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J Am Acad Audiol 22:663–677 (2011)
Teenage Use of Portable Listening Devices: A Hazard
to Hearing?
DOI: 10.3766/jaaa.22.10.5
Cory D.F. Portnuff*
Brian J. Fligor†
Kathryn H. Arehart*
Abstract
Background: Recently, a number of popular media articles have raised some concern that portable
listening devices (PLDs) may be increasing the risk for music-induced hearing loss (MIHL). However,
literature regarding adolescents’ listening behavior and how their attitudes and beliefs relate to behavior
is currently limited.
Purpose: The purposes of this study were (1) to investigate the relationship between volume control settings and output levels of PLDs, (2) to examine how adolescents’ listening behavior changes as a function of
background noise and noise isolation, (3) to investigate the relationship between self-reported listening
levels and laboratory-measured listening levels, and (4) to evaluate the validity of the Listening Habits Questionnaire as a research tool for evaluating how attitudes and beliefs relate to PLD use behavior.
Research Design: A descriptive study. Experiment 1 evaluated the output levels of a set of PLDs,
and Experiment 2 characterized the listening behavior and attitudes toward PLD use of a group of
adolescents.
Study Sample: Twenty-nine adolescents aged 13–17 yr, with normal hearing, participated in Experiment 2.
Data Collection and Analysis: Experiment 1 evaluated the output levels of a set of PLDs with stock and
accessory earphones using an acoustic manikin. Experiment 2 included survey measures of listening
behavior and attitudes as well as output levels measured using a probe microphone.
Conclusions: The output levels of PLDs are capable of reaching levels that could increase the risk for
MIHL, and 14% of teenagers in this study reported behavior that puts them at increased risk for hearing
loss. However, measured listening levels in the laboratory settings did not correlate well with self-reported
typical listening levels. Further, the Listening Habits Questionnaire described in this study may provide a
useful research tool for examining the relationship between attitudes and beliefs and listening behavior.
Key Words: adolescents, Health Belief Model, iPods, MP3 players, personal listening devices
Abbreviations: CLL 5 chosen listening level; DRC 5 damage-risk criteria; EAECL 5 estimated ambient
ear canal noise level; HBM 5 Health Belief Model; LHQ 5 Listening Habits Questionnaire; MIHL 5
music-induced hearing loss; MIRE 5 microphone-in-real-ear; NIOSH 5 National Institute for
Occupational Safety and Health; OSHA 5 Occupational Safety and Health Administration; PLD 5
portable listening device; RMS 5 root mean square
S
everal recent research studies, as well as reports
in the popular media, have raised concerns that
the use of digital portable listening devices
(PLDs) contributes to the development of hearing loss,
particularly in young people (Williams, 2009; Keppler
et al, 2010; Shargorodsky et al, 2010). However, the
*University of Colorado at Boulder; †Children’s Hospital Boston, Harvard Medical School
Cory D. F. Portnuff, Au.D., Ph.D., 409 UCB, Speech, Language and Hearing Sciences Department, University of Colorado at Boulder, Boulder, CO
80309-0409; Phone: 303-492-0067; Fax: 303-316-7061; E-mail: [email protected]
Support for this work was provided by Children’s Hospital Boston, Department of Otolaryngology and Communication Enhancement, as well as the
American Speech-Language Hearing Association’s Students Preparing for Academic and Research Careers Award.
Parts of this work were presented at the Noise Induced Hearing Loss in Children Conference, Covington, KY, October 2006, and the National
Hearing Conservation Association Conference, Atlanta, GA, February 2009.
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Journal of the American Academy of Audiology/Volume 22, Number 10, 2011
evidence linking hearing loss to the use of PLDs is
sparse and thus makes it difficult to effectively evaluate
the claims made by the popular media. The present
study examines how self-reported PLD use of a group
of adolescents relates to laboratory measures of listening behavior. These results are also analyzed in regard
to hearing loss damage-risk criteria and used to identify
factors that contribute to teenagers’ choice of listening
levels.
BACKGROUND
W
ith the exception of age-related hearing loss,
noise-induced hearing loss (NIHL) is the most
common form of acquired hearing impairment
(National Institutes of Health, 1990). A significant body
of research indicates that adults exposed to noise for
extended duration are at a significant risk of hearing loss
(Mencher et al, 1997; Ward et al, 2000). Damage-risk criteria (DRC) have been adopted by the National Institute
for Occupational Safety and Health (NIOSH, 1998), the
Occupational Safety and Health Administration (OSHA,
1981), and the European Parliament and Council of the
European Union (EPCEU) (2003) indicating the “acceptable” risk for noise-induced permanent threshold shift in
adults exposed to occupational noise. For each DRC, the
permissible exposure level or recommended exposure
level is different (80, 85, or 90 dBA), and the time–intensity
trading ratio is different (3 or 5 dBA). While each
organization’s choice of formula for calculating risk
has been justified for use on an adult population, it is
unknown if these risk estimates are similar in an adolescent population. In each DRC, exposure can be expressed
in terms of a daily noise dose (in percent), where 100%
noise dose is equivalent to the maximum permissible or
maximum recommended exposure; typically, this is the
equivalent of 8 hr of exposure at a given criterion level
(80, 85, or 90 dBA). Noise dose is a cumulative measure,
and exposures from individual activities in a given day
are added together to calculate a daily noise dose.
Exposure to high levels of music has been of recent
interest in popular media and peer-reviewed literature
(Williams, 2009; Keppler et al, 2010; Shargorodsky
et al, 2010). Listening to music as a leisure activity can
also expose adolescents to high sound levels in a variety
of environments, including urban music clubs, concert
halls, and discotheques (Royster et al, 1991; Gunderson
et al, 1997; Serra et al, 2005). Adolescents may also be
exposed to high sound levels through the use of PLDs.
Several studies have established that older PLD technology, including cassette tape players and compact disc
players, are capable of producing high output levels that
could increase the risk of music-induced hearing loss
(MIHL [Airo et al, 1996; Fligor and Cox, 2004]).
The current generation of digital PLDs is also capable
of producing output levels that might increase the risk
for MIHL if used for extended durations (Keith et al,
2008; Keppler et al, 2010). As with older technology,
output levels of digital PLDs vary depending on the type
of earphones used. For example, higher output levels
can be produced by some aftermarket earphones than
by stock earphones (Fligor and Cox, 2004). Keith
et al (2008) reported that the maximum level from digital PLDs ranges from 83.4 to 107.3 dBA, depending on
the earphone, the maximum PLD output voltage, and
the recorded level of the music. If the actual fit of the
earphones in the ear is considered (well fit vs. loosely
fit), the range of maximum output expands further.
Additionally, digital PLDs have the potential to be used
for longer durations than older technologies, as digital
PLDs have expanded music storage and battery life
capabilities. Following occupational DRC, potential
damage to hearing from PLD use will be dependent
on both listeners’ actual chosen listening levels (CLLs)
and durations of exposure. Just because PLDs may produce high maximum output levels does not mean that
users of PLDs actually listen to music at these high output levels. CLLs are dependent, in part, on the background noise levels. In the presence of background
noise, listeners increase their CLL proportionately to
the levels of the background noise, seeking a desired
signal-to-noise ratio (SNR [Fligor and Ives, 2006]). Earphones that isolate listeners from the ambient noise
levels by either passive isolation or active noise cancellation could allow listeners to choose lower listening
levels while retaining their desired music-to-noise
ratio.
Two different techniques can be used in the estimation of CLLs. First, listeners can be surveyed about
their typical CLLs, using either a rating scale that
is related to a PLD’s volume control or qualitative
reports of loudness. Self-reported CLLs are useful
for estimating real-world PLD usage, as listeners
can report both the level at which they typically listen
and the duration of their listening. However, the validity of listeners’ self-reports has not been established,
and it is unclear whether self-reported CLLs are similar to actual CLLs. Second, listeners’ CLLs can be
measured in the laboratory using either a microphone-in-real-ear (MIRE) technique (ISO 11904-1
[2002]) or a technique employing a manikin with an
ear-simulating microphone (ISO 11904-2 [2004]). In
either of these measurement techniques, care must
be taken to ensure that recorded sound output levels can be compared against DRC. As current DRC
assume a diffuse-field noise source, a diffuse-field equivalent transfer function must be applied to any measurements recorded in the ear canal using a diffuse-field
inverse filter as described in ISO 11904-2 (2004). While
laboratory measurements of CLL can be obtained with
high levels of accuracy, it is often unclear how they relate to real-world music exposure.
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Teenage MP3 Player Use/Portnuff et al
Self-reports of listening behavior have been used by
several researchers to establish patterns of behavior.
Across surveys of college students, the majority of listeners use PLDs around 2 hr per day, with 12% listening 3 to
5 hr per day and 4 to 5% of listeners listening greater
than 5 hr per day (Ahmed et al, 2006; Torre, 2008;
Danhauer et al, 2009). Surveys of CLL have asked people
about their typical settings on the volume control of their
PLDs. For example, Ahmed and colleagues (2006) asked
college students to report their preferred setting by a percentage of the volume control. The average setting of this
group was 60% of the maximum volume, with 14% of listeners reporting levels greater than 80% of maximum
volume. Hoover and Krishnamurti (2010) asked participants to report their preferred volume settings by quartiles of the volume control. About half of the listeners
reported using their players above 50% of maximum volume, and 23% of listeners reported listening between 75
and 100% of the maximum volume level. Danhauer et al
(2009) used a 1–10 Likert scale to rate preferred volume
control setting, finding that 21% listened at a “6” on the
scale, 25% listened at a “7,” and 26% listened between “8”
and “10.” Across all of these surveys, it is clear that a
small but substantial group of PLD users choose high
CLLs, and at least some of this group could be increasing
their risk for acquiring MIHL.
Another set of studies has attempted to evaluate listeners’ CLLs by measurement using objective techniques (e.g., the manikin or the MIRE technique),
obtaining some mixed results. Williams (2005) measured the CLLs of adult PLD users passing through
noisy public areas by placing participants’ earphones
on a manikin and found a mean CLL of 86.1 dBA. When
self-reported listening times were taken into account,
the mean exposure was 79.8 dB LAeq,8h, and 25% of
users exceeded an estimated 85 dB LAeq,8h. A followup study using the same methods found a significantly
lower mean CLL in 2008 of 81.3 dBA, with 17% of listeners exceeding 85 dB LAeq,8h (Williams, 2009). Using
similar methodology, though using a recording system
instead of a manikin, Epstein, Marozeau, and Cleveland
(2010) measured the outputs of iPod users in a subway,
on a busy street, in a library, and in a student center.
The authors found that out of the 64 users evaluated,
none chose levels greater than 85 dBA and that the
maximum recorded NIOSH noise dose was 10%. Using
an MIRE technique, Fligor and Ives (2006) evaluated
the CLLs of 100 graduate students ranging in age from
20 to 46 yr (mean: 23.8 yr). In this group, 6% of listeners had CLLs that exceeded 85 dBA in quiet. The researchers also measured CLLs in several levels of
background noise, pink noise from 50 to 80 dBA, a restaurant background noise, and an airplane background
noise. A linear relationship between CLLs and the level
of background noise was identified for each earphone.
The authors reported that significantly lower CLLs
were chosen when the listeners used isolator earphones
compared with earbuds or supra-aural earphones with
little background noise attenuation.1
Another group of college-age students was evaluated
by Hodgetts, Rieger, and Szarko (2007) using an MIRE
technique. In this study, listeners had mean CLLs of
76.0 dBA in quiet, 83.7 dBA in a 70 dBA multitalker babble, and 85.4 dBA in 70–80 dBA street noise. Additionally, listeners chose higher output levels when using
earbuds than when using supra-aural-style earphones,
and the authors conclude that supra-aural earphones
reduce the risk of MIHL when used in noise. However,
the authors describe the earphones as a closed style that
could provide active noise cancellation; these earphones
also provide, by design, some passive attenuation of
background noise (whether or not the active noisecanceling circuit is engaged). Some or all of the effect
of earphone style may be due to background noise attenuation in the supra-aural style. Further, the authors
present CLLs as measured by a probe microphone at
the eardrum without applying a diffuse-field equivalent
transfer function. Thus, the numbers reported cannot
be compared with either occupational DRC or with
other studies that report diffuse-field equivalent levels.
A follow-up study by the same authors measured CLLs
while exercising and in background noise, finding a
significant increase in CLL both when exercising and
when resting in the presence of background noise
(Hodgetts et al, 2009). However, the CLLs were similarly not reported as diffuse-field equivalent levels, precluding a direct comparison with DRC.
In order to gain a better understanding of how teenagers use PLDs, the present study consisted of two
experiments designed to assess listeners’ self-reported
and laboratory-measured CLLs. In the first experiment,
a series of measurements of PLD output levels were
taken using an acoustic manikin technique. These
measures were designed to help understand the effect
of various earphones on the output levels of a set of
PLDs, providing data to relate a volume control setting
to an actual output level in A-weighted, diffuse-field
equivalent decibels. In the second experiment, CLLs
were measured in the laboratory using an MIRE technique, with measurements in the presence of several
different levels and types of background noise, using
three different styles of earphones. Additionally, a Listening Habits Questionnaire (LHQ) was developed to
allow adolescents to report their typical volume control
levels, including what style of earphone they used.
Using the data collected in Experiment 1, which provided a conversion factor for estimating the output level
of a PLD from the volume control level, the participants’
self-reported volume control levels were converted to
output levels in decibels.
To gain insight into the psychosocial factors that influence adolescents’ CLLs, the LHQ included questions
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Journal of the American Academy of Audiology/Volume 22, Number 10, 2011
based on the Health Belief Model (HBM [Rosenstock,
1960]). The HBM is a widely used conceptual framework designed to model the factors underlying health
behaviors. The HBM provides a model with several
generic constructs that can be adapted to explain
the beliefs behind specific health behaviors. Since its
creation in the 1950s, the HBM has been applied to a
wide variety of health behaviors (Hochbaum, 1958;
Rosenstock, 1960; Janz et al, 2002). The components
of the HBM are based on the theory that people will take
actions to change health behaviors if they feel susceptible to a condition with consequences they feel are serious and will take actions if the benefits of taking action
will outweigh the barriers to taking action (Janz et al,
2002). The constructs measured in the traditional HBM
include the following: perceived susceptibility, perceived severity, perceived benefits, and perceived barriers. For behaviors requiring lifestyle changes, a
construct of perceived self-efficacy to take action can
also be included in the HBM (Rosenstock et al, 1988).
The use of the HBM has been validated in several areas
of health behaviors, including nutrition education, mediation compliance, and beliefs about hearing loss (Becker
et al, 1974; Abood et al, 2003; Rawool and ColligonWayne, 2008). The HBM has been identified as particularly effective in explaining preventative health
behaviors (Janz and Becker, 1984).
Though they are common in other disciplines, few
studies in the field of hearing conservation have used
models of health beliefs to evaluate the factors predictive of health behaviors. Several studies have evaluated
the correlates of adolescents’ exposure to loud music,
though none to date have used a validated health
behavior model, such as the HBM, to look specifically
at listening behaviors for PLDs (Vogel et al, 2007). Additionally, cognitive theories of health models suggest
that adolescents’ risk perception and knowledge impact
their choices in health behaviors (Greening et al, 2005;
Reyna and Farley, 2006). The action of “choosing moderate listening levels” is, in itself, a preventative health
action. As the HBM has been validated for explaining
preventative behaviors, it has been suggested as a good
model for understanding knowledge, beliefs, and attitudes about PLD use (Sobel and Meikle, 2008).
The goal of this study is to examine adolescents’ perceptions of their PLD use compared with laboratory
measurements of CLL, as well as to model how adolescents’ beliefs about PLDs and hearing loss relate to
actual behavior. Specifically, this study is designed to
evaluate the following research questions:
1. What is the relationship between volume control
settings and output levels of current-generation
PLDs?
2. How do adolescents’ CLLs on digital PLDs change as
a function of background noise and earphone style?
3. What is the relationship between self-reported listening levels and CLLs measured in the laboratory?
4. How effective is the use of the LHQ in predicting adolescents’ CLLs?
EXPERIMENT 1: OUTPUT LEVEL
MEASUREMENTS
I
nitial research by Keith et al (2008) and Keppler et al
(2010) has indicated that PLD output levels could
reach a level that increases the risk of MIHL. These
studies provide excellent information and analysis
showing the maximum output levels for several PLDs
but do not provide data on output levels beyond the
maximum. In order to assess CLLs by self-report, listeners must be given a metric with which to report their
behavior. On many PLDs, a volume control provides a
measure that can be represented by a visual analogue
scale. However, to be able to convert a volume control
setting to a CLL, it is critical to understand the relationship between volume control settings and PLD outputs.
The available published data do not provide enough
information to convert a volume control level to actual
sound output levels. Thus, Experiment 1 examines the
output level of PLDs in relation to the volume control for
several PLDs and types of earphones.
METHOD
Digital Music Players and Headphones
Five commercially available PLDs were evaluated:
an Apple iPod, an Apple iPod Mini, an Apple iPod Nano,
a Creative Zen Micro, and a SanDisk Sansa. Each
device was purchased new prior to data collection
and came with a set of stock (earbud-style) earphones.
These players were chosen because they represented a
cross section of the available devices on the market at
the time of data collection, in late 2005. As of the third
quarter of 2005, the Apple corporation held 44.8% of the
market share for all digital music players and 71.5% of
the hard-disk-based digital music player market, which
is reflected in the choice of players for this study
(Canalys, 2005). To examine the effect of different styles
and brands of earphones, output measurements were
taken using the stock earbud-style earphones that were
packaged with each player, as well as with a set of Apple
In-Ear earphones, Etymotic Research ER-6i isolator
earphones, Shure ER4c isolator earphones, and a set
of Koss supra-aural earphones.
Apparatus
Recordings were taken through a KEMAR manikin
(G.R.A.S. Sound & Vibration, Denmark). In the KEMAR
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Teenage MP3 Player Use/Portnuff et al
used, the pinna of the left ear was made of a softer silicone rubber than the right ear, creating a better fit of the
earphone in the left ear than in the right ear. The earsimulating microphones were connected through ER-11
preamplifiers to an analog-to-digital converter (Echo
Event) to a personal computer. A custom-designed Matlab 7.0 (Mathworksª, Needham, MA) program recorded
the input to a 16-bit stereo .WAV file with a sampling
rate of 44.1 kHz. The diffuse-field inverse filter of the
ER-11 preamplifiers was engaged during recording,
applying a transfer function to the signal so that the output was equivalent to diffuse-field recordings based on
manufacturer data.
at lower volume control levels were calculated using
the regressions obtained by the pink noise recordings.
Consistent with ISO 11904-2 (2004), A-weighting was
then applied to the .WAV file through a digital filter in
Matlab, and RMS averages and peak levels were calculated. Additionally, a voltmeter was used to measure the voltage output by each player for a 1000 Hz
tone at 10% increments of the volume control.
Between each measurement, the earphones were
removed and replaced on the KEMAR. To determine
the reliability of the measurements, all of the pure
tone trials and 39 of the combined 151 pink noise
and music trials were repeated three times. A total
of 1024 recordings were completed.
Output Level Measurement Procedure
The top-ranked songs on the iTunes Web site at the
time of data collection (www.itunes.com) were chosen
from the categories Rock, R&B, Dance, Top 40, and
Country and were purchased from a commercial music
sales Web site. A 60 sec recording section was chosen
for each song, starting at the beginning of the first chorus, to be used for analysis. The downloaded songs
were transferred to the PLDs following the manufacturers’ directions via proprietary software where necessary. The songs are listed in Table 1. Additionally,
recordings of pink noise and of a 1 kHz tone were generated at equal root mean square (RMS) levels in
Adobe Audition 1.5 (Adobe Systems Inc., San Jose,
CA), recorded to a 16-bit stereo .WAV file at a sampling rate of 44.1 kHz, and transferred to each of
the PLDs.
For each recording trial, the chosen earphones were
placed on both of KEMAR’s ears, and the recording
was started at the chosen time. For recordings of
the pure tone and noise, measurements were taken
at 10% increments of the volume control 10–100%
for each player and each set of earphones. The Apple
and Creative products have a visual indicator of volume level, which was measured, divided into the 10%
increments, and marked on the player. The SanDisk
PLD had a variable volume control with incremental
steps achieved by pushing a volume button. The total
number of steps was counted and divided into the 10%
increments. For music samples, recordings were
taken at 100% of the volume control, and output levels
Table 1. Music Samples Used to Evaluate Player
Output Levels
Artist
James Blunt
Nine Inch Nails
Madonna
Mary J. Blige
Kenny Chesney
Song
Genre
“You’re Beautiful”
“Every Day Is Exactly the Same”
“Sorry”
“Be Without You”
“Living in Fast Forward”
Top 40
Rock
Dance
R&B
Country
RESULTS
Output Voltage and Sound Pressure Levels
A comparison of earphones across each player indicates that an incremental increase in volume results in
a linear increase of output both in voltage and in diffuse-field equivalent A-weighted decibels. This linearity of change was independent of earphones used, and
no earphones added nonlinear distortion to the output.
Table 2 shows the incremental increase in volume for
each player, as measured using pink noise. The average increase in output level for a 10% increase in the
volume control for all players was 6.2 dBA, approximately doubling the sound pressure with a 10%
increase in volume. Table 2 also shows the maximum
voltage output of each player using a 1000 Hz tone. At
100% of the volume control, the peak sound pressure
levels for the music studied ranged from 104.6 dBA
to 126.9 dBA using the Creative Zen player with the
iPod In-Ear earbuds. The highest peak level measured
for any stimulus was 126.9 dBA for the Creative Zen
player with the iPod In-Ear earphones. For all music
samples, the average difference between peak level
and RMS was 18 dB.
Effect of Earphone Fit on Output Levels
Measurements were taken simultaneously through
the hard and soft pinnae on the KEMAR manikin.
The difference in hardness of the pinna caused a noticeably different fit of the earbud style of earphones. As
judged by the experimenters, the soft pinna created a
good fit, and the hard pinna created a worse fit for
the earbud. The difference in output levels due to earphone fit is shown in Table 3, which reports the average
difference between the hard and soft pinnae when playing pink noise signals. The soft pinna, in the opinion of
the researchers, was more similar in hardness to
human pinnae. Therefore, all further measurements
reported reflect the better fit of the soft KEMAR pinna.
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Journal of the American Academy of Audiology/Volume 22, Number 10, 2011
Table 2. Incremental Increase in Output Level with 10% Increase in Volume Control
Player
iPod
iPod Mini
iPod Nano
Creative Zen Micro
SanDisk Sansa
Output Level Increase with
10% Volume Control
Increase (dBA)
Voltage at 100%/10% of
Volume Control,
1 kHz Tone (mV)
Output of 1 kHz Tone/Pink Noise Through
Stock Earphones at 100% of Volume
Control, “Ideal” Fit (dBA)
5.93
6.10
6.30
5.81
6.61
534/0.9
577/0.93
450/0.75
448/1.0
284/0.35
112.7/98.9
113.1/99.5
111.7/99.1
112.9/101.0
113.4/104.4
Differences Between Players, Stimuli,
and Earphones
Figure 1 shows the output levels of five models of MP3
players averaged across all music conditions using
stock earphones. A one-way analysis of variance
(ANOVA) noted no significant differences in maximum
outputs among players using all five music signals (F
[4,124] 5 0.20, p 5 .94). Additionally, one-way ANOVAs
revealed no significant differences among players for
pink noise (F[4,24] 5 0.38, p 5 .82) and a 1 kHz tone
(F[4,24] 5 1.05, p 5 .39). The average maximum RMS
output level across all players, using the music samples,
was 100.8 dBA, with a standard deviation of 1.24 dBA.
As identified above, the linearity of the volume control
setting (in dBA) can be seen in Figure 1.
When averaged across all players using stock earphones, no significant differences were found among
the five genres of music and the pink noise stimuli
(F[4,124] 5 .07, p 5 1.0). All differences in output levels
between genres of music were less than 1 dB. A oneway ANOVA found significant differences between the
signals (F[5,150] 5 20.5, p , .001). A Scheffe post hoc test
indicated that the differences are a result of differences
between the 1 kHz tone and each of the music genres,
and no differences were noted between the music genres
and pink noise. For all players, the 1 kHz tone was 7.2 dB
higher than the pink noise stimulus.
Table 3 shows the maximum output levels of each
earphone averaged across the music stimuli. A oneway ANOVA identified a significant difference among
the maximum output levels of the earphones when
averaged across all music genres (F[4,124] 5 85.3,
p , .001). A Scheffe post hoc test revealed significant
differences in output level among all pairs of earphones
except between the Shure E4c earphone and the Etymotic Research ER-6i earphone. The isolator-style earphones produced higher output levels than the earbuds
and supra-aural earphones.
In order to assess the reliability of this method, intraclass correlations for these measurements were obtained
using a one-way random effects model. For the betterfitting ear, an intraclass correlation of 0.975 was found,
and for the worse-fitting ear, an intraclass correlation of
0.954 was found. These correlations indicate that the
measurement procedure obtained stable, reliable measurements. Additionally, the mean range between the
highest and lowest level for each trial was 1.1 dB, with
a median of 0.5 dB. These small ranges and high intraclass correlations indicate good test–retest reliability.
DISCUSSION
T
able 4 shows the average listening time necessary
for each style of earphone to reach 50% daily noise
dose, using the NIOSH DRC, which the authors suggest
to be the maximum exposure from PLD alone (given a
person may be exposed to other intense noise during the
day). A general rule of thumb one might give PLD users
is that they can listen to their music at 80% of the maximum volume control for 90 min per day, using the earbud earphones that are purchased with the PLD,
without increasing their risk for MIHL.
An additional concern reported in the literature has
been that of acoustic trauma due to transient sounds in
music samples, such as cymbal crashes. Though many
of the peaks noted in the music samples could be considered to be transient sounds, none exceed the critical
level noted by Price (1981) of 132 dB for causing acoustic
trauma to tender ears. Thus, though the peak levels
add to the overall RMS level of the recording, their presence does not inherently increase the risk of hearing
loss. It is interesting to note that the highest peak level
(126.9 dBA) identified here is somewhat lower than the
highest peak level found for CD players (139 dB SPL
[Fligor and Cox, 2004]).
Table 3. Average Maximum Outputs in A-Weighted
Decibels of Each Earphone, Averaged Across All Players
and All Music Signals, with Ranges in Parentheses,
and the Difference Between Good and Ideal Fits Using
Pink Noise
Earphone
Average Maximum
Output
Level (dBA)
Stock earbuds
iPod In-Ear
Shure E4c
ER-6i
Koss supra-aural
100.8
102.3
105.1
103.2
96.7
(96.3–103.9)
(97.8–104.5)
(102.3–107.6)
(99.2–105.2)
(89.5–103.9)
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Difference Between
“Good” Fit and
“Ideal” Fit (dBA)
7.47
1.09
1.18
1.87
1.17
Teenage MP3 Player Use/Portnuff et al
relationship between attitudes and beliefs regarding
PLD use and listening behaviors measured through
self-report and in the laboratory.
METHOD
Listeners
Figure 1. Diffuse-field equivalent output levels of five MP3 players, using stock earphones, as a function of volume control settings.
The Grand Average is the mean of all music genres across all players. Error bars represent 61 SD of the Grand Average.
The output levels reported in this experiment encompass the highest levels reported by Williams (2005,
2009), though comparisons between the studies are difficult, as Williams did not differentiate by model of MP3
player or earphone. Additionally, the maximum output
levels reported in this study are lower than those of
Hodgetts and colleagues (2007), a difference that is
likely due to the fact that Hodgetts et al (2007) did
not correct for the transfer function of the open ear to
arrive at a diffuse-field equivalent level. A more direct
comparison can be made between the present data and
those reported by Keith et al (2008). They identified
maximum RMS average levels for stock earphones
between 101 and 107 dBA, which are slightly higher
than those found in this study, although Keith et al
(2008) evaluated many more PLDs and earphones that
this study did. Additionally, Keith and colleagues found
similar voltage output levels as this study, as well as a
similar output level disparity between the better- and
worse-fit earphones. The results for earbud-style earphones are very similar to those found by Keppler
and colleagues (2010).
EXPERIMENT 2: CHOSEN LISTENING
LEVEL MEASUREMENTS
A
s Experiment 1 showed that PLDs are capable of
producing output levels that could increase listeners’ risk of MIHL, Experiment 2 evaluated the CLLs of a
set of adolescents using both laboratory measures and
self-report measures. A new survey tool, the LHQ, was
created to assess attitudes and beliefs about PLD use,
as well as assessing individual usage of PLDs. Selfreported volume control levels were converted to estimated output levels on a decibel scale using the data
from Experiment 1. Experiment 2 also evaluated the
Twenty-nine normal-hearing teenagers, comprising
12 males and 17 females between the ages of 13 and
17 yr (mean age: 14.4 yr), were recruited from the Denver
and Boulder, Colorado, metropolitan areas. All participants reported using an MP3 player at least 2 hr per
week and had hearing thresholds within the normal
range (15 dB HL or better) in both ears at octave frequencies from 250 to 8000 Hz, as well as at 3000 and 6000 Hz.
All participants provided informed assent to the study,
and parental permission was provided for all participants, under a protocol approved by the Institutional
Review Board of the University of Colorado at Boulder.
Music Files
Twenty-three songs representing the most popular
songs in several musical genres were downloaded from
the Apple corporation iTunes Web site. Songs were converted to .WAV format, lead-in and lead-out sections
comprising silence were removed, and all songs were
equalized by RMS voltage levels using a custom Matlab
routine. Each .WAV file was then uploaded to an Apple
fourth-generation iPod Classic (Apple Inc., San Jose, CA).
Procedures for Determining Chosen
Listening Level
Participants were asked to choose one song from the
list of available music files that was representative of
the music genre they preferred to listen to. Consistent
with ISO 11904-1 (2002), a probe microphone (Etymotic
Research ER7c, Elk Grove Village, IL) was placed into
the ear canal and secured to the ear with medical tape.
Prior to insertion, the probe microphones were marked
Table 4. Average Time to 50% Noise Dose
(8 hr 85 dB LAeq) Using National Institute for Occupational
Safety and Health Damage-Risk Criteria
Maximum Listening Time per Day
% of Volume Control
10–50
60
70
80
90
100
Earbud
No limit
No limit
6 hr
90 min
22 min
5 min
Isolator
Supra-aural
No limit
14 hr
3.4 hr
50 min
12 min
3 min
No limit
No limit
19 hr
4.6 hr
66 min
16 min
Note: “Earbud” includes stock earphones and iPod In-Ear
earphones. “Isolator” includes Etymotic ER-6i and Shure E4c.
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Journal of the American Academy of Audiology/Volume 22, Number 10, 2011
at a length of 20 mm, and otoscopy was performed following insertion to ensure that the silicone tubing was
within 5 mm of the eardrum. The investigator monitored insertion of the earphone into or onto the ear to
ensure that the probe microphone was not dislodged.
Using the Apple iPod, the participant’s chosen song
was set on repeat with the volume control set to zero,
and the participant was instructed to “turn the volume
up to the level you like” once the listening trial started.
The display of the iPod was obscured from the view of the
participant. During each listening trial, a 30 sec recording was taken using a custom Matlab recording routine,
and RMS average levels were calculated with both Aweighting and diffuse-field inverse filters applied digitally.
This procedure was repeated for each earphone and in
each background noise condition. All measurements
were completed in a double-walled sound-attenuating
booth with an ambient noise level of 13 dBA, complying
with American National Standards Institute (ANSI) S3.
1-1999 standard for maximum permissible ambient
noise levels in audiometric test rooms.
CLLs were measured for all subjects for three different
sets of earphones. These earphones were each representative of a popular style of earphone and included (1) a
set of Apple iPod earbuds, (2) a set of supra-aural Sony
MDR-110LP earphones (Sony Corporation, New York,
NY), and (3) a set of Etymotic Research ER-6i IsolatorTM
earphones (Etymotic Research, Inc., Elk Grove Village,
IL). Participants were instructed to move the earphone
into a position that was comfortable. For the in-ear isolator-style earphone, participants were provided with
the instructions that are included with the earphones
and were not coached on obtaining an optimal fit. Participants listened to each earphone in a randomized order
within seven background noise conditions, including
pink noise presented at 50, 60, 70, and 80 dBA; a recording
from a bus at 70 dBA; and a recording of an airplane cabin
at 75 dBA. The environmental recordings were downloaded from the Free Sound Project (www.freesound.
org), an open-source repository for sound recordings.
A “Quiet” condition was also included, with no noise
stimulus presented (13 dBA ambient noise). One noise
condition was repeated at random for each earphone to
assess the reliability of measurements. When present,
background noise was presented from four loudspeakers in the corners of the sound-attenuated booth,
and levels were confirmed via a Brüel & Kjaer Type
2230 sound-level meter prior to data collection.
the inward- and outward-facing flanges around the
probe tube to attempt to obtain the same sound isolation
as the original ER-6i flange. A 60 dBA pink noise stimulus was presented through the loudspeakers, and two
6 sec measurements were taken through the probe microphone first with an open ear canal and then with
the earphone in place. The difference between the RMS
average levels of the two measurements is reported as
the noise-isolation level of the earphone.
Listening Habits Questionnaire
Participants also completed an LHQ, which assessed
both listening behaviors and attitudes and beliefs about
listening levels. To assess listening behaviors by a selfreport measure, the LHQ asked participants what type
of earphones they typically use and how long they usually listen to their MP3 player during a day. Using a 1–10
scale, with 10 representing maximum volume, participants were asked to rate the volume setting at which
they “usually” listened, what volume setting is “comfortable,” and what volume setting is “slightly too loud.” Participants were allowed to look at their own PLD or a
laboratory PLD to help them gauge volume settings. Participants were encouraged to ask for clarification on any
questionnaire items that they did not understand.
To evaluate the use of the constructs of the HBM as
predictors of CLL, each of the HBM constructs was
incorporated into the LHQ. The questionnaire (Appendix 1) included a total of 26 questions, each designed to
represent a part of an HBM construct. The wording of
these questions was derived from Bryan et al (1997),
who used a survey to assess condom use in undergraduate women. Readability analysis of the LHQ indicates
that it has a Flesch-Kinkaid grade level of 7.0 and a
SMOG Index of 6.8, indicating that it should be understandable to the study population (aged 13–17). The
questions were based on a Likert scale with a range
of 1 to 7, with high numbers indicating agreement with
the statement. The questions for each construct were
averaged into a subscale. The traditional constructs
of the HBM were interpreted with regard to MIHL
and PLD use and were represented by questions asking
about perceived susceptibility to MIHL from PLD use,
the perceived severity of MIHL, the perceived benefits
of preventing MIHL, the perceived barriers to taking
action to prevent MIHL, and the perceived self-efficacy
in taking action to prevent MIHL. The specific questions asked can be found in Appendix 1.
Noise Isolation Measurements
RESULTS
A noise isolation measurement was completed for each
earphone using the MIRE technique described above.
For noise isolation measures of the ER-6i, a hole the size
of the probe tube was made in the earphone flanges, and
the probe tube was inserted. A drop of glue was placed on
Measured Chosen Listening Levels
Plotted in Figure 2 are the CLLs measured in the laboratory as a function of the background noise level
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Teenage MP3 Player Use/Portnuff et al
condition, divided by earphone. In this figure, 70 dBA
pink noise was combined with the 70 dBA bus noise.
As expected, CLL increases as the background noise
increases for all earphones, though the absolute CLLs
are higher for both earbuds and supra-aural earphones
than for isolator earphones. Interestingly, listeners’
standard deviations for CLL decreased as the background noise level increased. A two-factor ANOVA indicated that significant differences in CLL were present
within both earphone style and background noise level,
though no significant interaction was identified
between the two variables (F[17,689] 5 22.9, p , .01).
Scheffe post hoc tests indicated that levels measured
with the isolator earphone were significantly lower
than those measured with the earbud or supra-auralstyle earphones ( p , .01). No significant differences
were found between earbuds and supra-aural earphones. Additionally, Scheffe post hoc tests also indicated that CLLs were significantly different ( p , .05)
among all pink noise conditions except Quiet and
50 dBA pink noise. CLLs for 70 dBA bus noise were significantly different from those in Quiet and 50 dBA,
60 dBA, and 80 dBA pink noise conditions but not for
the 70 dBA pink noise. CLLs for 75 dBA airplane noise
were significantly different from those in Quiet and
50 dBA and 60 dBA pink noise conditions.
For each participant, the amount of sound isolation
provided by each earphone was measured. As expected,
isolator-style earphones provided the most attenuation (mean 5 8.4 dB, range: 2.3–14.5 dB), followed by
earbud-style earphones (mean51.9 dB, range: 0–5.2 dB)
and supra-aural earphones (mean 5 0.6 dB, range:
0–6.1 dB). A one-way ANOVA indicated that significant
differences were present in noise-isolation levels among
all earphone styles (F[2,687] 5 793.4, p , .01). These
earphone differences might influence a subject’s CLL,
as the level of the background noise reaching the ear-
drum will be affected by the noise isolation provided
by the earphone. Shown in Figure 3 are each subject’s
CLLs as a function of individual estimated ambient ear
canal noise level (EAECL), providing a view of the
group’s performance as a whole. The EAECL was calculated by subtracting the measured earphone isolation
from the known background noise level to provide an
estimate of the actual background noise level at the eardrum with the earphone in place. The EAECL removes
the variability of earphone isolation from this measure,
and when CLL is plotted against the EAECL, the
impact of background noise on CLL can be seen, regardless of the type of earphone used. A linear regression
line fit to these data explained 31% of the variance in
the data set (r2 5 0.31, p , .05). The average SNRs were
calculated by subtracting the EAECLs from the measured CLL. Table 5 shows that the average SNR
decreases as the background noise increases. A oneway ANOVA found significant differences among CLLs
in the background noise conditions (F[5,684] 5 66.6, p ,
.01). A Scheffe post hoc test found significant differences
in CLL among the Quiet, 50 dBA, 60 dBA, and 70 dBA
background noise conditions ( p , .05).
Figure 4 shows the percentage of participants who
chose listening levels above 85 dBA. Because the mean
levels of isolation provided by earbuds and supra-aural
earphones (0.6 dBA and 1.9 dBA, respectively) were
not significantly different, these two earphone styles
were combined for data analysis. CLLs were higher
when participants were using earbuds and supraaural earphones, which together provided an average
1.3 dBA of isolation, than when participants were
using isolator earphones, which provided an average
8.4 dBA of isolation. A Pearson’s chi-square test identified significant differences between the two styles of
earphones in the 70 dBA and 80 dBA background noise
conditions ( p , .05).
Figure 2. Diffuse-field equivalent chosen listening levels plotted
as a function of the background noise level condition.
Figure 3. Diffuse-field equivalent chosen listening levels as a function of the estimated ambient noise level in the ear canal for all trials
in background noise, excluding the Quiet condition (r2 5 0.31).
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Journal of the American Academy of Audiology/Volume 22, Number 10, 2011
To assess the reliability of the laboratory measurement technique, one noise condition was repeated for
each earphone used, resulting in three repeated measures for each subject. A total of 86 measures were
repeated. These repetitions showed excellent reliability,
indicated by a very high correlation between the two
measurements (Spearman’s rho 5 0.932). Additionally,
a paired-samples t-test comparing the first measurement
to the second measurement found no significant differences between the two measures (t 5 –1.0, p 5 .316).
Self-Reported CLLs
On the LHQ, participants were asked to report the
number of hours per day that they usually listened to
their MP3 player. Participants’ mean and median listening time was 2 hr per day, with a range of 0.5–5.75 hr per
day. Eighty percent of listeners listened 2.5 hr or less per
day. Participants also reported, on a scale from 1 to 10,
the level at which they “usually” listened and which style
of earphones they used (earbuds, supra-aural, or isolator). Participants reported a mean “usual” listening level
(on a scale of 1–10) of 5.6 (SD 5 1.72). Listeners also
reported the levels that were “comfortable” (mean 5
5.5, SD 5 1.72) and that were “too loud” (mean 5 7.7,
SD 5 1.47). All participants rated their usual volume
control setting at a level below their “slightly too loud”
rating and within one rating step of their individual
“comfortable” volume control setting.
Using the output level measurements above, each participant’s self-reported volume control setting was converted
to an estimated diffuse-field equivalent dBA, considering
the participant’s preferred earphone style indicated in
the LHQ. This calculation estimated a mean volume
control–equivalent CLL of 74.1 dBA (SD510.8, range:
52.3–91.8 dBA). The estimated equivalent CLL based on
self-reported volume control setting was greater than
85 dBA in 20 percent of participants. Using participants’
self-reported listening time and self-reported volume control setting, individual noise doses were calculated for each
Table 5. Mean Chosen Listening Levels (CLLs) Averaged
Across Earphones, Correlations Between Measured CLL
and Self-Reported CLLs, and the Average Signal-toNoise Ratio (SNR) at the Eardrum for Each Condition,
Accounting for Individual Earphone Isolation
Noise Condition
Mean (SD)
CLL (dBA)
Quiet
50 dB
60 dB
70 dB
70 dB
75 dB
80 dB
68.3
70.6
74.6
79.3
79.1
81.3
84.3
pink noise
pink noise
pink noise
bus noise
airplane noise
pink noise
*p , .05; **p , .01.
(10.9)
(9.2)
(7.3)
(5.2)
(5.3)
(4.1)
(3.0)
Correlation
SNR (dB)
0.619**
0.499**
0.440*
0.368*
0.439*
0.393*
0.298
54.7
21.1
14.7
9.2
12.2
9.8
4.13
Figure 4. Percentage of participants whose chosen listening level
exceeded 85 dBA in each background noise condition (“PN” signifies
pink noise). Significant differences between the two groups are noted
by * (p , .05).
participant, as reported in Table 6. These calculations
reflect the noise doses for OSHA (1981), NIOSH (1998),
and the EPCEU (2003) damage-risk criteria.
HBM Constructs
Mean scores on each of the HBM scale items are
reported in Appendix 1. In order to determine how well
the HBM constructs predict listening levels, several linear regression models were created. The averages for
each HBM question on the LHQ are presented in
Appendix 1. Table 7 presents the regression coefficients
for each of these models, as well as Pearson’s correlation
of regression (r2). Seven models used the HBM constructs measured on the LHQ to predict each of the
CLL measurements in various conditions, and two models are presented using the HBM constructs to predict
self-reported volume control–equivalent CLLs. When
all variables are entered, no model predicts CLLs well
for any of the noise conditions measured in the laboratory (r2 # 0.308). However, the full model predicting listeners’ self-reported volume control–equivalent CLLs
explains 67.9% of the variance (r2 5 0.679). Though
these full models reflect the behavior of only 29 participants, it is notable that only the model predicting the
self-reported listening levels included any significant
factors. Within that model, all factors were significant
with the exception of the self-efficacy factor. To assess
the impact of including the self-efficacy construct, a model
was constructed with only susceptibility, severity, benefits, and barriers. Removal of the self-efficacy construct
from this model leads to only a small change in the variance accounted for (r2 5 0.669).
To assess the reliability of the scale variables created to
represent each of the HBM constructs, Cronbach’s alpha
was calculated for each scale. Cronbach’s alpha is a
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Table 6. Noise Dose Statistics for Self-Reported Chosen
Listening Levels and Duration of Use According to
Occupational Safety and Health Administration (OSHA),
National Institute for Occupational Safety and Health
(NIOSH), and European Parliament and Council of the
European Union (EPCEU) Damage-Risk Criteria
Variable
Average noise dose
Noise dose range
Subjects exceeding
50% noise dose
Subjects exceeding
100% noise dose
OSHA
NIOSH
EPCEU
8.0%
0–74.1%
1 (3.4%)
20.9%
0–241.0%
4 (13.8%)
66.4%
0–765%
7 (24.1%)
0 (0%)
2 (6.9%)
4 (13.8%)
coefficient of internal consistency for a scale that measures how well a set of individual variables measures a
single construct. For each scale variable, as reported in
Appendix 1, Cronbach’s alpha was 0.8 or higher. This
indicates that the set of questions asked for each HBM
construct measures the computed scale variable well.
Relationship Between CLLs and
Self-Reported Levels
Reported in Table 5 are the Pearson correlations
between measured CLLs and self-reported volume control–equivalent CLLs. The strongest correlation
between a measured CLL and self-reported CLLs was
found in the Quiet condition (r 5 0.619). However, significant correlations ( p , .05) were noted in all conditions except for the 80 dBA pink noise condition.
DISCUSSION
A
s expected, CLLs increased with the addition of
background noise to the environment. Consistent
with the findings of Fligor and Ives (2006), the presence
of CLL differences between the isolator earphones and
the other two styles suggests that earphone attenuation
impacts CLL substantially. This is demonstrated in Fig-
ure 3, where the CLL as a function of estimated ambient
ear canal noise level is shown. The effect of earphone
isolation on CLL lends credence to the recommendation
that using earphones designed to passively reduce
background noise will help to reduce the risk of MIHL
from PLD use (Vogel et al, 2009).
More important for assessing the risk to hearing,
though, are the actual levels chosen by participants.
While the average CLLs were below 85 dBA for all conditions, a subgroup of participants chose levels that
might increase their risk for MIHL, depending on the
durations of their listening. The CLLs measured in this
study were, in general, consistent with CLLs measured
in previous studies. Williams (2005) found an average
listening level in a busy public place of 73.2 dBA for
a group of adults, which is very similar to the average
self-reported CLL of 74.1 dBA for adolescents in this
study. Airo, Pekkarinen, and Olkinuora (1996) reported
average CLLs for personal cassette players of 69 dBA
using supra-aural earphones in quiet. This level is
nearly identical to the 68.7 dBA documented in this
study using supra-aural earphones. The graduate students in Fligor and Ives (2006) had average CLLs using
stock earphones ranging from 62.2 to 80.8 dBA in the
quiet through 80 dBA conditions. The present study
of adolescents found a range of average CLLs from
70.1 to 86.0 dBA for the same conditions. A comparison
of the reported CLLs indicates that the teenagers of this
study chose levels, on average, 7.5 dB higher than those
of the graduate students of Fligor and Ives (2006). More
research is needed to directly compare the CLLs of different age groups and to understand the underlying factors that contribute to the age differences.
The ultimate goal of identifying the output levels of
PLDs is to determine the risk for hearing loss from using
the devices. To that end, the output levels must be compared with DRC. OSHA (1981) accepts that roughly 25%
of persons exposed at the permissible exposure level will
have a material hearing impairment after a 40 yr working lifetime. NIOSH (1998) accepts that 8% of persons
exposed at the recommended exposure level will have
Table 7. Coefficients from the Linear Regression Models Predicting Both Measured Chosen Listening Levels (CLLs) in
Each of the Seven Noise Conditions and the Self-Reported Volume Control Equivalent CLL
Regression Coefficient
Scale
Quiet
PN 50
PN 60
PN 70
Bus 70
Airplane 75
PN 80
Self-Report
Self-Report
Susceptibility
Severity
Benefits
Barriers
Self-efficacy
1.51
–0.721
–1.99
2.60
0.401
1.15
–0.693
–2.23
2.14
0.566
1.02
–0.594
–1.69
1.65
0.932
0.667
–0.222
–0.094
1.05
0.400
0.636
0.031
–0.848
1.16
0.516
0.349
–0.049
–0.517
1.01
0.166
0.245
0.553
–0.870
0.553
0.251
0.315*
0.699**
–0.550*
0.556**
–0.186
0.327*
0.718**
–0.607**
0.635**
0.305
0.308
0.274
0.210
0.182
0.203
0.172
0.679
0.669
r2
Note: Each model includes each of the Health Belief Model scales measured on the Listening Habits Questionnaire, except for the last column,
which reflects all but the Self-efficacy scale. PN 5 pink noise.
** p , .01; *p , .05.
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Journal of the American Academy of Audiology/Volume 22, Number 10, 2011
a material hearing impairment after a 40 yr working lifetime. The EPCEU (2003) directive is most conservative,
serving to prevent any MIHL. As such, the EPCEU
(2003) directive may be restrictive and difficult for
PLD users to follow, particularly when using a PLD in
background noise. Currently, no DRC exist specifically
for music, nor is there consensus in the hearing conservation community about which DRC is most appropriate
for evaluating music exposure.
Table 6 presents the percentage of participants who
exceed both 50 and 100% of a daily noise dose if they
use their PLDs as reported on the LHQ. When considering an acceptable noise dose due to PLD use alone, other
daily noise exposure, such as occupational or other recreational exposure, should be taken into account. As adolescents could obtain a portion of their noise dose from
other activities in a given day, recommending limits that
reach 100% of a daily noise dose from a PLD alone is not
appropriate. A lower cutoff for exposure due to PLD use,
such as a 50% noise dose, may be a better recommendation. Certainly, more research is needed to determine
what cutoff level is the most appropriate for a recommended limit for PLD use in adolescents. Using the NIOSH
criteria with a 50% noise dose limit, 14% of teenage listeners are potentially at risk for MIHL. While 14% of listeners is a relatively small percentage of the total
population, as of October 2011 over 320 million iPods
have been sold (Apple, Inc., 2011). Because the ownership
rates of PLDs are high, a small percentage would translate to a very large number of individuals with hearing
loss attributable, in part, to PLD use. While Shargorodsky et al (2010) did not find noise exposure to be a significant predictor of poorer high-frequency hearing
thresholds in adolescents, they indicated that noise exposure could not be ruled out as a causal factor in the 30%
increase in the number of teenagers with hearing thresholds outside the normal range from 1988 to 2006.
Differences in Laboratory and
Self-Report Measures
This experiment found significant correlations
between the participants’ self-reported volume control
settings and laboratory-measured CLLs in several
background noise conditions, as reported in Table 5.
Interestingly, the strongest correlation of self-reported
CLLs to a laboratory background noise condition was in
the Quiet condition, though significant correlations
were noted in most noise conditions. Several underlying
factors may explain why these correlations were seen in
multiple laboratory conditions. First, it is possible that
the two measurements may be evaluating separate constructs. A laboratory measure captures behavior over
the course of the experiment, but a self-report measure
must examine past behavior. In this experiment, listeners provided a one-dimensional assessment of their
typical behavior, reporting only their typical volume
control setting. However, persons listening to PLDs
may adjust the volume control to compensate for their
environment. This reported typical setting may not
truly reflect the listener’s behavior but, instead, could
report an average volume control setting. A multidimensional self-report, where listeners report their
usage time for various volume control levels, may provide a stronger association to laboratory measures. Second, it is possible that listeners are not able to provide
an accurate report of their chosen volume control settings because they do not pay attention to a volume dial.
Additional research is needed to assess the reliability of
PLD users’ self-reports of listening behavior.
Relationship of Attitudes and Beliefs to Behavior
This study also shows that the constructs of the HBM
may be useful in determining what factors influence an
individual’s CLL. Interestingly, the HBM constructs
were poor predictors for all of the CLLs measured in
the laboratory, regardless of noise condition. However,
the HBM constructs were a strong predictor of selfreported CLLs. To put these conditions into context,
the self-reported CLLs could also be considered to represent a perceived CLL. Considering that all of the constructs of the HBM are dependent on the listener’s
self-perceptions (i.e., Perceived Susceptibility to MIHL),
it is possible that perceived beliefs might be more strongly
related to perceived behaviors, rather than actually measured behaviors. Future research is needed to validate the
efficacy of self-reported measures of listening behavior.
Within the HBM that predicted self-reported CLLs,
all individual constructs were significant predictors,
with the exception of self-efficacy. Though the selfefficacy factor had a mean score of 5.44, which was similar to the other factors, 31% of participants marked the
highest value (7) on the scale. A score of 7 on this scale
indicates that the participant feels strongly that he or
she is able to monitor listening levels in quiet and noisy
environments and to appropriately limit his or her volume. The skew of responses and lack of significance of
self-efficacy indicate that it may not be a construct necessary for understanding the relationship between
beliefs and CLLs. The other constructs, though, were
significant predictors of self-reported behavior. Examination of the regression model shows several trends.
First, as might be expected, when perceived barriers
to preventing MIHL increase, CLL increases. Similarly,
when the perceived benefits of preventing MIHL
increase, CLL decreases. Curiously, however, when
perceived susceptibility to MIHL and perceived severity
of MIHL increase, CLL increases. These regression
results suggest that effective educational intervention
might focus on promoting the benefits of and reducing
the barriers to prevention, such as teaching what
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Teenage MP3 Player Use/Portnuff et al
volume control setting is a safe level for the desired
listening duration and the benefits of using soundisolating earphones when in noisy listening environments.
While this study validates the use of the HBM constructs as factors predictive of self-reported CLLs, the
small sample size of this study attenuates the extent to
which these results can be generalized. Nevertheless,
further investigation of the other HBM constructs is
warranted to evaluate the relationships between attitudes and behaviors in more detail. A larger and more
representative sample population may allow for
extrapolation to a general population of adolescents.
Moreover, a larger data set would allow for more sensitive measures, including path analysis or structural
equation modeling for evaluating the strength of each
construct in the HBM. Future studies of CLL and
health beliefs should evaluate a large population to
take advantage of these statistical techniques. Additionally, future studies in this area should consider
expanding the HBM to include a standardized scale
of adolescents’ risk-taking behavior or sensation-seeking behavior, as suggested by Bohlin and Erlandsson
(2007).
CONCLUSIONS
C
onsistent with previous research, this study shows
that the current generation of PLDs is capable of
producing output levels that could cause MIHL. Further, this study validates a method for evaluating
the CLLs of adolescents. In this sample, a small but
substantial number of adolescents listened at levels
that increased their risk for MIHL. Though the percentage of adolescents who are at higher risk of MIHL
is not higher than those reported for users of older
technologies, adolescents in this study listened at
higher levels than graduate students using similar
technology (Fligor and Ives, 2006). Moreover, background noise levels and earphone sound isolation were
important predictors of CLL. Interestingly, this study
also found relationships between self-reported CLLs
and measured CLLs in several laboratory conditions.
As no clear, singular relationship was found, the results
open the door to additional examinations of the validity
of self-reported CLL to further understand why these
relationships were identified. Finally, this study validates the use of the HBM for evaluating what factors
influence CLL. With the exception of the self-efficacy
construct, the HBM is an effective method for modeling
self-reported listening behaviors. Further investigation
applying this model to a larger population of PLD users
is needed to examine the complex relationship between
attitudes and beliefs about PLD use and hearing loss and
real-world behavior.
NOTE
1. For the purposes of this study, an earbud is a small earphone
that fills the concha of the ear with the housing sitting medial to
the tragus. An isolator is a flanged earphone that has been
designed to attenuate background noise when properly
inserted into the ear canal. A supra-aural earphone rests on
the pinna of the ear.
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Teenage MP3 Player Use/Portnuff et al
Appendix 1. Means and standard deviations of responses to Listening Habits Questionnaire questions organized by
Health Belief Model construct. Indexes include Cronbach’s alpha, a measure of internal consistency reliability for a
scale variable. Responses ranged from 1 to 7, with greater numbers indicating agreement with the statement.
Question
Mean
SD
Alpha
Susceptibility to Music-Induced Hearing Loss (MIHL)
1. How susceptible to hearing loss do you feel?
2. What is the chance that you will experience hearing loss from listening to loud music?
3. How likely do you think it is that you will experience hearing loss resulting from listening to
loud music on an MP3 player?
4. Would you say that you are the type of person who is likely to experience hearing loss?
Susceptibility to MIHL Index
3.14
3.62
3.83
1.55
1.86
1.91
3.00
3.48
1.79
1.63
0.87
Severity of MIHL
1. How disruptive would hearing loss be to your quality of life?
2. How disruptive would the cost of treating hearing loss be?
3. How disruptive would it be to have to wear a hearing aid?
4. How disruptive would hearing loss be to your ability to communicate with your friends and loved ones?
5. How disruptive would it be to sustain permanent hearing loss as a result of listening to loud music?
6. Overall, how disruptive would hearing loss be in your life?
Severity of MIHL Index
5.86
5.21
5.34
6.24
6.31
6.34
5.88
1.19
1.57
1.70
1.38
0.97
0.97
0.97
0.83
5.17
6.14
4.86
5.72
5.21
4.34
5.00
1.58
1.19
1.75
1.20
1.54
1.84
1.63
5.21
1.05
0.81
Barriers to Preventing MIHL
1. If I turned my music down to a safe level in a loud environment, I wouldn’t be able to hear it.
2. If I turned my music down to a safe level in a loud environment, I wouldn’t enjoy my music as much.
3. I don’t know what level my music should be turned down to in a loud environment to protect my hearing.
4. I don’t know what level my music should be turned down to in a quiet environment to protect my hearing.
Barriers to Preventing MIHL Index
5.24
5.31
4.38
3.41
4.59
1.77
1.91
1.61
1.96
1.47
0.83
Self-Efficacy for Taking Preventative Action
1. I feel confident in my ability to monitor the volume at which I listen to my music.
2. I feel confident in my ability to make sure I listen to music at a safe level when I’m in a quiet environment.
3. I feel confident in my ability to make sure I listen to music at a safe level when I’m in a loud environment.
4. I feel confident in my ability to set the volume limiter of my MP3 player to a safe level.
5. If I knew I were listening at an unsafe level, I would be willing to turn down the volume.
Self-Efficacy for Taking Preventative Action Index
5.55
5.93
5.14
5.17
5.41
5.44
1.40
1.16
1.55
1.61
1.66
1.24
0.89
Benefits of Preventing MIHL
1. Making sure I listen to music at safe levels would prevent me from experiencing hearing loss.
2. Turning my music down to a safe level when I’m in a quiet environment would be a good thing for me to do.
3. Turning my music down to a safe level when I’m in a loud environment would be a good thing for me to do.
4. Making sure my music is at a safe level when I’m in a quiet environment would prevent hearing loss.
5. Making sure my music is at a safe level when I’m in a loud environment would prevent hearing loss.
6. Setting my volume limiter at a safe level would be a good thing for me to do.
7. Using special earphones that block out background noise when I listen to music would be a good
thing for me to do.
Benefits of Preventing MIHL Index
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