the past 10 to 15 years techniques for non-

invasive measurement of the flow of acoustic energy at the

ear canal or adjacent to the lateral surface of the tympanic

membrane have been developed, debated, and refined (Feldman

and Wilber, 1976; Wilber, 1976; Moller, 1984). Basic

science investigators designed equipment to facilitate

their particular research efforts. The commercially built

units that evolved for clinical and laboratory use have

lacked concensus as to the proper method of measurement

and descriptive terminology to be used (Moller, 19 84;

Popelka, 1984). However, most available instruments

utilize a probe assembly that must be sealed into the ear

canal. This probe contains a sound source that is used to

introduce a calibrated signal referred to as the probe tone.

A microphone in the probe monitors the resultant sound 21

pressure level in the ear canal. Changes in the transmission

of energy through the middle ear, caused by the AR, alter

the measured sound pressure. This change can be observed

as a meter deflection (as is often done clinically) or

the corresponding meter circuit voltage change can be

displayed graphically for analysis (Wilber, 1976; Block and

Wiley, 19 79) .

Measurements accomplished using instruments designed

to record units of acoustic impedance describe relative

resistance to energy flow through the middle ear. Measure-

ments of acoustic admittance (the reciprocal of impedance)

describe the relative ease with which energy is transmitted.

Acoustic immittance is a generic term that encompasses

both these properties (Popelka, 1984) . Professional

debate over the merit of the two basic forms of immittance

measurement resulted in two somewhat separate bodies of

literature. This has complicated comparisons of reported

experimental results. Results reported in terms of one

acoustic immittance measure can be converted arithmetically

to the other. However, there are subcomponent values that must be included in such calculations. These values are often not available to the reader (Block and Wiley, 1979).

Therefore, the preponderance of recent experimental data on the AR has been reported in terms of acoustic admittance and its subcomponents, susceptance and conductance. The admittance instruments most widely used in current research .

have been designed to allow the monitoring of these values

(Block and Wightman, 1977; Wilson and McBride, 1978;

Wilson, Steckler and Jones, 1978; Block and Wiley, 1979;

Hepler, 1984; Fowler and Wilson, 1984)

A clinical adjunct to the measurement of the AR is the tympanogram. Tympanometry defines variations in acoustic immittance as air pressure in the ear canal is varied systematically (Feldman and Wilber, 1976; Popelka,

1984; Shanks, 1984). Tvmpanometric data is of importance in AR research applications for at least two reasons.

First, experimental subjects should exhibit normal middle ear function prior to AR measurements. Tympanometric data outside the well defined range of clinical normalcy should be used to exclude subjects with potential middle-ear system abnormality (Jerger, 1975). Second, it is necessary to arithmetically account for the contribution of the immittance of the volume of air between the probe assembly and the tympanic membrane. This can be accomplished for each subject using tympanometric peak and minimum immittance data. Subtraction of minimum immittance

(measured at a pressure of -300 daPa) from peak (generally measured at ambient pressure) results in the immittance at the lateral surface of the tympanic membrane (Block and

Wiley, 1979; Popelka, 1984). 23

Acoustic Reflex Threshold

Threshold values for physiological response variables

are usually reported in terms of the stimulus level required

to elicit the response at some level. Acoustic reflex

threshold is most commonly defined as the sound intensity

(expressed in decibels) required to elicit a measurable

change in the acoustic immittance of the middle ear (McPherson

and Thompson, 1977). In most AR research, threshold judgments

are accomplished by monitoring ongoing immittance baseline activity and identifying descernible time-locked departures

from that baseline (Popelka, Margolis and Wiley, 1976; Wilson and McBride, 1978) . If these judgments are made visually from tracings on an oscilloscope or recorder, the baseline activity must be free enough from noise that a potential response is not obscured. This method has advantages over clinical definitions of the AR threshold as a "meter deflection." However, both methods leave room for experimenter bias and error.

Various authors have proposed alternative approaches to AR threshold measurement. For example, the presentation level required to shift measured immittance from baseline to 10% of the maximum shift obtained for a given elicitor can be calculated and labeled as threshold (Moller, 1962;

Borg, 1972) . A similar technique that reduces experimenter error was proposed by Block and Wiley (1979) and used by

Hepler (1984) . 24

With the aid of a computer, digitized immittance waveforms can be analyzed in terms of a "criterion magnitude." Average baseline immittance (pre-elicitation) is calculated for each presentation. Then, an average

immittance value is calculated for the time period of greatest AR contraction. Threshold is defined as the

lowest elicitation level (in dB SPL) that causes an

immittance shift from baseline of more than two standard

deviations for two out of three presentations. There are

no reported differences in AR threshold searches that use

ascending elicitation level increments and those that use

descending trials (Lilly and Franzen, 1970; Peterson and

Linden, 1972; Wilson, 1979).

The AR occurs bilaterally in response to either

monaural or binaural stimulation. Ipsilateral AR threshold

values are reported to be approximately 5 dB (SPL) lower

on the average than those measured contralaterally (Moller,

1961, 1962; Zakrisson, 1974). While this precludes

comparisons of such data without the application of a

correction factor, it does not suggest that one elicitation

paradigm is superior to the other. The use of externally

generated and controlled elicitation stimuli in conjunction

with commercially available immittance instruments

predisposes the experimenter to contralateral AR measure-

ments in most cases. 25

Acoustic reflex threshold varies according to the spectral content of the elicitor. This has been attributed primarily to the frequency selectivity of the basilar membrane (Moller, 1962; Flottorp, Djupesland and Winther,

1971; Djupesland and Zwislocki, 1973; Popelka, Margolis and

Wiley, 1976; Silman, Popelka and Gelfand, 1978; Gorga,

Lilly and Lenth, 1980; Margolis, Dubno and Wilson, 1980).

The central auditory system also exhibits spectral integration. The AR pathway is the same as the ascending auditory pathway up to the level of the superior olive. Therefore, spectral integration of AR elicitation signals is thought to also occur beyond the cochlea. Many superior olivary neurons impinge upon each facial motorneuron involved in the AR. The range of frequencies over 'which energy is integrated may, therefore, be broader than that for the

auditory system per se (Moller, 1984) .

The term "critical band" has been used in descriptions of AR spectral integration. Lower AR thresholds are elicited with filtered bands of noise than with the tonal center frequencies of those bands (Moller, 1962). However, the addition of energy outside a particular bandwidth does not increase the efficiency of that noise in eliciting the AR (Djupesland and Zwislocki, 1973; Popelka, Margolis and Wiley, 1976). The addition of acoustic energy below

0.7 kHz or above 6.0 kHz does not appear to contribute to

AR sensitivity for a broad-band elicitor. Energy contained 26

at octave intervals within that range contributes equally to the efficiency of the elicitation stimulus. If a broad-band elicitor is successively narrowed by filtering, the threshold of the AR will increase 4 dB for each octave of energy removed from its total bandwidth.

The AR threshold is reported to be stable over time.

Test-retest variations of no more than 5 to 6 dB are found for subjects in trials separated by as much as one week

(Weiss, Mundie, Cashin, and Shinaburger, 1962; Jerger,

1972; Chun and Raff in, 1979; Chermak, Dengerink and

Dengerink, 1983). Acoustic reflex threshold occurs at a relatively constant sensation level (re auditory behavioral

threshold) for pure tone elicitors, with the exception of

2.0 and 4.0 kHz. Many normal hearing subjects exhibit

elevated or absent AR responses at those frequencies for

as yet unexplained reasons (Dallos, 1973; Jerger, 1975;

Barry and Resnick, 1976). The average reported range of

contralaterally elicited AR thresholds for pure tones in

normal hearing adults is 85 to 100 dB SPL. Broad-band

elicitation results in thresholds approximately 20 dB lower

(Metz, 1952; Jepsen, 1963; Dallos, 1964; Jerger, Jerger

and Mauldin, 1972; Kaplan, Gilman and Dirks, 1977; Hepler,

1984; Gelf and, 1984) .

Acoustic reflex threshold may be elicited at succes-

sively lower intensities with advancing age, but this

effect is only shown to be significant in comparisons of 27

subjects across broad age range groupings (Jerger, Jerger

and Mauldin, 1972; Osterhammel and Osterhammel , 1979;

Silverman, Silman and Miller, 1983) . These same authors reported no sex differences in AR threshold data.

Acoustic Reflex Magnitude

Silman (1984) defines AR magnitude as the amount of immittance change (as measured at the lateral surface of the tympanic membrane) that occurs due to AR elicitation.

Magnitude of the AR is directly related to elicitation stimulus intensity, frequency and duration (Moller, 1962;

Djupesland, Flottorp and Winther, 1967; Habner and Snyder,

1974; Wilson and McBride, 1978). This aspect of the AR is usually discussed in terms of AR growth functions. Such functions describe the range of elicitor intensities over which AR magnitude increases before reaching a saturation level. The AR magnitude dynamic range (threshold to saturation) covers approximately 30 dB for tonal elicitors and

50 dB for broad-band noise (Dallos, 1964; Block and Wightman,

1977; Wilson and McBride, 1978; Zito and Roberto, 1980;

Silman, 1984) . However, as Silman (1984) points out, the relative saturation level for various elicitors is not clearly defined due to inconsistencies in reported methodology and equipment limitations. In addition, many . .

subjects will not tolerate elicitor presentations at levels that would define the upper limits of their individual

AR growth functions

Acoustic reflex magnitude is greatest for broad-band noise elicitors and mid-frequency tonal elicitors (Wilson and

McBride, 1978; Wilson, 1981; Silman, 1984) . Several investigators have reported that subjects with relatively large static (resting/non- reflexive state) immittance values also exhibit large AR magnitudes (Terkildsen,

Osterhammel and Nielsen, 1970; Lutman and Martin, 1977;

Silman and Gelfand, 1981; Wilson, 1981; Hepler, 1984).

Hepler (1984) also noted that AR magnitude was consistent for individual subjects across elicitors. Subjects who exhibited relatively large AR magnitudes for a broad- band noise elicitor were also shown to have large magnitude values for tonal elicitors.

Acoustic reflex magnitude growth functions can be plotted in terms of elicitor sensation level (re AR threshold) in decibels. The slope of growth functions for various elicitation signals can then be discussed. Acoustic reflex magnitude slope values for broad-band elicitor data are reported to be shallower than those for tonal elicitors.

Slope values for filtered noise elicitors are reportedly steeper than those for pure tones (Wilson and McBride,

1978; Wilson, 1981; Silman, 1984) .

Acoustic reflex magnitude growth functions have been

shown to vary according to subject age. The relative growth

in AR magnitude decreases as age increases. However, as

with AR threshold data, age may only be a significant factor

when comparing non-homogeneous groups of subjects who are

widely distributed as to age (Silman, 1984)

Acoustic Reflex Latency

Onset latency is a common temporal descriptor of

physiological responses. Acoustic reflex latency is

usually defined as the time of the first detectable

immittance change resulting from elicitation of the AR

(Bosatra, Russolo and Silverman, 1984) . As with AR

threshold measurements, this definition encompasses

several actual measurement techniques. Feldman (1976)

provides an explanation of the technical problems encountered

in AR latency measurements. The actual point in time after

elicitation stimulus onset that a change from immittance

baseline occurs is open to measurement error. Noise in

baseline activity that may obscure valid latency observa-

tions leads most investigators to arbitrary determinations

of latency at points above the noise floor. Various

protocols have been reported with latency calculated as

the time between stimulus onset and the point where the

AR reaches 5%, 10% or even 50% of its maximum amplitude .

(Moller, 1972; Borg, 1972; Colletti, 1974; McPherson and

Thompson, 1977; Clemis and Sarno, 1980; Church and Cudahy,

1984) .

Another important AR latency measurement parameter is elicitation stimulus rise time (McPherson and Thompson,

1977) . Shorter latency values are reported as elicitor rise time is decreased. Stimulus rise times of 2 to 10 msec (to 90% of maximum stimulus amplitude) are acceptable for most measurements (Feldman, 1976; Bosatra, Russalo and Silverman, 1984)

The time constant of the immittance measurement device is a similarly confounding parameter. This determines the relative speed with which the instrument can follow a response without the creation of artifact (Feldman, 1976;

McPherson and Thompson, 1977). Early investigations involving electroacoustic impedance bridges resulted in longer reported latency values due to this interaction.

Bosatra, Russalo and Silverman (1984) caution that some latency measurement error may result even with contemporary instruments. Subjects with uniquely slow AR response rise times may have obscured onset latency points. That is, even with an operationally defined index of 50% of maximum response amplitude, the response growth to that point may confound precise measurement.

Latency is inversely related to elicitation stimulus intensity. The more intense the elicitor, the .

shorter the measured onset latency will be (Borg, 1972;

Dallos, 1973; Church and Cudahy, 1984; Bosatra, Russolo

and Silverman, 1984) . Latency values are normally reported and discussed in terms of sensation level of the elicitor

(re AR threshold) . Mean reported values for young adults

(18 to 30 years of age) range from 150 to 250 msec at or near threshold. Reported latencies at sensation levels of

30 to 40 dB range from 25 to 40 msec (Dallos, 1964;

Moller, 1984). It may also be illustrative to plot AR latency growth functions in a manner similar to that previously discussed for AR magnitude.

Elicitation stimulus frequency is reportedly a factor in the level and stability of AR latency. However, there is no reported consensus on this factor, except that tonal elicitors are recommended for clinical measurements

(Bosatra, Russolo and Silverman, 1984)

Acoustic Reflex Adaptation

If the AR elicitation stimulus is sustained over a period of time, then response adaptation occurs. The middle-ear muscles gradually relax and acoustic immittance returns to its prestimulus level (Fowler and Wilson, 1984).

The rate of this adaptation appears to be dependent upon the intensity and frequency of the sustained elicitor

(Metz, 1951; Dallos, 1964; Johansson, Kylin and Langfv, 32

1967; Habner and Snyder, 1974; Holmes, 1978; Borg and

Odman, 1979; Wilson, Shanks and Lilly, 1984). The perstimulus reduction in the AR is normally expressed in units of time that elapse until a decrease to 50% of maximum AR amplitude has occurred (Anderson, Barr and

Wedenberg, 1970; McPherson and Thompson, 1977). A second method is to express the percentage of maximum reflex magnitude at specified time intervals in reference to the elicitation signal onset (Wilson, Shanks and Lilly,

1984) .

The physiological explanation for AR adaptation is unclear. However, several factors suggest that it is related to the cochlea and the ascending portion of the auditory nerve pathway rather than to a mechanism involving muscle fiber fatigue. In normal hearing subjects AR adaptation increases as a function of elicitation frequency

(above 2.0 kHz) and very little adaptation to low frequency elicitors (below 1.0 kHz) is reported. This finding is contrary to that expected with muscle fiber fatigue.

Clinically, AR adaptation is one of the most positive diagnostic indices of auditory nerve pathway pathology. In that context, the amount of adaptation appears to be inversely related to the number of auditory nerve fibers remaining intact to carry the ascending impulses (Wilson,

Steckler and Jones, 1978; Fowler and Wilson, 1984; Wilson, .

McCullough and Lilly, 1984; Wilson, Shanks and Lilly,

1984)

Studies comparing noise and tonal elicitors indicate that the AR is sustained over a longer time period in response to noise. The rate at which adaptation occurs

(slope) is also less rapid with noise as compared to tonal elicitation. Low-frequency filtered noise has been reported to sustain the response longer than high-frequency filtered noise. These findings may all be related to the relatively random nature of noise stimuli which may serve to continually re-elicit the AR. The frequency specificity found with noise (and tonal) elicitors may relate to the capacity of the inner ear to respond to low versus high frequency stimulation. It is known, for example, that a phase locking of responses occurs predominately with low frequency stimuli. This may be a factor in the AR as well

(Ward, 1961, 1973; Djupesland, Flottorp and Winther, 1966;

Wilson, McCullough and Lilly, 1984).

The effect of elicitation stimulus intensity on AR adaptation is not clear. There are many inconsistencies in the results reported for studies of this type. Wilson,

Shanks and Lilly (1984) summarized this research and concluded that two trends seem to be shown. First, AR adaptation may decrease as the intensity of a low-frequency elicitor is increased. Second, for elicitation frequencies of 2.0 kHz or higher there does not appear to be a .

systematic relation between elicitor intensity and AR adaptation.

Most studies have shown that adaptation (to 50% of maximum AR amplitude) occurs well within 60 seconds of sustained stimulation in normal hearing subjects. This finding is reported for tonal elicitors above 0.5 kHz and is usually measured at elicitation levels of 10 dB in reference to AR threshold. Similar data for broad-band noise and tonal elicitors of 0.5 kHz and below indicates

that AR amplitude may be maintained above a 50% level well beyond 60 seconds (Wilson, Shanks and Lilly, 1984)

Baseline drift is a factor that confounds measurement

of AR perstimulus adaptation. The acoustic immittance of

the middle-ear changes constantly due to the absorption of

air by the mucosa lining (Tonndorf and Khanna, 1986;

Wilson, Steckler and Jones, 1978). Corrections for this

baseline change must be incorporated into measurement

procedures. In addition, the intensity level most commonly

used to make AR adaptation measurements (10 dB above AR

threshold) was reportedly somewhat arbitrarily chosen.

That level is on the linear portion of the AR growth

function. However, Wilson, Shanks and Lilly (1984)

suggest that sustained elicitation levels of 15 or 20 dB

above AR threshold may provide a better description of AR

adaptation. .

The Acoustic Reflex and Temporary Threshold Shift

There have been many attempts to demonstrate the protective function of the AR. An extensive body of

literature has developed since the early 1800s involving research on animals with experimentally inhibited AR

function (Borg, Counter and Rosier, 1984) . Somewhat

similar, but comparably limited, research has been done in humans. For example. Bell's palsy patients exhibit uni-

lateral facial nerve paralysis and thus can only have an

AR related immittance change in the ear contralateral to the damaged nerve. Short term (less than 10 minute) exposures of such subjects to filtered noise have resulted in confirmation of the low-frequency attenuation provided by the AR. Temporary threshold shift was greater in the affected than in the non-affected ear when the exposure was to low-frequency noise, centered at 0.5 kHz. There was no statistical difference in the TTS that resulted in the affected versus the non-affected ear following a high- frequency exposure, centered at 2.0 kHz (Azkrisson, 1974,

1975) . However, a similar comparison of stapedectomized patients with a control group having intact middle ears showed no significant differences in the TTS caused by 10 minute exposures to a 1.2 kHz filtered noise at 110 dB SPL

(Fletcher and King, 1963)

Karlovich, Lutterman and Abbs (1972) devised an experimental paradigm in which normal hearing subjects 36

were exposed to noise in one ear while AR eliciting stimuli were presented to the other ear. This dichotic presentation

arrangement assured that the AR repeatedly occured throughout two separate three-minute exposures to 1.0 kHz (110 dB

SPL) and 4.0 kHz (100 dB SPL) pure tone stimuli. A control

group of subjects had similar exposures but did not have

their AR artificially stimulated. There was a significant decrease in TTS in the experimental group as compared to

the control group following the 1.0 kHz exposure. Temporary threshold shift was not significantly different between the groups for the 4.0 kHz exposure condition.

The somewhat contrived or artificial nature of experiments such as those described above has been argued by many authors. Various theories about middle-ear function have been expressed that minimize the protective role of the AR. Such theorists have offered cogent explanations for the evolution of a human middle ear that can modulate input to the inner ear for reasons other than protection from overstimulation. Most of these authors have criticized the AR protection theory based upon the relatively recent advent of noise in man's environment at levels that damage hearing. Borg, Counter and Bosler

(1984) summarized this debate and reviewed interspecies similarities and differences as to the evolution and function of the middle ear. Their review suggests that the human middle ear evolved (as it did in other terrestrial 37

animals) to increase hearing sensitivity for faint, significant sounds such as approaching predators or fleeing prey. However, this increased sensitivity (provided by the middle-ear impedance matching) creates a problem in the handling of intense internal sounds (such as vocalization or mastication) as well as intense external sounds. The

AR provides the attenuation necessary to separate relevant from irrelevant sounds. It also prevents overstimulation from interferring with the reception of sounds necessary for communication.

While the AR admittedly did not evolve exclusively to deal with man's 20th century noise environment, it certainly does have a modulating effect on that environment. Temporary threshold shift is directly related to the sound pressure levels that reach the inner ear. Acoustic stimulation at levels that will result in TTS also causes the reflexive contraction of the middle-ear muscles. While the muscles are contracted in the AR, the transmission of sound energy to the inner ear is reduced. The reduction can be as much as 20 dB SPL for frequencies below 1.0 kHz (Ward, 1965;

Borg, 1968; Cancura, 1970; Dallos, 1973; Moller, 1974; Borg,

Nilsson and Liden, 1979; Nilsson, Borg and Liden, 1980).

The effect of the AR on sound energy transmission is not static. That is, properties of the AR are directly related to the duration and spectrum of the acoustic .

stimulation (noise) and also vary in individuals. These

"dynamic" properties of the AR include threshold, magnitude, latency and perstimulus adaptation. The extent to which these properties interact with and modify the sound energy transmitted through the ear determines the protective influence of the AR (Gerhardt and Hepler, 1983)

Several studies involving normal hearing human subjects have supported a relation between dynamic properties of the

AR and the TTS that results from noise exposure. Johansson,

Kylin and Langfy (1967) found TTS following 15 minute exposures to bands of noise (0.3 to 1.2 kHz at 110 dB SPL) to be significantly correlated to AR latency. Holmes (1978) reported a significant negative correlation between AR maximum contraction strength (magnitude) and the TTS caused by exposure to white (broad-band) noise for 10 minutes at 115 dB

SPL. Acoustic reflex contraction strength was calculated in response to pure tone elicitors presented at 10 dB above AR threshold. Subjects identified as having a more vigorous response to the 2.0 kHz elicitor also exhibited less TTS.

Gerhardt and Hepler (1983) found a similar AR magnitude/TTS correlation in subjects following a much longer noise exposure (four hours at 90 dB SPL) to a 1.0 kHz octave-band noise. Temporary threshold shift from that exposure was greatest at 1.4 kHz and the TTS at that frequency was negatively correlated to AR magnitude as elicited with a similar pure tone. Hepler (1984) found a significant 39

relation between AR magnitude as elicited with broad-band noise and the TTS that followed a two-hour exposure to that same noise at 90 dB SPL.

The reportedly rapid perstimulus adaptation of the

AR has presented a major obstacle to the concept that the

AR may be a factor in TTS. However, there are data indicating that the AR may not adapt equally for all noise exposures. The effect of the AR may be sustained longer than most measurement paradigms have indicated and it may be sustained through re-elicitations due to irregularities in the time course of industrial noise (Bjaevenes and

Sohel, 1966; Borg and Odman, 1979; Borg, Nilsson and Liden,

1979; Borg and Nilsson, 1984).

Experiments on normal hearing subjects exposed to 30 minutes of recorded shipyard noise at 97 dB showed that the AR was active throughout the period with only a slight reduction in magnitude (Nilsson, Borg and Liden,

1980) . In actual industrial noise environments the AR is most likely active throughout the workday.

From a different perspective, a measure of AR adaptation may be an important predictive variable for TTS.

Holmes (1978) tracked AR response level for eight minutes for both 0.5 and 2.0 kHz elicitors (at a presentation level 10 dB above AR threshold). For the 0.5 kHz elicitor, the response decreased 34.7%. The reduction for the 2.0 kHz elicitor was 75.7%. While AR magnitude was greater 40

for the high frequency elicitor, its rapid and relatively complete adaptation may actually have reduced its effective- ness. Hepler (1984) also reported that AR adaptation (as measured over 7 1/2 minutes) was significantly correlated

to "Total TTS " (i.e., the sum of TTS at all test frequencies).

Inclusion of an adaptation variable in a step-wise regression modeling procedure added significantly to the prediction of TTS combined at 1.0, 2.0 and 3.0 kHz as well.

Susceptibility to Noise

Individual susceptibility to noise is highly variable.

In controlled laboratory experiments, this variability is

manifested in the vastly different TTS exhibited by

individuals following exactly the same noise exposure.

Similarly wide variation is shown in the PTS suffered by

individuals following occupational exposures assumed to

have been the same over several years. No single compre-

hensive index of susceptibility, to either TTS or PTS,

has been demonstrated. Studies relating dynamic features

of the AR to TTS have the short-term goal of identifying

which feature (s) predict TTS. However, such features will,

at best, account for only a portion of the observed

variation in TTS. Thus, the long-term goal of such

investigations must be to integrate the information . ,

gained about potential middle-ear protection quality in individuals with the results of other studies that address potential indices of inner-ear, outer-ear and non-auditory protective factors (Hepler, Moul and Gerhardt, 1984; Borg and Nilsson, 1984)

Various authors have proposed individual tests of

inner-ear function as it relates to TTS . Procedures based on the concept of individual variation in inner-ear distortion level will probably be included in any ultimate battery of tests for susceptibility (Lawrence and Blanchard,

1954; Clack and Bess, 1969; Humes, 1977, 1978, 1980; Humes,

Schwartz and Bess, 1977; Humes and Bess, 1977). Other potentially valuable tests measure slight changes in the ability of individuals to discriminate between speech

stimuli or to make judgments as to the relative loudness

of various sounds (Michael and Bienvenue, 1976; Bienvenue,

Violon-Singer and Michael, 1977) . The sensitivity of the

inner ear for frequencies beyond the normal audiometric

range (10 to 16 kHz) is another area of interest. Ultra-

audiometrics have been discussed in terms of prediction of

susceptibility and the potential for monitoring early noise-

induced changes that may normally go undetected (Sataloff,

Vassallo, and Menduke, 1967; Flottorp, 1973; Osterhammel,

1979; Erickson, Fausti, Frey and Rappaport , 1980; Fausti

Erickson, Frey, Rappaport and Schecter, 1981; Collins,

Cullin and Berlin, 1981). ,

An inverse relation has been reported between the pre-exposure behavioral auditory thresholds of individuals and TTS. That is, individuals with more sensitive hearing at a particular test frequency apparently exhibit more TTS for that frequency following noise exposure (Ward, 1965;

Davis and Ahroon, 1972; Hepler, 1984).

Physical differences in the outer ear dimensions of individuals may contribute to their susceptibility to noise-induced auditory changes at specific frequencies.

Outer-ear resonance characteristics change with the length of the ear canal and are thus not the same for all individuals.

To the extent that the resonance of the ear canal influences the intensity and spectral content of the signal reaching the inner ear, this individual physical difference may contribute to susceptibility (Miller, Watson and Covell,

1963; Shaw, 1971; Tonndorft, 1976; Caiazzo and Tonndorf

1977) . The volume of the ear canal can be estimated from static immittance measurements. Hepler (1984) noticed a difference in the frequency of maximum TTS for subjects grouped according to ear canal volume. Subjects with TTS centered at 3.0 kHz had significantly larger volume measurements than those subjects with TTS centered at 6.0 kHz.

Since volume of the ear canal relates directly to length, and thus to resonance, the implication was. that the individual variation in this outer-ear characteristic contributed to individual variation in TTS. .

Non-auditory indices may identify individuals who are

predisposed to TTS . For example, there have been strong

correlations reported between eye color and susceptibility

to TTS (Hood, Poole and Freedman, 1976; Carter, 1980;

Carlin and McCroskey, 1980; Cunningham and Norris, 1982).

Pigmentation of both the iris of the eye and the stria

vascularis of the cochlea involves the substance melanin

(Bonaccorsi, 1965) . Blue-eyed individuals lack this

pigmentation which may be crucial to cellular metabolism

in the inner ear. This deficit may be a partial explana-

tion for the reported eye-color/TTS correlation.

Vascular factors are known to be involved in several

pathologies of the inner ear. The vascular integrity of

inner-ear structures or noise-induced changes in blood

flow to, and within, the cochlea could be complex factors

interacting to influence individual susceptibility. There

are two possible mechanisms for noise to influence inner-

ear blood vessels. Sound may (particularly at extreme

levels) mechanically or hydrodynamicallv influence cochlear

microcirculation. Secondly, there may be (even at moderate

levels) changes in the inner ear due to a general circulatory

response (Axelsson, Vertes and Miller, 1981)

The literature on direct measurement of cochlear

microcirculation and on the examination of cochlear tissue

for histopathology does not provide a concensus on the

effects of noise exposure. This lack of agreement is due. 44

perhaps, to the complex and varied methodology used to

assess changes in blood flow, most of which are not

feasible with human subjects. A second body of literature

is, therefore, developing on the assessment of general

circulatory responses to noise.

The relation of noise-induced changes in peripheral blood flow to changes in cochlear blood flow is not clear.

However, the key benefit to such research is that the methods used are applicable for human research and the measurements are relatively easy to obtain. For example,

it has been suggested that personality type (as a reflec-

tion of stress-prone behavior and vascular response) may

be correlated to TTS (Ickes and Nader, 1982) , Considerable

utility could be gained from simple paper and pencil tests

of factors such as personality, if a predictive relation

to TTS could be established. In a pilot study prior to

data collection for the present investigation, such an

analysis was done on the subjects from Hepler's (1984) broad-band noise exposure. Thirty-six college-age males

and females had been exposed for two hours at 90 dB SPL

in that investigation. The Jenkin's Activity Survey was

administered to each of those subjects. The survey is a multiple-choice, self-assessment inventory that was normed

for subjects in the college age range (18 to 25 years of age) . It has been reported to be a reliable index of

stress-prone characteristics and personality type (Glass, .

1977) . Correlational analysis between the results of this inventory and TTS did not reveal any statistically significant pattern (Moul, Gerhardt and Hepler, 1985)

The failure to correlate this easily measured, indirect index of vascular function to TTS should not be interpreted to suggest that other indices of vascular function may not be important measures of susceptibility.

Dengerink, Dengerink and Chermak (1982) suggest that stress-related measures of peripheral vasomotor activity, such as blood pressure and pulse amplitude may be clinically feasible indicators of susceptibility.

Other easily documented non-auditory factors have been shown to be related to TTS, such as subject age, sex and smoking history (Ward, 1980b; Thomas, Williams and Hoger,

1981; Chung, Willson, Gannon and Mason, 1982) . Age may be a factor only in broad comparisons across non-homogeneous groups. The apparent sex difference may be related to many factors including the issue of variations in ear-canal resonance with increased size discussed previously.

However, both age and sex data should be included in any analysis. The Chung, Willson, Gannon and Mason (1982) review of susceptibility factors provides a formula for calculating lifetime smoking history. Smoking may reduce the oxygen available to the inner ear and may thus be a factor in susceptibility to noise. 46

Statement of Purpose

Susceptibility to noise-induced change in auditory

sensitivity is a complex issue. There is an interaction of

factors that predisposes individuals to a lesser or greater

extent. The ultimate goal of research on susceptibility

to noise-induced hearing loss is to identify individuals who are uniquely at risk. The clinical tool that may

result from such research will probably be a comprehensive battery of tests. As is often the case in clinical diagnosis,

the results of one specific evaluation may be equivocal.

However, the combined results of several correlated

evaluations may present a much clearer impression. The key to the process is the definition of the most important pieces that must be gathered to efficiently construct the whole.

Individual variation in the protective function provided by the AR has not been clearly defined. The

relation of dynamic properties of the AR to the TTS that

individuals develop must be explored in studies that involve long-term exposures (hours) rather than short-

term exposures (minutes) . This study sought to further define the AR/TTS relationship. More detailed information was needed on the protective effect on the AR when the TTS producing noise is varied as to spectral content. The

following experimental question was asked. 47

Can measurements of acoustic reflex activity

(threshold, magnitude, latency and adaptation) be used to predict the TTS that develops in normal hearing subjects following two separate octave-band noise exposures (0.5 and 3.0 kHz)? CHAPTER II METHODS

Subjects

This study compared measures of acoustic reflex activity and the TTS that followed exposure to octave- band noise. Thirty subjects had two separate two-hour noise exposures. The noise was centered at 0.5 kHz for one exposure and at 3.0 kHz for the other exposure.

Subjects were between 18 and 34 years of age (mean age =

23) and were paid for their participation in the experiment.

The thirty subjects were selected from students and university staff members who met the following criteria:

1) normal hearing (15 dB HL re ANSI, 1969) at octave intervals from 0.25 through 8.0 kHz; 2) no history of chronic ear disease; 3) a normal tympanogram and otoscopic screening; 4) no employment history which required exposure to hazardous noise for periods in excess of two years; 5) AR thresholds of less than 100 dB SPL in response to a broad-band noise elicitor; 6) ability to perform a standard Bekesy procedure (modified method of adjustment) defined by repeatable thresholds (within 3 dB) at 1.0 kHz and stable tracings for up to one minute; 7) Bekesy

48 .

tracing excursion widths of less than 10 dB; and 8) signed consent to the experimental procedures, plus verbal commit- ment to return 24 hours after each of the exposures for testing to assure complete recovery from TTS

Instrumentation and Procedures

Exposure

Continuous octave-band noise (centered at 0.5 or 3.0 kHz) was presented bilaterally at 90 dB SPL for two hours under earphones. The order of exposure was alternated with each successive subject. A period of at least 24 hours

separated the two exposures . Complete recovery from the initial exposure was documented prior to proceeding with the final exposure.

Noise produced by a Coulbourn Instruments, model

S81-02, noise generator was filtered (Krohn-Hite, 3550), amplified (Coulbourn Instruments, S82-24 mixer-amplifier), attenuated (Hewlett-Packard, 350D attenuator set) and fed to Beyer DT48 earphones in an Industrial Acoustics audiometric booth.

The octave-band noise spectra used to produce TTS are included in Figures 2 and 3. Spectral measurements 50

volts)

316

. 0

dB (0

Frequency (kHz)

Figure 2. Spectrum of the 0.5 kHz octave-band noise through the Beyer DT 48 earphone. 51

volts)

.316

Figure 3. Spectrum of the 3.0 kHz octave-band noise through the Beyer DT 48 earphone. 52

were accomplished by coupling the earphone to a flat- plate attached to a standard 6 cc cavity. Using a 1"

Bruel and Kjaer microphone and type 2604 microphone amplifier the signal was analyzed and plotted using a Hewlett-Packard wave analyzer.

Behavioral Hearing Threshold Measurements

Subjects were trained during a trial session to perform a standard Bekesy tracking procedure for determination of behavioral hearing thresholds. Stable tracings for at least one minute with excursions of less than 10 dB and mid-line deviations of no more than 3 dB were required at each test frequency before continuing. A computer program was developed to control the behavioral stimulus parameters, accept or reject thresholds (according to the above tracing criteria) and, to store threshold data on disk for each subject.

Pulsed tones (250 msec with a 50% duty cycle) were used to establish thresholds at discrete frequencies of

0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0, and 8.0 kHz.

The tonal stimuli were generated by a Hewlett-Packard 3311A function generator and routed to Coulbourn Instruments timing, gating and digital logic circuitry. The subject's hand-held switch activated a programmable attenuator and the signal was delivered to a single earphone (Telephonies 53

TDH-30) with standard (MX-41/AR) audiometric cushion.

Calibration values were reconfirmed at all test frequencies

at the start of each day.

Table 1 outlines the test session format followed

in obtaining hearing threshold and AR data. Behavioral

hearing thresholds in the left ("test") ear were established

for each subject during test sessions occurring prior to,

immediately following, and finally, 24 hours after termin-

ation of each of the noise exposures. Thresholds in the

right ("non-test") ear were established during the trial

session and were compared to thresholds for that ear

obtained at the end of the subject's participation. This

procedure was adopted for two reasons. First, designation

of a single "test" ear for TTS determination was necessi-

tated by the relatively rapid recovery timecourse in

exposures of this intensity and duration and by the rela-

tively large number of frequencies tested. Threshold

determination immediately following a noise exposure was

limited to 15 minutes and to only one ear in order that it

be accomplished prior to the recovery of sensitivity.

( The same left "test" ) /right ("non-test") configuration was used for all subjects to simplify comparisons to

acoustic reflex data as described in the next section.

Second, having made the above distinction, it was still

necessary to document complete recovery in the "non-test"

ear since the TTS producing noise was delivered bilaterally 11 1 i -

c X 13 WO OJ 0 (0 G G O G •p •P >< d> X fd 4-1 fC Eh CD 4J -p in P X d) I -H -PC • in G fd p CD X -P - X P X -P m P CD • id ID 0 P a) +) (d (do cn X U g J CQ •H XI -P •p -p • p S - in -h G X h cd • 0 in O fd d) < X G id O •p o g d) -P X X X O,o X P x 2 p" p* o x e O in id o c cn o 1 CD o X a - • •p it-" in X td td in rH • O -P -p in Eh X X p - • p id o < S 0 dJ T3 G d) cn X X in 0 CD in p Q X m m O u dJ in tr> x X d) > X eli m x o CD G G p p •p fd Eh d) 0 0 c p applicable • i— -p p (D P X it

preceeding

each

audiometric, data

and that X prior G cn G rmat oise, rom G •P t O X O X G G Eh O O X 0 fd CD TJ d) Tl P C C G dl X t c • • X o 0 X X CD O (d • • rH •H •p rs o p -h in x CO in p p d) o fd CD o P X -P d) P -P O 0) CO p O X ft -P p P •p X O -P -P Q in in aj p w X CO O d) p Eh X p 0) X 1 o G G ift ft ID fd 0) H CO CO o Eh < < CO CQ < H Eh < < ft 0Q r— CO CO X CO CO fd w • • • • pt] • • • • • Eh CO rH CN CO co HiNH^rin U i 1 ' - '

cn T3 • V • cu cn i— P 3 LO N o X cn p 'd O CO 0 • m • • X m 0 cu X CU rH o M cn N +J X cn cu K T3 G •H P cu X — in X (0 0 O >i (0 p z cu CN cu X X •H P > X X cn > • > in X X CU Eh cn (0 rH o p • X ro r0 CU > • cn cu IP IP o X p 0 cu c0 X cu co T3 d cu o > 15 • z o P o p — • C Cn cu CU 0 X c 0 0 o X (0 •H p P X CU o cu • (0 X X (0 o cu Eh d p p rH • CN g -p T3 c m IP d1 d d T3 cn 0 c cn (U X W cu 0 o 0 Si CU P cn 0 cu CO co d d X cn ip • ip in G (0 x X cn cn ro • H CU cn CU X cn T3 cu , c G iH CO 0) X! CU cn X c •H o X s X X P 0 cn X cu •H cu cn • cn o 0 0 Qj p cn p d cn CN cn • X • O g p Pu O o p — in — S X 0 • 0 c < 0 O, o 'd X o 0 X X! (0 d X CU X d cu QJ rH 0 c • cn cn X d -P d > X X cu m S c0 cr cn g 0 cn P cu X p rH in r- cn O C (U CD <0 X 10 CU X -H cu c cn • cn • X p Eh cn m S CP Eh 0 o o o X IP X

(U (U p p cn d cn cn d T3 cn T3 TO cn X O X X 0 O !>i CP 0 o cpx cn X X X X cn CU w cn cn cn w cu X cu i cu >i2 cn d cn p cn cu cn 'd cu X cu T3 X O G X &H X G CU CP (0 CU cu cfl cn X cn cn >i

cn cn

i CU 1 CU cu cu >1 'o a; cu cu p cu > p X p pH T3 p > d p (0 d cu -p 0 d d co cn CP cn X cn cn cu x X • • cn X O • • o O s cn •H CP O o O-i w >1 x o a •• >i THREE o CU c p CPO X > p O X X p c P cn o X cn G 1 cn > EH a a i cn X 0 CU 0 X 0 g a cu p cn 2 0 P o cn 2 o cp o p •H 0 o cu X (U 0 O cu < M cn IP CP H cn Eh cn CP H CP SESSION C/3 C/3 cn cm cn cn • • • w • • • w • • • w • iH CN cn C/3 •— CN cn cn rH CN ro cn rH ,

to the subjects. That exposure arrangement was based on

subject comfort. Subjects in pilot studies indicated that

bilateral noise under earphones was less disturbing than

a unilateral exposure. A common comment was that they

felt more "balanced."

The sampling order for behavioral threshold determin-

ation during each test session was as shown in Table 1.

Pre-exposure thresholds and 24-hour recovery thresholds were obtained sequentially for each of the frequencies from

0.25 to 8.0 kHz. However, the frequency sampling order for

immediate post-exposure threshold determination was altered

to facilitate expediency in testing prior to the onset of

recovery. The sampling order used was consistent with

previously published data on the frequency of maximum

shift following octave-band exposures (Melnick and Maves

1974; Melnick, 1974; Patterson, 1977; Gerhardt and Hepler,

1983) .

Calculations of TTS at each hearing threshold

frequency and for combinations of those frequencies were

obtained by subtracting pre-exposure threshold from

immediate post-exposure threshold. Pre-exposure and 24-

hour recovery thresholds were also compared to document

the complete recovery of sensitivity. 57

Acoustic Reflex Measurements

Subjects were screened otoscopically to rule out external ear conditions that would have interfered with proper placement of the admittance transducer assembly probe. At the initial trial session tympanometric data were obtained in order to verify normal middle ear compliance values. An acoustic reflex screening was accomplished at that time to verify the presence of a measurable response to a broad-band elicitor at 100 dB SPL.

Following the above screening, AR parameters were measured at two separate test sessions prior to the noise exposures. A period of at least 24-hours separated these sessions from the noise exposures in order to avoid the interference of TTS due to the elicitation levels used.

Data on the perstimulus adaptation of the AR response were obtained during the first session and data on AR elicitor-intensity/response-magnitude relationships were obtained during the second session, as described below.

Tympanometric data were obtained at both sessions.

Admittance data were recorded at tympanometric points corresponding to an ear canal pressure of -300 daPa and at ambient pressure in order to estimate (and allow corrections for) ear canal volume.

A Grason-Stadler , 1723 Middle Ear Analyzer was modified to facilitate the simultaneous monitoring of admittance (Y) 58

and its sub-component susceptance (B) . These analog voltages were routed to a Data Translation analog to digital I/O system connected to an IBM PC computer.

Elicitation stimuli were generated externally using a

Coulbourn Instruments, S81-02 noise generator and a Krohn-

Hite, 3550 filter. Three elicitors were used: broad- band noise, and two filtered octave-band noises (centered at 0.5 and 3.0 kHz). Contralateral reflex measurements were obtained using a 0.220 kHz probe tone frequency.

All AR measurements were obtained at ambient ear canal pressure with the admittance probe inserted in the subject's right ("non-test") ear and the elicitation earphone over the left ("test") ear in order to simplify comparisons to

TTS data. Wilson (1981) found no significant differences in the left ear versus right ear AR data obtained for subjects with normal hearing bilaterally. The elicitor was presented through a Telephonies TDH-49 earphone with a standard (MX-41/AR) audiometric cushion. Stimuli were of

1000 msec duration, with a rise/fall of 10 msec controlled with Coulbourn Instruments gating and timing devices interfaced with the computer. The admittance values were digitized and stored on disk for later analysis.

The computer was programmed to analyze variation from static baseline admittance activity. This allowed AR response determinations based on comparisons of admittance outputs prior to and during the presentation of an .

elicitation stimulus. A trial was labeled as a "response"

if the mean admittance during elicitation (averaged over

500 msec) exceeded the mean baseline admittance by more

than two standard deviations. Two responses out of three

presentations at a single elicitation intensity level

defined the subject's threshold for that particular elicitor.

Elicitor levels could be controlled by the experimenter

at the computer keyboard or by the computer program which

automatically incremented or attenuated the signal. In

the automatic mode the elicitation signal was introduced

at a level below anticipated AR threshold and then increased

in 2 dB steps until a response was recorded. If the initial

presentation elicited a response the program requested a

new starting value. Once an initial response level had

been determined the elicitor was attenuated 4 dB and three

successive trials occurred at that level. The intensity was then increased by 2 dB and three more presentations were

made until a level was reached where the two-out-of- three

response threshold criterion was met. Previous studies

have shown no significant differences in the AR threshold

data obtained in ascending or descending techniques (Lilly

and Franzen, 1970; Peterson and Liden, 1972; Wilson, 1979).

A visual display of each trial on the computer screen

allowed a store/no-store decision by the experimenter.

Trials that contained extensive artifact due to subject movement were repeated. The entire response waveform was

stored for later analysis (Figure 4) 60

2.1447

2. 1322 2.1197

2. 1072 (mmho) 2.0947 2.0822 2.0697 2.0572

Admittance 2.0447 2.8322 2.0197 2.0072 1.9947

Acoustic 1.9822 1.9697 1.9572 1.9447 (hS) 0 250 500 750 1800 1250 1500 1750 2800 2250

Time (msec)

Figure 4. Example of AR response as stored by computer. . .

Data for magnitude/intensity functions were obtained using a program that incremented elicitor intensity levels in 2 dB steps beginning at AR threshold. Two presentations were made at each level up to 12 dB SL (re the AR threshold)

The intensity series then continued with one presentation per 2 dB increment up to a maximum of 110 dB SPL for the two filtered noise elicitors or 104 dB SPL for the broad- band elicitor. As before, this data was stored on disk for analysis and the experimenter made a store/no-store decision in order to eliminate trials containing movement artifact.

The magnitude of each stored AR response was computed

in terms of acoustic admittance (Y) Analyses of such data must include the phase and vector information that is

contained in the susceptance (B) and conductance (G) sub-

components of admittance (Block and Wiley, 1979). There

is also a need to arithmetically remove the acoustic

admittance of the ear canal from such values to account

for variations in that volume. Such corrected values are

more valid measures of the admittance at the lateral

surface of the tympanic membrane (Popelka, 1984; Block and

Wiley, 1979). The stored response data included both Y and

B measurements . The required values for G were calculated

for each data point using the relationship:

2 2 G = ^(Y) - (B) 62

Using the tympanometric data obtained prior to each AR trial, the computer also corrected each Y, G and B data point for ear canal volume by subtracting the corresponding value at -300 daPa. The portions of the stored waveforms prior to and during elicitation were then averaged and compared as shown in Figure 5. The obtained difference

(expressed in mmhos) became the magnitude value for that particular response.

The computer calculated onset latency for each waveform by identifying the time at which the immittance change attained 10% of its maximum deviation from baseline. A correction factor of 15 msec was used to account for the response time of the measurement system.

Data on the perstimulus adaptation of the AR response was obtained for each of the three elicitors in two stimulus presentation conditions. First, a "standard" measure of reflex adaptation was obtained by monitoring the response to a sustained elicitor for 4.0 minutes at a sensation level

of 15 dB (re AR threshold) . Reflex adaptation was measured in this manner for the three elicitors (broad-band, 0.5 and 3.0 octave-band noise) and expressed as the percent of change in AR magnitude (Y) as a function of time with reference to stimulus onset (Wilson, Steckler and Jones,

1978; Hepler, 1984) . A 2500 msec segment of admittance output was sampled by the computer every 7.5 seconds throughout the time period. Baseline drift was assessed 63

2.1447 2.1322 2.1197 s

2 ., 1072

2 .0947.

2 ..0822

2 ..0697

2 ..0572 2.0447 2. 0322 \ 2.0197 2.0072 1.9947 1.9822

(mmho) 1.9697 1.9572 1.9447 1750 2000 2250 2508 (mS) 8 250 500 758 1000 1250 1580 - 2. 1572 - Admittance 2.1447 2.1322 - s - / 2.1197 \ 2. 1072 \ 2.0947 Acoustic 2.0822 2.8697 2.0572 2.0447

2.0322 \i yr 2.8197 ft 2.8072 uw-AT 1.9947 1.9822 1.9697 1.9572 1.9447 1750 2000 2250 2500 (nS) 0 258 588 750 1888 1250 1508 Time (msec)

Figure 5. Example of computer averaging of portions of the AR. ,

for each subject by monitoring an additional 4.0 minute period with no elicitation signal present.

A second measure of the perstimulus AR response as a function of time was obtained by briefly interrupting the sustained elicitation signal. The elicitor was presented at 15 dB SL as before, but was switched off for a 250 msec period mid-way through each 2500 msec sample over the 4.0 minutes. As shown in Figure 6 this resulted in a partial relaxation of response followed by a resumption of activity but not the re-elicitation of a new response. The relative reduction in this event (as shown by comparing the initial sampling period with the subsequent ones) resulted in a reflex interruption test score (RIT) for each subject.

This score is the point-in-time (after elicitation signal onset) at which the admittance change resulting from the brief elicitation signal interruptions dropped to below 10% of that initially observed. This procedure was repeated for each of the three elicitation signals.

Calibration

The Telephonies TDH-39 (hearing test signal) and TDH-49

(reflex elicitation signal) earphones had standard super- aural (MX-41/AR) cushions and were calibrated according to

ANSI S3. 7-1973 using a 1" Bruel and Kjaer condenser micro-

phone coupled to a 6cc cavity (type 4152) . A Bruel and . .

3.2424 - 3.2299 - (mmho) 3.2174 3.2049 3.1924 3.1799 3.1674 Admittance 3.1549 - 3.1424 - - 3. 1299 3.1174 - - 3. 1049 Acoustic 3.3924 - - 3.0799 (*S) 0 250 500 750 1008 1250 1500 1750 2008 2250 2508

Time (msec)

test) Figure 6 Example of the RIT (reflex interruption .

Kjaer pistonphone (Model 4220) was used to assure calibration of the microphone amplifier.

The Beyer DT48 (noise exposure) earphones had circum-

aural cushions that were not compatible with the standard

coupler. Hepler (1984) designed a flat-plate coupler,

according to specifications published by Michael and

Bienvenue (1976b), that allowed calibration of this type

earphone. This device is illustrated in Figure 7.

Calibration was completed as recommended by the original

authors

The internal electronic circuit voltage and phase Middle Ear checkpoint values in the Grason-Stadler , 1723

Analyzer were confirmed prior to, during, and after the

completion of data collection. The unit remained stable

Y and B according to the mauf acturer ' s guidelines. The

admittance component voltage outputs were calibrated by

connecting the probe assembly to a microsyringe. Voltage

change per change in physical volume was calculated and

used to program the computer. For the 0.220 kHz probe

tone used, a 100 mV change in output voltage corresponded

to a 1 mmho change in measured admittance. Pre-operation

checks were performed daily using the manufacturer's

calibration cavity. .

Figure 7. Flat plate used with the NBS 9-A coupler for Beyer DT 48 earphone calibration (after

Hepler , 1984) 68

Data Analysis

An IBM 3081D computer at the Northeast Regional Data

Center was used to obtain standard descriptive statistics for all AR, behavioral hearing threshold and subject demographic data. Individual bivariate correlational analyses were performed between all AR parameters and TTS for both noise exposures. The predictive quality of specific AR parameters was modeled using a multiple regression analysis (SAS, 1982). .

CHAPTER III RESULTS

Behavioral Threshold Shift

Pre-exposure threshold data were compared with immediate post-exposure threshold data for each of the 30 subjects.

Temporary threshold shift was calculated at each test frequency by subtracting the pre-exposure value from the post- exposure value. Mean TTS results for the two 90 dB SPL octave-band noise exposures (centered at 0.5 and 3.0 kHz) are displayed separately since the audiometric frequencies differed (based on the predicted pattern of TTS by frequency)

Figure 8 shows the means and standard deviations of

TTS by frequency for the two-hour exposure to noise centered at 0.5 kHz. The TTS, as averaged across all subjects, was greatest at 1.0 kHz. Mean TTS at that test frequency was 7.5 dB. Slightly less TTS occurred at the adjacent frequencies of 0.75 kHz (7.1 dB) and 1.5 kHz (6.3

dB) . Only a minimal shift in hearing (less than 3 dB) occurred at 0.5 kHz and at 2.0 kHz. However, TTS at all the above frequencies (0.5 to 2.0 kHz) was shown to be significant using a paired difference t-test comparison

(p = .01). The pre-/post-exposure comparisons for 3.0

• 69 .

•25 .5 .75 1.0 1.5 2.0 3.0 4.0 Frequency (kHz)

Figure 8. Mean TTS values by frequency for 30 subjects exposed to 0.5 kHz octave-band noise (bar = one standard deviation) 71

and 4.0 kHz were not statistically significant and the

"negative" TTS (-2.9 dB) at 0.25 kHz was not considered to be clinically relevant.

Figure 9 shows mean TTS and standard deviations by frequency for the two-hour exposure to noise centered at

3.0 kHz. This high-frequency exposure resulted in greater

TTS, as averaged across all subjects, than did the low- frequency exposure. The TTS was also distributed relatively closer to the center frequency of the noise used in that exposure. Maximum TTS was at 3.0 kHz (22.54 dB) and at

4.0 kHz (22.50 dB) . Temporary threshold shift dropped off dramatically for the audiometric frequencies on either side of that maximum. Paired difference t-test comparisons

for this exposure were significant for 1.5 to 8.0 kHz (p =

.01). The slight pre-/post-exposure shift at 0.5 and 1.0 kHz was not statistically significant. It was not possible

to obtain thresholds above the normal audiometric upper

frequency boundary of 8.0 kHz.

There was a great deal of individual variation in the amount of TTS that resulted from each of these exposures.

Table 2 includes the minimum and maximum TTS observations

for individuals by audiometric frequency for the 0.5 octave-band noise exposure. The mean TTS values for all

subjects are displayed for comparison. Table 3 is a

similar minimum- /maximum- individual versus group-mean

comparison for the 3.0 octave-band noise exposure. As .

Frequency (kHz)

Figure 9. Mean TTS values by frequency for 30 subjects exposed to 3.0 kHz octave-band noise (bar =• one standard deviation) —

Table 2. Individual minimum and maximum TTS values by frequency as compared to mean TTS values for the 0.5 kHz octave-band noise exposure.

Individual TTS Mean TTS Frequency Minimum Maximum N = 30 STD Error (kHz) (dB) (dB) (dB) of Mean

0.25 -8.3 3.1 -2.9 0.5

0.5 -2.5 10.4 2.8 0.6

0.75 0.1 16.6 7.1 0.7

1.0 -2.8 16.6 7.5 0.8

1.5 -4.0 15.9 6.3 0.9

2.0 -7.1 10.3 2.9 0.7

3.0 -4.6 7.1 0.9 0.5

4.0 -6.4 9.2 -0.2 0.7 — 74

Table 3. Individual minimum and maximum TTS values by frequency as compared to mean TTS values for the 3.0 kHz octave-band noise exposure.

Individual TTS Mean TTS Frequency Minimum Maximum N = 30 STD Error (kHz) (dB) (dB) (dB) of Mean

0.5 -6.0 5.3 -1.6 0.4

1.0 -4.8 6.0 0.2 0.5

1.5 -2.9 8.2 1.6 0.6

2.0 -4.3 8.3 2.2 0.6

3.0 5.0 35.3 22.5 1.3

4.0 8.2 34.5 22.5 1.2

6.0 -7.5 36.0 11.8 1.2

00 • o -4.7 17.1 4.7 0.9 75

shown by these tables, individual variation in TTS was

greater following the high-frequency exposure (maximum

spread of 43.5 dB at 6.0 kHz) than following the low-

frequency exposure (maximum spread of 19.5 dB at 1.5 kHz).

Table 4 (for the 0.5 octave-band exposure) and Table 5

(for the 3.0 octave-band exposure) provide comparisons of

individual variation in the audiometric point of maximum

TTS for the two exposure conditions. Twenty-six of the

thirty subjects demonstrated maximal TTS at 3.0 or 4.0

kHz following the high-frequency noise exposure. However,

following the low-frequency exposure only ten of the thirty

subjects had individual points of maximum TTS at the group mean frequency of 1.0 kHz.

For descriptive purposes several expressions of TTS were used in the initial comparisons of these noise-

exposure data. Temporary threshold shift was expressed by

frequency, as a combination (or sum) of observations for

specific frequencies and as "total" TTS (the sum of

observations at all tested frequencies) . These individual

and collective expressions of TTS are summarized for the

0.5 octave-band noise exposure in Table 6. Individual

minimum and maximum observations are included, as well as mean values. A similar summary for the 3.0 octave-band

noise exposure is provided in Table 7.

Testing following each noise exposure was limited to

selected frequencies and to one "test" ear as explained in 76

Table 4. Individual variation in the audiometric point of maximum TTS for the 0.5 octave- band noise exposure (N = 30).

Frequency .25 .5 .75 1.0 1.5 2.0 3.0 4.0 (kHz)

Subjects 1 8 10 7 1 1 2 with maximal o TTS .

Table 5. Individual variation in the audiometric point of maximum TTS for the 3.0 octave- band noise exposure (N = 30)

Frequency .5 1.0 1.5 2.0 3.0 4.0 6.0 8.0 (kHz)

Subjects with 0 0 0 0 15 11 4 0 maximal TTS —

Table 6. Descriptive statistics for expressions of TTS' for the 0.5 kHz octave-band noise exposure

(N = 30) .

Expression Minimum Maximum Standard of TTS Observation Observation Mean Deviation (kHz) (dB) (dB) (dB) (dB)

Total TTS -3.9 73.1 24.4 16.7

.75 + 1 + 1.5 -3.1 46.3 20.9 10.2

2 + 3 + 4 -10.3 17.5 3.6 6.9

.5 -2.5 10.4 2.8 3.4

.75 0.0 16.6 7.1 3.9

1 -2.8 16.6 7.5 4.8

1.5 -4.0 15.9 6.3 5.0

2 -7.1 10.3 2.9 4.0 —

Table 7. Descriptive statistics for expressions of TTS for the 3.0 octave-band noise exposure (N = 30).

Expression Minimum Maximum Standard of TTS Observation Observation Mean Deviation (kHz) (dB) (dB) (dB) (dB)

Total TTS 15.6 113.7 63.9 25.3

1.5 + 2 -5.1 15.3 3.8 5.1

3 + 4 + 6 + 8 18.2 100.4 61.5 21.3

1.5 -2.9 8.2 1.6 3.0

2 -4.3 8.3 2.2 3.3

3 5.0 35.3 22.5 7.3

4 8.2 34.4 22.5 6.9

6 -7.5 36.0 11.8 10.2

8 -4.6 17.1 4.6 5.4 .

24-hour the previous section. However, pre-exposure and recovery thresholds were compared at each sequential audiometric frequency (0.25 to 8.0 kHz) for both the test and non— test ear. Mean recovery thresholds were within

0.5 dB of pre-exposure thresholds at all frequencies, indicating a complete return of hearing sensitivity for comparison the experimental subjects. Table 8 presents this for the test ear and Table 9 for the non— test ear. The

ANSI (1969) normative hearing values (representing audiometric zero for young adults) are included. Mean thresholds for these subjects were within 5 dB of those norms at all frequencies except 0.25 and 6.0 kHz.

Acoustic Reflex Properties and TTS

Acoustic Reflex Threshold

Mean AR threshold data are shown in Table 10 . The

values given for each of the three elicitors (0.5 octave-

band, 3.0 octave-band, and broad-band noise) are the

lowest presentation level (dB SPL) at which an AR occurred

two out of three times. Broad-band noise elicited the AR

at the lowest presentation level as averaged across all

subjects 81

Table 8. Mean behavioral thresholds and standard deviations left/test ear, pre-exposure and 24-hour recovery (N = 30). Norms for audiometric zero are also shown (ANSI, 1969).

Pre-Exposure Recovery Frequency Threshold Standard Threshold Standard Norm (kHz) (dB SPL) Deviation (dB SPL) Deviation (dB SPL)

0.25 31.1 4.7 30.1 4.1 25.5

0.5 15.9 4.4 15.6 4.6 11.5

0.75 8.6 4.9 8.8 4.7 8.0

1.0 6.7 4.7 6.3 4.7 7.0

1.5 6. 8 4.4 6.3 4.9 6.5

2.0 9.1 4.6 8.9 5.1 9.0

3.0 11.0 5.9 10.8 5.9 10.0

4.0 10.6 6.2 9.7 5.8 9.5

6.0 24.7 8.4 23.6 7.9 15.5

8.0 12.5 7.3 12.3 8.0 13.0 .

Table 9. Mean behavioral thresholds and standard deviations right/non- test ear, pre-exposure and 24-hour recovery (N = 30) Norms for audiometric zero are also shown (ANSI, 1969).

Pre-Exposure Recovery ANSI Frequency Threshold Standard Threshold Standard Norm (kHz) (dB SPL) Deviation (dB SPL) Deviation (dB SPL)

0.25 29.9 3.7 28.5 3.8 25.5

0.5 13.6 3.9 14.0 3.6 11.5

0.75 8.5 4.1 8.1 4.5 8.0

1.0 6.7 3.6 7.1 5.3 7.0

1.5 7.9 4.7 8.3 5.9 6.5

2.0 8.3 4.5 7.0 4.6 9.0

3.0 10.4 6.3 9.3 6.3 10.0

4.0 10.9 6.7 10.9 6.3 9.5

6.0 21.0 6.8 20.8 6.2 15.5

8.0 10.4 4.4 11.3 6.1 13.0 .

Table 10. Mean acoustic reflex threshold for three elicitation stimuli (N = 30)

Elicitation Stimulus

Broad-band Octave-Band Noise Noise 0.5 kHz 3.0 kHz

Acoustic Reflex 78.1 90.3 84.5 Threshold

Standard 9.95 7.93 8.23 Deviation 84

Correlational analyses were used to determine if TTS

- resulting from a specific noise exposure (0.5 octave band or 3.0 octave-band) could be significantly related to the subject's AR threshold (as elicited prior to that exposure correlation with three elicitation signals) . The resultant values for individual and collective expressions of TTS

(for the 0.5 kHz octave-band exposure) versus AR threshold are shown in Table 11. No significant pattern of relationships emerged from this comparison. The comparisons for the 3.0 octave—band noise exposure are

shown in Table 12. Those comparisons also failed to

illustrate any significant relationship.

Acoustic Reflex Magnitude

The magnitude of the AR response (defined as shift in

acoustic admittance) varied with individuals. That is, for

each of the three elicitors, equal presentation levels did

not result in an equal magnitude of response across

individuals. An example of this is given in Figure 10

where the AR for two separate subjects (resultant from a

90 dB SPL broad-band elicitor) is shown. Magnitude/intensity functions were plotted for each

subject. Simple linear regression analysis of the plotted

data resulted in positive slope values for each function. .

Table 11. Correlation matrix for AR threshold (as determined for 3 elicitors) and expressions of TTS (resultant from exposure to 0.5 octave-band noise) (N = 30)

AR Threshold 0.5 3.0 Broad-band octave-band octave-band

Total TTS -.23 -.29 -.24

.75 + 1 + 1.5 -.19 -.17 -.11

2 + 3 + 4 -.22 -.42* -.28

.5 -.33 -.16 -.34

.75 -.02 .02 .01

1 -.21 -.13 -.16

1.5 -.17 -.24 -.09

2 -.24 -.30 -.25

* p < .05 .

Table 12. Correlation matrix for AR threshold (as determined for 3 elicitors) and expressions of TTS (resultant from exposure to 3.0 octave-band noise) (N = 30)

AR Threshold 0.5 3.0 Broad-band octave-band octave-band

Total TTS -.09 .008 -.18

1.5 + 2 -.02 -.05 -.24

3 + 4 + 6 + 8 -.14 -.03 -.16

1.5 .06 .09 -.16

2 -.08 -.17 -.22

3 -.006 -.02 . 02

4 -.18 -.16 -.22

6 -.14 .02 -.17

8 -.04 .04 -.10 87

1.4791 1.4666 1 1.4541 1.4416 V r 1.4291 rhvyv 1.4166 1.4041 1.3916 1.3791 1.3666 1.3541 1.3416 1.3291

(mmho) 1.3166 (S) 0 "250 500 750 1000 1250 1500 1750 2000 2250

-1 2. 1447

2. 1322

Admittance 2. 1197 2.1072 2.0947 2.0822 2.8697 2.0572 Acoustic 2.0447 2.0322 2.0197 2.0072 1.9947 1.9822 1.9697 1.9572 1.9447 !i»S) 0 258 500 750 1000 1250 1500 1750 2080 2250

Time (msec)

Figure 10. Example of AR magnitude difference between 2 subjects for the same elicitor and presentation level. .

Increases in elicitation level thus resulted in a growth in

AR magnitude for all sub ject/elicitor comparisons. Figure

11 provides a comparison of magnitude/intensity functions

for the three elicitation stimuli as averaged across all

thirty subjects. Since the 0.5 octave-band elicitor

resulted in higher threshold values across subjects (see

Table 10) , the function for that elicitor covers a narrower

range.

In order to describe AR magnitude as a single

representative value for each sub ject/elicitor combination,

the observations at presentation levels of 6, 8, 10 and 12

dB (re AR threshold) were averaged. This value represented

AR magnitude as averaged over the most linear portion of

the individual growth functions. Correlational analysis was used to determine if this average AR magnitude could be

related to the TTS that developed from a specific noise

exposure. Table 13 presents this comparison by elicitor

for the 0.5 octave-band noise exposure. A similar compari-

son is provided in Table 14 for the 3.0 octave-band noise exposure. No specific pattern was shown in either analysis, however average AR magnitude in response to a broad-band elicitor was correlated to the collective TTS at 2+3+4 kHz for the 0.5 octave-band noise exposure (r =-.46,

p < .01) .

(mmhos)

Admittance

Average

Figure 11. Average growth in AR magnitude per unit increase in elicitor presentation level for 30 subjects (aisO.5 octave-band noise, + is 3.0 octave-band noise, o is broad-band noise) .

Table 13. Correlation matrix for AR average magnitude (as determined for 3 elicitors) and expressions of TTS (resultant from exposure to 0.5 octave- band noise) (N = 30)

AR Average Magnitude 0.5 3.0 Broad-band octave-band octave-band

Total TTS -.29 -.33 -.13

.75 + 1 + 1.5 -.05 -.17 -.006

2 + 3 + 4 -.46** -.31 -.26

.5 -.17 -.30 -. 003

.75 -.06 -.34 -.07

1 -.11 -.23 -.06

1.5 -.18 -.09 -. 009

2 -.31 -.05 -. 02

* p < .01 .

Table 14. Correlation matrix for AR average magnitude (as determined for 3 elicitors) and expressions of TTS (resultant from exposure to 3.0 octave- band noise) (N = 30)

AR Average Magnitude 0.5 3.0 Broad-band octave-band octave-band

Total TTS -.12 -.08 -.26

i 1.5 + 2 -.35 • o to -.17

3 + 4 + 6 + 8 —.01 -.09 -. 21

1.5 -.33 -.11 -.11

2 -.24 -. 06 -.07

3 -.01 -. 04 -.04

4 -.10 -.27 -.27

6 -.02 -.18 -.19

8 -.06 -.36 -.36 92

Acoustic Reflex Latency

The response onset latency of the AR (defined as time from elicitation stimulus onset until an immittance change of 10% of maximum) varied across subjects according to the elicitor used. There was a great deal of individual subject variation, however mean AR latency at threshold was shortest for the broad-band elicitor (385 msec) and longest for the 0.5 octave-band elicitor (437 msec) . Figure 12 provides a comparison of the average decline in AR latency per unit increase in elicitor presentation level for the three separate elicitors. The function for the 0.5 kHz octave-band elicitor is, once again, relatively narrow since AR threshold for that elicitor was higher (see

Table 10) .

As with the AR magnitude data, latency observations were averaged for presentation levels of 6, 8, 10 and 12 dB (re AR threshold) for each sub ject/elicitor combination.

Correlational analyses were used to determine if this single representative value for latency could be related to TTS

resulting from a specific noise exposure. As shown in

Table 15, AR average latency for the broad-band elicitor was significantly correlated to several expressions of

the TTS resultant from exposure to the 0.5 kHz octave-band

noise. Table 16 provides similar comparisons for the 3.0

kHz octave-band noise exposure. .

(msec)

Latency

Average

0 2 4 6 8 10 12 14 16 18 20 22 24 26

Elicitation Level (dB re AR Threshold)

Figure 12. Average decline in AR latency per unit increase in elicitor presentation level for 30 subjects ( is 0.5 octave-band noise, + is 3.0 octave-band noise, o is broad-band noise) .

Table 15. Correlation matrix for AR average latency (as determined for 3 elicitors) and expressions of TTS (resultant from exposure to 0.5 octave- band noise) (N = 30)

AR Average Latency 0.5 3.0 Broad-band octave-band octave-band

Total TTS .28 .22 .19

.75 + 1 + 1.5 .42* .22 .21

2 + 3 + 4 -.15 .19 .03

.5 .25 .05 .06

.75 .51** .12 .19

1 . 43* .19 .24

1.5 .06 .21 . 05

2 -.20 .21 .05

* p < .05

** p < . 01 95

Table 16. Correlation matrix for AR average latency (as determined for 3 elicitors) and expressions of TTS (resultant from exposure to 3.0 octave-

band noise) (N = 30) .

AR Average Latency 0.5 3.0 Broad-band octave-band octave-band

Total TTS -.32 -.43* -.36*

1.5 + 2 -.34 -.32 -.18

3 + 4 + 6 + 8 -.31 -.43* -.35

1 . 5 -.21 -.25 -.09

2 -.33 -.28 -.20

3 -.13 -.37 -.28

4 -.21 -.37 -.45

6 -.24 -.27 -.22

8 -.36 -.23 -.38

* p < .05 96

Acoustic Reflex Adaptation

The data on perstimulus adaptation of the AR were only retrievable for 24 of the 30 subjects. Extensive movement or myogenic artifact precluded analysis of the data for the remaining six subjects. A standard measure of AR adaptation over a four-minute period of sustained elicitation was obtained by calculating percentage of decline in acoustic admittance at signal offset as compared to onset.

As averaged across subjects the AR adaptation was 41% for the 0.5 octave-band elicitor as compared to 85% for both the 3.0 octave-band and the broad-band elicitors. A

Reflex Interruption Test (RIT) score was also obtained.

The RIT score for each subject was the time at which the admittance change resulting from the brief elicitation signal interruptions dropped to below 10% of that initially observed for each elicitor. This value was 191 seconds for the 0.5 octave-band elicitor as averaged across the 24 subjects. The corresponding decrease in the RIT occurred at 108 seconds for the 3.0 elicitor and 114 seconds for the broad-band elicitor. Correlational analyses between these two measures of adaptation resulted in significant negative correlations as shown in Table 17.

Both the adaptation measures were then compared to

TTS. As shown in Table 18, the TTS at 1.5 kHz resulting from the 0.5 octave-band noise exposure was significantly related to both broad-band adaptation (r = .42, p < .05) 5 .

Table 17. Correlation values for a standard measure of AR adaptation and the reflex interruption test (RIT) as observed over 4.0 minutes for 3 elicitors (N = 24)

RIT Standard 0.5 3.0 Broad-band octave-band octave-band

Broad-band -.59** .13 -.49*

-.53** 0 . octave-band .33 -.47*

3.0 octave-band -.26 -.18 -.23

* p < .05 ** p < .01 1 1

,o c (0

noise) X3 o 1 (Ti IT) ll l i l l i -p o 3 0 octave-band C for Eh (0 H X! Pi in i ina'tr-'iooooocNoo • < a) I I 0.5 P u determined 0 to c (C XI * (as 1 oooojiO'tfroi-Hcn T) CNCNOi— exposure It-HO^CN cC

0 I I I I I I I I u OQ

adaptation from T) C (C XI O I 00 CO 00 t-~ o CN o • o CM iH CN CN rH rH CN CN co > *

and (resultant (0 i -p o c 0 RIT 0 •H t -p C

(0 rfl for TTS -p X! a. in 1 O CO CN in in in to • cu rH rH rH o co rH rH CN of T3 o > • IT) < i i i i -P matrix Pi O < 0

T) C expressions (0 * XJ * * 1 ID 00 00 00 rH CN CO Tl CN rH CO m o o in Correlation (0 •

0 I I II p a in rH and 2 in o O • • V V co 18. + Eh a Oi Eh i— + in * He i— + co in r- in * (TJ • • t Table -P in + rH rH 0 n* Eh • CN 99

and RIT scores (r = -.41, p < .05). Adaptation to the

broad-band stimulus was also shown to be related to the TTS

observed at 2.0 kHz (r = .53, p < .01).

The results of a similar analysis of adaptation and

RIT data as compared to TTS for the 3.0 kHz octave-band

noise exposure is shown in Table 19. Temporary threshold

shift at 6.0 kHz was significantly related to the amount of

adaptation to the broad-band and 0.5 octave-band elicitors

(r = .55, p < .01; and r = .43, p < .05 respectively) and

to the RIT score obtained for the 3.0 octave-band elicitor

(r = .44, p < .05). Temporary threshold shift at 3.0 kHz

was also shown to be related to amount of adaptation

observed for the 3.0 kHz octave-band elicitor.

Demographic Data and TTS

The demographic data obtained for each of the thirty

subjects is shown in Table 20. Between subject differences

on the qualitative variables (sex and eye color) were

compared separately to expressions of TTS for each noise

exposure. A special case of regression analysis involving

artificial or "dummy" variables was used. The introduction

of such variables to represent levels or classes of

categorical variables is equivalent to an analysis of variance. No significant difference was demonstrated in 1

100

TJ C cd

noise) xj *

O I 00 00 ID n m o . 00 elicitors) (1) o CN rH o o oo > P

octave-band T3 C for HEh A(0 OS in i in ID ID r* o D n* 00 00 . a) O 00 o ro O CN o o OS o > rij

Id I O u CQ

adaptation from T3 C

and (resultant (d •P o C O RIT o •H T3 P c id ns for TTS p p * Cu m 1 oo ID CO Cl 00 ID oo CN (d • a) o o o CN CN of to > < id P matrix OS o < 0

T3 C * expressions id K XI CN ro as o 00 00 n 00 ro 00 1 CN CN ro rH o n ro n* •o •

Correlation O') id o i 1 p CQ n rH and 2 00 o o • • + V V cn VO 19. Eh H CN + in a si * * r— + •H CN ro VO 00 * rd Table P ID + O • Eh rH ro . :

101

Table 20. Demographic data for 30 subjects (14 male, 16 female)

Eye Smoking Eye Smoking Age Sex Color History Age Sex Color History

18 M 1 1 34 F 1 4

19 M 3 1 21 F 2 2

21 M 2 1 22 F 2 1

19 M 2 0 24 F 2 1

20 M 2 0 25 F 2 0

24 M 1 0 23 F 3 0

19 M 1 0 29 F 3 0

19 M 1 0 25 F 1 0

29 M 2 0 30 F 1 0

22 M 2 0 26 F 1 0

21 M 2 0 21 F 2 0

20 M 2 0 27 F 3 0

22 M 3 0 23 F 2 0

19 M 2 0 18 F 2 0

22 F 3 0

21 F 2 0

= Key: Blue « 1 Smoking History Brown = 2 Minimum = 0 Other = 3 Maximum = 9 102

the TTS of male subjects versus female subjects using this

method (p = .05) . Similarly, there was no significant difference in the TTS of subjects in the three eye color classifications (p = .05).

The additional data on smoking history (also shown in

Table 20) was calculated for each subject based on response to questions about smoking. There was no significant difference in the TTS (following either noise exposure) of subjects categorized according to that variable. This analysis was carried out as indicated above.

Pre-Exposure Audiometric Threshold and TTS

Pre-exposure audiometric threshold (at individual and combined frequencies) was compared to similar expressions of TTS for both noise exposures. There was a significant negative correlation (r = -.55, p < .01) between pre- exposure threshold and TTS at 1.0 kHz following the low-

frequency exposure. Single-frequency TTS at 1.5 and 6.0 kHz following the high-frequency noise exposure was

significantly related to pre-expsoure threshold at those

frequencies (r = -.51, p < .05; and r = -.54, p < .01 respectively). Combined TTS at 1.5 and 2.0 kHz was also

shown to be negatively related to a similar combination of pre-exposure frequencies (r = -.48, p < .01). 103

Multiple Regression Modeling

The individual correlation coefficients were small for most of the above described comparisons of results. For

the sample size (30 or 24 subjects depending on the

variable) coefficients larger than .40 were required to

demonstrate significance at the .05 level. With the

exception of the pre-exposure threshold/TTS comparisons,

there were few obtained correlation values of that magnitude.

For the number of comparisons made on these variables, the

scattered, isolated instances of significant correlation

are not impressive statistically and theoretically could

be attributed to chance. However, regression analysis was

used in an attempt to define any trends in the data that

might have clinical utility. The variables considered in

this process were restricted to those that had shown any

significant correlation to expressions of TTS. A second

criterion was that the variables could be reasonably

determined clinically.

Pre-exposure audiometric threshold was the most

strongly correlated single comparison variable and was

therefore included in each iteration. Elicitor/AR variable

combinations for average magnitude, average latency,

adaptation and RIT had also produced scattered, significant

correlations with various expressions of TTS. These

variables were included, but the modeling was restricted to 104

a comparison of two elicitors for each exposure condition.

Clinically, it would be most reasonable to use a single elicitation stimulus to obtain AR measurements. The intent of this modeling was to show which elicitation stimulus would be the most efficient for that use.

For the TTS resulting from the 0.5 octave-band noise exposure AR adaptation, RIT, average magnitude and average latency as elicited with broad-band noise were considered first. Then, the entire procedure was repeated considering those same variables as elicited with 0.5 kHz octave-band

noise (since that was the exposure stimulus) . In the same manner, the TTS resulting from the 3.0 octave-band noise exposure was first modeled against pre-exposure audiometric threshold and AR adaptation, RIT, average magnitude and average latency as elicited by broad-band noise. Then, the modeling was repeated for those same variables as elicited by the exposure stimulus (3.0 kHz octave-band noise).

A stepwise modeling procedure designed for maximum

2 improvement m the coefficient of determination (r ) was used. This technique determines the best one variable model based on a comparison of single Pearson correlation coefficients. Then, other variables are entered and partial correlations considered to select the best two variable model and so on. A variable that is initially entered may be dropped if the addition of new variables lowers its individual significance (in relation to the 105

dependent variable) to below a pre-determined level (p = 0.5).

The best single variable models determined in the above manner are shown in Table 21. Using the same inclusion criteria, the best two variable models were determined as shown in Table 22. The addition of further variables did not significantly improve the amount of variation in the dependent variable explained by the model determined according to the above criteria.

A second (less statistically rigid) criteria was adopted to identify the combination of elicitor/variables that produced the highest coefficient of determination while maintaining an overall significance level (for the entire model) below p = .05. This method ignores the relative effect of individual variables. The result of modeling according to this criteria are shown in Table 23

(for the 0.5 octave-band noise exposure) and in Table 24

(for the 3.0 octave-band noise exposure). A discussion of the relative statistical merit of this modeling is provided in Chapter IV. 106

Table 21. Best single variable models, based on data for 24 subjects.

Expression Level of 2 of TTS Best Variable/Elicitor r Significance

0.5 kHz Octave-Band Exposure

.75 AR Latency/Broad-band .18 .04

1.0 Pre 1.0 .38 .001

1.5 AR Adaptation/Broad-band .18 .04

.75+1+1.5 Pre (.75+1+1.5) .15 .06

total Pre (all freqs.) .14 .06

3.0 kHz Octave-Band Exposure

3.0 AR Adaptation/3.0 Octave- .17 .04 band

4.0 AR Latency/3.0 Octave-band .16 .05

6.0 AR Adaptation/Broad-band . 31 .005

3+4+6+8 AR Latency/3.0 Octave-band .19 .03

total AR Latency/3.0 Octave-band .20 .03 107

Table 22. Best two variable models, based on data for 24 subjects.

Expression Level of of TTS Best Variables Significance R R 2 ADJ

0.5 kHz Octave-Band Exposure

.75+1+1.5 RIT/Broad-band .0005 Pre (.75+1+1.5) .0002 .53 .51

total RIT/Broad-band .02 .34 .31 Pre (all freqs.) .006

(Note: No other variable combinations improved on the single variable model.)

3.0 kHz Octave-Band Exposure

6.0 AR Latency/3.0 .025 .44 .42 Octave-band Pre 6.0 .001

(Note: No other variable combinations improved on the single variable model.) 108

Table 23. Multiple regression predictive models that yielded the highest coefficients of determination for TTS resultant from exposure to 0.5 kHz octave-band noise (N = 24). NOTE: The broad-band elicitor was best in each case.

Expression Best Level of 2 2 of TTS Variables R r adj Significance

.75 Adaptation .41 .32 .03 RIT Ave. Latency Pre-exposure Threshold

1.0 Adaptation .54 .44 .01 RIT

Ave . Latency Ave. Magnitude Pre-exposure Threshold

00 1.5 All above • .37 .02

75+1+1.5 All above .60 .51 .003

total RIT .34 .28 .04

Ave . Latency Sum of Pre- exposure Thresholds 109

Table 24. Multiple regression predictive models that yielded the highest coefficients of determination for TTS resultant from exposure to 3.0 kHz octave-band noise (N = 24). NOTE: The 3.0 kHz octave-band elicitor was best in each case.

Expression Best Level of 2 2Z of TTS Variables R R ADJ Significance

3.0 Adaptation .28 N/A .03

4.0 Ave . Latency .33 .27 .04

Ave . Magnitude Pre-exposure Threshold

6.0 Adaptation .51 .41 .02 RIT

Ave . Latency Ave. Magnitude Pre-exposure Threshold

3+4+6+8 RIT .38 .29 .05

Ave . Latency

Ave . Magnitude Pre-exposure Threshold

total Adaptation .41 .32 .03

Ave . Latency Ave. Magnitude Pre-exposure Threshold CHAPTER IV DISCUSSION

Interpretation of Results

Behavioral Threshold Shift

The spatial distribution of TTS by frequency that resulted from the two-hour octave-band noise exposures

(see Figures 8 and 9) is consistent with that reported by other authors (Ward, Glorig and Sklar, 1958; Melnick, 1974;

Zakrisson, 1975; Mills, Gilbert and Adkins, 1979; Kraak,

1981) . It was expected that the low frequency exposure would result in TTS spread over a somewhat broader range of frequencies than would be the case for the high-frequency exposure. This did occur and can be explained in terms of the spread of acoustic stimulation along the basilar membrane. Low frequency energy results in a broader pattern of sensory cell stimulation. Also, the distribution of TTS was relatively closer to the center frequency of the noise for the 3.0 octave-band exposure. The basis for this difference in pattern of TTS may relate to the physical structure (and thus response characteristics) of the basilar membrane itself. However, the shaping of the

110 . ,

Ill

acoustic energy as it is transmitted to the inner ear may also be involved. The middle ear and AR contribute to that process by reducing the transmission of low-frequency energy

As shown in Tables 2 and 3, the high-frequency exposure resulted in greater TTS (as averaged across subjects) than did the low-frequency exposure. This result, and the greater range of minimum/maximum observations of

TTS by subject following the high-frequency exposure have been reported previously (Ward, 1973; Ward, Cushing and

Burns, 1976; Hamernick, Henderson, Coling and Slepecky,

1980) . However, the individual maximum TTS observation of

16.6 dB (at 1.0 kHz) following the 0.5 octave-band noise exposure was less than had been predicted. Melnick (1978) suggested that a maximum of 25 dB of TTS would occur at any given frequency for a low-frequency octave-band exposure of an intensity and duration similar to that in this investigation. Conversely, the 3.0 octave-band exposure resulted in a maximum individual TTS observation of 36.0 dB (at 6.0 kHz) which was slightly higher than the 32 dB

Melnick predicted. In practical terms, these differences are not great and can be attributed to factors such as the different earphone transducer upon which Melnick' s calculations were based. The crucial point is that the TTS created in both these octave-band exposures did not exceed the hypothetical individual frequency maximum of 40 dB which is thought to be the level above which PTS may occur 112

(Ward, Glorig and Sklar, 1959) . Recovery from TTS was complete for all subjects, as shown in Tables 8 and 9, confirming that no permanent change in hearing sensitivity

i resulted from this investigation.

Individual variation in the amount of TTS resulting from the same noise exposure is a factor that confounds attempts to predict susceptibility (Melnick, 1978; Kraak,

1981). This wide individual variation was certainly con-

firmed in the present investigation as the data in

Tables 2 through 5 illustrate. Combined expressions of

TTS such as those shown in Tables 6 and 7 are intended to

aid in the comparison of TTS between individuals. For example, if TTS is defined only as a shift in hearing

sensitivity at a particular frequency, then that definition may not describe an individual's even greater shift at

another audiometric frequency (Hepler, 1984) . Therefore, most definitions of industrial hearing damage-risk and all

hearing loss compensation formulas are based on hearing

sensitivity as measured across a range of frequencies to,

in part, account for individual variation in points of

maximum sensitivity change. However, there may be a

statistical problem involved in combining single frequency

TTS observations into collective terms (such as total TTS)

as was done in this investigation. Statistical variation

may be additive. Low individual correlation coefficient

values were expected in this study because there are so . .

113

many other auditory and non-auditory factors known to

relate to TTS . Combined expressions of TTS are necessary to realistically describe relationships but may also tend to obscure them statistically. The consequence may have been even lower correlations

Acoustic Reflex Threshold

It was expected that AR threshold would vary for the three elicitors used in this study. AR threshold is known to vary according to the spectral content of the elicitor.

As shown in Table 10, mean AR threshold was lowest for the broad-band elicitor as has previously been reported

(Popelka, Margolis and Wiley, 1976; Wilson and McBride,

1978; Hepler, 1984; Gelfand, 1984) . The mean AR thresholds obtained for all three elicitors, as well as the range of individual observations, was consistent with results reported by the above authors

Hepler (1984) found no significant relationship between AR threshold and the TTS that resulted from a broad-band noise exposure of the same intensity and duration

as the present octave-band exposures. As shown in Tables 11 and 12, there was also no significant TTS/AR threshold

relationship demonstrated for this investigation. The

concept that a more sensitive AR (as evidenced by lower 114

thresholds within a range of normalcy) might provide protection from noise is not supported.

Acoustic Reflex Magnitude

Hepler (1984) reported wide inter-subject variation in AR magnitudes resulting from the same elicitor/intensity combination. These variations were consistent across the broad-band and tonal elicitors used in that study. The present investigation confirmed Hepler' s observations using different elicitation stimuli. A difference in AR magnitude between subjects for the same elicitor/intensity combination is illustrated in Figure 10. Subjects with

relatively large AR magnitude for the broad-band elicitor

also exhibited relatively large magnitude for the two

octave-band elicitors.

The AR magnitude/intensity relationships (shown in

Figure 11) for the three elicitation stimuli are consistent with those reported in the literature (Silman, 1984). As

averaged for the thirty subjects, these functions show that

AR magnitude systematically increased with elicitation

intensity. The obtained admittance values used to plot

these functions are consistent with the reported range of

responses for similar subject populations. These averaged

functions do not represent the entire AR dynamic range .

115

(threshold to saturation of response) for these subjects since equipment and subject comfort limitations precluded testing at sensation levels that would define the upper limits of that range. Wilson and McBride (1978) suggested that the group (mean) dynamic range for similar elicitors exceeds 120 dB SPL (or a sensation level of 32 dB in reference to mean AR threshold)

Inspection of such functions for individual subjects in the present investigation showed that the slope or rate of growth in AR magnitude varied substantially for the same elicitor between subjects. Some individual growth functions demonstrated an asymptote or leveling off of response magnitude. As also reported by Wilson and McBride (1978), the functions showing these relatively broader (or complete) dynamic ranges were for subjects with AR thresholds well below the group mean value for the particular elicitor.

In order to account for this individual variation and to obtain a single value to best represent AR magnitude differences between subjects, an average AR magnitude was calculated. The AR magnitude values across 6 through 12 dB (reference AR threshold) were averaged since that range corresponded to the most linear portion of each individual

AR magnitude growth function.

The anticipated relation between average AR response magnitude and TTS was an inverse one. That is, the more .

116

vigorous the reflex, the larger the shift in acoustic immittance and the smaller the resultant TTS . Gerhardt and

Hepler (1983) and Hepler (1984) reported a consistent pattern of negative correlations that supported this proposed relationship. In the Hepler (1984) study, AR magnitude (as similarly averaged) in response to broad-band elicitation was consistently related to TTS resultant from a broad-band exposure (of similar intensity and duration as the present exposures). Correlation coefficients of -.40

(p = .01) were reported for Hepler' s 36 college-age subjects in comparisons of AR magnitude and total TTS, as well as combined TTS at 2+3+4 kHz.

If the present investigation were to support the

a stronger Hepler findings , then it was expected that relationship would be shown for the AR magnitude/low-

frequency exposure condition than for the high-frequency

exposure condition. The correlational analyses of average

AR magnitude (by elicitor) versus TTS for the respective noise exposures resulted in the expected pattern of low/

negative r values (refer to Tables 13 and 14) That trend

and the significant correlation of AR magnitude as elicited with broad-band noise to the TTS at 2+3+4 kHz (that resulted

from the 0.5 octave-band exposure) supports the concept

that AR magnitude and TTS are inversely related. .

117

Acoustic Reflex Latency

Acoustic reflex latency is reported to be inversely

related to elicitation stimulus intensity (Dallos, 1964; Borg,

1972; Bosatra, Russolo and Silverman, 1984). That is, the

more intense the elicitor, the shorter will be the onset

response latency. Figure 12 illustrates this declining func-

tion for latency data obtained with the three elicitors as

averaged across all subjects. Mean latency values at AR

threshold ranged from 385 msec (for the broad-band elicitor)

to 437 msec (for the 0.5 kHz octave-band elicitor). These

values are higher than those reported in Moller's (1984)

literature review (average reported range 150 to 250 msec)

This difference in mean values may relate to measurement

error due to differences in AR waveform morphology. There

were several subjects in this investigation whose AR latency

at threshold was repeatedly shown to be in excess of 600

msec. In most instances these relatively large estimates

of latency were due to a very gradual response rise time.

As previously noted by Bosatra, Russalo and Silverman (1984)

such uniquely slow response rise times can obscure the

point at which the response actually begins. While these

individual observations may have somewhat distorted the

mean AR latency values near threshold for these subjects,

the measures of latency above threshold were not effected.

That is, with increased elicitation levels the AR waveform .

118

was shown to be more well formed and onset latency more

readily discernable.

As with AR magnitude, inspection of AR latency/intensity

functions revealed extensive individual variation. Average

AR latency was therefore calculated (across 6 through 12

dB in reference to AR threshold) in each case. If average that AR latency has an effect on TTS , it was anticipated

it would be most pronounced for the low-frequency exposure

and would be in a positive direction. That is, for a

fatiguing stimulus centered in the spectral range known to

be effected by the AR (below 2.0 kHz) it was expected that

the sooner the response occurred the less TTS would be

resultant

The individual correlations for average AR latency

(shown in Table 15) suggest that AR latency as elicited

with a broad-band stimulus is related to the TTS resulting

from the low-frequency noise exposure. Only two correlation

coefficients in that table are negative in sign. That

overall trend supports the concept that a more rapidly

occurring AR is related with less TTS. Conversely, there

were significant inverse correlations between measures of

TTS resultant from the high-frequency exposure and average

AR latency as elicited with the octave-band stimuli (see

Table 16) . That result suggests that a delayed response

could be related to less TTS. This ambiguity is not 119

readily explained and may be an example of the more complex relation between AR latency measures and stimulus parameters.

Acoustic Reflex Adaptation

Several authors had reported a more rapid decrease in

AR magnitude over time with sustained high-frequency elicitation as opposed to low-frequency eliciation (Johansson,

Kylin and Langfy, 1967; Anderson, Barr and Wedenberg, 1970;

Habener and Snyder, 1974) . It was therefore anticipated that the rate of adaptation would be different for the three elicitation stimuli (broad-band, 0.5 kHz and 3.0 kHz octave-band noise) in the present investigation.

However, the twenty-four subjects for whom this data was available generally exhibited a similar rate of initial adaptation to each elicitor. There was a rapid declination in response magnitude within the first sixty seconds of the four-minute measurement period as averaged across subjects for the three elicitors.

While the rate with which response adaptation occurred was not substantially different among elicitation stimuli, the level at which the response was sustained throughout the elicitation did vary. It was expected that adaptation would level off after the initial decline and that the sustained response thereafter would be dependent upon the . .

120

frequency spectrum of the elicitor. Response adaptation

(onset to offset) was 41% for the 0.5 kHz octave-band elicitor. Average adaptation in response to the broad- band elicitor was 85%, which is essentially the same as that reported over a slightly longer period (7 1/2 minutes) by Hepler (1984). The AR in response to the sustained

3.0 kHz octave-band stimulus adapted a similar 85% as averaged across subjects.

The above results for the octave-band elicitors were as expected. Several authors have shown that low-frequency stimuli maintain the AR response more effectively than high-frequency stimuli (Djupesland, Flottorp and Winther,

1966; Ward, 1973; Holmes, 1978). In fact, the adaptation reported by Holmes in response to eight minutes of sustained pure tone elicitation (34.7% for a 0.5 kHz stimulus and

75.7% for a 2.0 kHz stimulus) are in agreement with the present results. Conversely, the present ordering of results for the broad-band versus the octave-band elicitors is not as predicted. It is unclear why the broad-band adaptation was the same as that for the high-frequency filtered elicitor and not the low-frequency filtered elicitor

This same pattern of results was shown for the RIT procedure. Mean RIT values for the broad-band and 3.0 kHz octave-band elicitors were similar (114 and 108 seconds)

The RIT as averaged across subjects was 191 seconds for 121

the 0.5 kHz octave-band elicitor. The meaning of order of magnitude differences in RIT scores is not known since similar data has not been published. However, this procedure measures the length of time that the AR is maintained at a level that is sufficient to be altered by brief interruptions in the elicitation signal. One would expect the significant negative correlations between RIT and standard adaptation that are shown in Table 17. The longer an active response can be shown to be maintained, the less it can be said to have adapted.

Both the RIT and standard adaptation procedures revealed a pairing of results for the broad-band and 3.0 octave-band elicitors (low RIT/high adaptation) as opposed to the 0.5 octave-band elicitor (high RIT/low adaptation).

Hepler (1984) found broad-band adaptation to be significantly related to total TTS (as resultant from a broad-

band noise exposure) . Individual correlations of data from the present octave-band exposures confirmed a relation between the broad-band RIT and adaptation results and expressions of TTS as shown in Tables 18 and 19.

Ear Canal Volume/Resonance

Subjects in the Hepler (1984) investigation demonstrated significant differences in ear canal volume that were shown 122

to have an effect on their individual points of maximum

TTS following exposure to broad-band noise. Hepler related this observation to the corresponding differences in ear canal resonance. That is, as the volume of a closed tube (such as the ear canal) is increased or decreased there is a corresponding change in its resonance. Differ- ences in the resonant frequency of the ear canal between subjects may alter the spectral content of the signal reaching the inner ear and thus may alter TTS.

Ear canal volume differences can be inferred from uncorrected static immittance measurements (that include the immittance of the volume of air between the probe

assembly transducer and the tympanic membrane) . Since the range of uncorrected static immittance observations was similar for this group of subjects and Hepler' s, it was anticipated that significant correlations would be shown between individual expressions of TTS and that measure.

The lack of significant correlations for this variable may be related to the difference in exposure stimuli. The broad-band exposure in Hepler' s study presented a signal at the ear canal that may have been altered to a greater extent by ear canal resonance. The low-frequency filtered noise in the present investigation should have been altered somewhat more than the high-frequency noise in subjects with different ear-canal resonances. However, the correlational data do not support that prediction. . .

123

Demographic Differences

It was not anticipated that the demographic variables

shown in Table 20 would be significantly correlated to TTS

Subject age was relatively homogeneous. Only two of the

thirty subjects were above age twenty-five. The lack of

correlation on this variable (given that homogeneity) is

consistent with the literature (Ward, 1980b) . Smoking

history and eye color may have been similarly affected by

sample homogeneity. That is, for a small sample with

relatively equal ages, exercise and nutritional habits,

it would not be expected that metabolic- related factors

such as eye color would be as prominent as in studies of

heterogeneous groups. The smoking history scores on these

subjects were definitely skewed toward the non-smoking

side of that scale. The range of possible scores on that

scale is zero to nine, representing calculations of various

time and level of smoking combinations (Chung et al. , 1982)

Twenty- three of the thirty subjects in the present study

had scores of zero on this measure and the maximum

observation was a four.

Previous reports of differences in the TTS resulting

for males versus females may, as suggested by Hepler (1984)

be related to physical differences such as ear canal

resonance. The lack of significant differences in TTS

based on male/female comparisons in the present study is 124

consistent with the negative finding as to ear canal volume differences discussed above.

Predictive Modeling

Ideally, the purpose of statistical modeling is to define predictive relationships. Variables shown to be correlated on individual comparisons can be considered in combination. Depending on sample size and considerations of acceptable levels of confidence and error, the merit of complete and reduced models can be tested. In such procedures variables are included only if the statistical sum of squares for error is significantly reduced. The resultant model can therefore be described with relative confidence and statistically defensible generalizations can be made.

The individual correlations between variables considered in this study and TTS were low, as predicted. The acoustic reflex is only one of many complex factors known to be involved in auditory changes due to noise exposure. How- ever, it was expected that a more consistent pattern of significant correlation values would emerge from the comparisons. Sample size may have contributed to this lack of expected pattern in results. This may have been even more of a factor in the consideration of the combined .

125

effects of variables. Those comparisons were limited to data from 24 of the subjects due to unretrievable adaptation results for the remaining six individuals. The results of single variable linear regression (as shown in Table 21) does not suggest that individual AR differences had a significant role in the development of TTS for these subjects.

The addition of two variables to the model only improved prediction in three instances (as shown in Table 22) A further addition of variables did not meet the modeling criteria for error reduction. The R2 ADJ (adjusted coefficient of multiple determination) values given in that table were calculated according to an example provided by Agresti

and Agresti (1979) . These calculated values take the relatively small sample size into account and better estimate the actual population value of that parameter.

Using these adjusted estimates it can be concluded that a model based on pre-exposure audiometric threshold (.75+1+1.5 kHz) and AR decay (as elicited with broad-band noise) explains approximately 51% of the variation in TTS (at

those same audiometric frequencies) . However, it must be remembered that the original significance of the variables included in this modeling is somewhat suspect due to the number of variables and correlations carried out versus the pattern of results.

A second criteria had been used which was much less restrictive as to the individual effect of variables 126

added. The purpose of this additional modeling was to highlight any trends in the data particularly in regard to the elicitation stimuli selected. The results of this modeling (as shown in Table 23 for the 0.5 kHz octave-band exposure) simply suggest that the selected AR variables

(as elicited with broad-band noise) and pre-exposure behavioral threshold, were most clearly related to the TTS at .75+1+1.5 kHz as resultant from the low-frequency noise exposure. Similarly (as shown in Table 24 for the 3.0 kHz octave-band exposure) the selected AR variables (as elicited with 3.0 kHz octave-band noise) and pre-exposure behavioral threshold, were most clearly related to the TTS at 6.0 kHz resultant from the high-frequency noise exposure.

It would be inappropriate to suggest that the coefficients of determination listed in the above tables represent the actual percentage of variation explained by those variables. However, given the selection criteria used

(maintaining overall significance of the entire model below p = .05) they do indicate that the AR was more significant in the development of TTS during the low frequency exposure of these subjects. The predictive relationship is slightly better, therefore, when the exposure is to a fatiguing stimulus within the spectral range known to be influenced by the AR. The extensive individual variation in expressions of TTS are such that this relation is obscured, however, and may not contribute .

127

significantly to an overall assessment of individual

susceptibility to TTS

Summary

As predicted, the level and frequency distribution of

TTS resulting from these two-hour exposures was related to

the spectral content of the noise. The exposure to a high-

frequency filtered noise resulted in a greater shift in hearing sensitivity over a relatively narrower range of

audiometric frequencies than did the low-frequency exposure.

Extensive individual variation in TTS for the same controlled exposure was confirmed.

The observations of other researchers were confirmed concerning several AR parameters. Magnitude and latency observations for individual and group mean data varied as previously reported. Threshold varied according to the elicitor used and was lowest for broad-band stimulation.

It is particularly evident that the AR remains active over the course of a noise exposure to a greater or lesser extent depending on the noise spectrum. The RIT procedure defined in this study has implications for future research applications in that regard. It may offer a method for observing the timecourse of AR activation that is not as 128

prone to measurement error as standard estimates of adaptation.

It was anticipated that any predictive relationship between dynamic properties of the AR and TTS for these subjects would be most pronounced for the 0.5 kHz octave-band noise exposure condition. The spectral content of that noise is within the range known to be most attenuated by the immittance change resultant from the AR.

While AR parameters were shown to be most significantly correlated to expressions of TTS from that low-frequency exposure, only a slight predictive relationship could be demonstrated. Sample size may have precluded more definitive results in this investigation, however, the extensive individual variation in observations of TTS 2 for these subjects also confounded prediction. There are other methods of measuring TTS (such as the mathematical integration of observations over the entire TTS timecourse) that may offer less variable results. The relation of

AR parameters to noise-induced permanent threshold shift is also still open to question. However, the results of this study do not support the usefulness of AR measurements in the prediction of individual susceptibility of TTS. . .

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BIOGRAPHICAL SKETCH

Michael Jay Moul was born September 11, 1946, in

Denver, Colorado. He is a career Army officer as well as a former Navy enlisted man. His wife is JoAnne (McDonald)

Moul, also of Denver. They have two daughters, Michele and Marcia.

Michael received the Bachelor of Science degree in psychology from Colorado State University in 1971. He received a Master of Science degree in audiology from that same university in 1973. He then was awarded a direct commission in the Army Medical Service Corps. His assign- ments have included Walter Reed Army Medical Center, in

Washington, D.C.; Fort Carson Army Community Hospital, in

Colorado Springs, Colorado; The Medical Service Corps

Academy of Health Sciences, in San Antonio, Texas; and.

The Army Environmental Hygiene Agency, at Aberdeen Proving

Grounds, Maryland. He began his doctoral studies at the

University of Florida in the fall of 1982. Upon completion of studies he will be assigned as the Chief of Audiology at Dwight D. Eisenhower Army Medical Center in Augusta, Georgia

146 I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy.

I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy.

1 Pa j KmC-O^ Patricia B. Kricos, Ph.D. Associate Professor of Speech

Alice E. Holmes , Ph.D. Assistant Professor of Speech

Associate Professor of Speech I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy.

Alan G. Agresti Professor of Statistics

This dissertation was submitted to the Graduate Faculty of the Department of Speech in the College of Liberal Arts and Sciences and to the Graduate Council, and was accepted as partial fulfillment of the requirements for the degree of Doctor of Philosophy.

May, 1985

Dean for Graduate Studies and Research I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy.

Alan G. Agresti Professor of Statistics

May, 1985

Dean for Graduate Studies and Research