Effectiveness of Noise-Attenuating Headphones on Physiological Responses for Children With Autism Spectrum Disorders

Abstract

Objective: The purpose of this study was to evaluate the proof of concept of an intervention to decrease sympathetic activation as measured by skin conductivity (electrodermal activity, EDA) in children with an autism spectrum disorder (ASD) and auditory hypersensitivity (hyperacusis). In addition, researchers examined if the intervention provided protection against the negative effects of decibel level of environmental noises on electrodermal measures between interventions. The feasibility of implementation and outcome measures within natural environments were evaluated. Method: A single-subject multi-treatment design was used with six children, aged 8–16 years, with a form of Autism (i.e., Autism, PDD-NOS). Participants used in-ear (IE) and over-ear (OE) headphones for two randomly sequenced treatment phases. Each child completed four phases: (1) a week of baseline data collection; (2) a week of an intervention; (3) a week of no intervention; and (4) a week of the other intervention. Empatica E4 wristbands collected EDA data. Data was collected on 16–20 occasions per participant, with five measurements per phase. Results: Separated tests for paired study phases suggested that regardless of intervention type, noise attenuating headphones led to a significance difference in both skin conductance levels (SCL) and frequency of non-specific conductance responses (NS-SCRs) between the baseline measurement and subsequent phases. Overall, SCL and NS-SCR frequency significantly decreased between baseline and the first intervention phase. A protective effect of the intervention was tested by collapsing intervention results into three phases. Slope correlation suggested constant SCL and NS-SCR frequency after initial use of the headphones regardless of the increase in environmental noises. A subsequent analysis of the quality of EDA data identified that later phases of data collection were associated with better data quality. Conclusion: Many children with ASD have hypersensitivities to sound resulting in high levels of sympathetic nervous system reactivity, which is associated with problematic behaviors and distress. The findings of this study suggest that the use of noise attenuating headphones for individuals with ASD and hyperacusis may reduce sympathetic activation. Additionally, results suggest that the use of wearable sensors to collect physiological data in natural environments is feasible with established protocols and training procedures.

Full text

ORIGINAL RESEARCH
published: 12 November 2019
doi: 10.3389/fnint.2019.00065
Edited by:
Jonathan T. Delafield-Butt,
University of Strathclyde,
United Kingdom
Reviewed by:
Elias Manjarrez,
Meritorious Autonomous University of
Puebla, Mexico
David R. Simmons,
University of Glasgow,
United Kingdom
*Correspondence:
Beth Pfeiffer
bpfeiffe@temple.edu
Received: 07 November 2018
Accepted: 21 October 2019
Published: 12 November 2019
Citation:
Pfeiffer B, Stein Duker L, Murphy A
and Shui C (2019) Effectiveness of
Noise-Attenuating Headphones on
Physiological Responses for Children
With Autism Spectrum Disorders.
Front. Integr. Neurosci. 13:65.
doi: 10.3389/fnint.2019.00065
Effectiveness of Noise-Attenuating
Headphones on Physiological
Responses for Children With Autism
Spectrum Disorders
Beth Pfeiffer1*, Leah Stein Duker2,AnnMarie Murphy1and Chengshi Shui3
1Department of Health and Rehabilitation Sciences, Temple University, Philadelphia, PA, United States, 2USC Chan Division
of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, United States,
3School of Nursing, University of California, Los Angeles, Los Angeles, CA, United States
Objective: The purpose of this study was to evaluate the proof of concept of an
intervention to decrease sympathetic activation as measured by skin conductivity
(electrodermal activity, EDA) in children with an autism spectrum disorder (ASD)
and auditory hypersensitivity (hyperacusis). In addition, researchers examined if the
intervention provided protection against the negative effects of decibel level of
environmental noises on electrodermal measures between interventions. The feasibility
of implementation and outcome measures within natural environments were evaluated.
Method: A single-subject multi-treatment design was used with six children, aged
8–16 years, with a form of Autism (i.e., Autism, PDD-NOS). Participants used in-ear
(IE) and over-ear (OE) headphones for two randomly sequenced treatment phases. Each
child completed four phases: (1) a week of baseline data collection; (2) a week of an
intervention; (3) a week of no intervention; and (4) a week of the other intervention.
Empatica E4 wristbands collected EDA data. Data was collected on 16–20 occasions
per participant, with five measurements per phase.
Results: Separated tests for paired study phases suggested that regardless of
intervention type, noise attenuating headphones led to a significance difference in both
skin conductance levels (SCL) and frequency of non-specific conductance responses
(NS-SCRs) between the baseline measurement and subsequent phases. Overall,
SCL and NS-SCR frequency significantly decreased between baseline and the first
intervention phase. A protective effect of the intervention was tested by collapsing
intervention results into three phases. Slope correlation suggested constant SCL and
NS-SCR frequency after initial use of the headphones regardless of the increase in
environmental noises. A subsequent analysis of the quality of EDA data identified that
later phases of data collection were associated with better data quality.
Conclusion: Many children with ASD have hypersensitivities to sound resulting
in high levels of sympathetic nervous system reactivity, which is associated with
problematic behaviors and distress. The findings of this study suggest that the use of
Frontiers in Integrative Neuroscience | www.frontiersin.org 1November 2019 | Volume 13 | Article 65

Pfeiffer et al. Noise-Attenuating Headphones
noise attenuating headphones for individuals with ASD and hyperacusis may reduce
sympathetic activation. Additionally, results suggest that the use of wearable sensors to
collect physiological data in natural environments is feasible with established protocols
and training procedures.
Keywords: hyperacusis, autism spectrum disorder (ASD), noise-attenuating headphones, noise canceling
headphone, electrodermal responses (EDR), autonomic nervous system, stress, anxiety
INTRODUCTION
Unusual responses to sensory stimuli are experienced by up
to 90% of individuals with autism spectrum disorder (ASD;
Ben-Sasson et al., 2009). Although it is unclear as to whether
sensory processing difficulties are a trait of ASD or a trait of
comorbid disorders (Landon et al., 2016), behavioral responses
to sensory stimuli have become so prevalent, that the most
recent criteria in the Diagnostic and Statistical Manual of Mental
Disorders 5th edition (DSM-V) for ASD added a diagnostic
component of hyper- and hypo- reactivity to sensory stimuli
(American Psychiatric Association, 2013). When studying the
neurobiological differences in those with sensory difficulties,
research indicates those with sensory over responsivity (SOR),
or hypersensitivities, present with atypical sympathetic and
parasympathetic functions of the nervous system (Miller
et al., 2009). Of the various sensory responses, one of the
most commonly reported challenges for those with ASD is
hypersensitivity to sound (Baranek et al., 2006; Kern et al., 2006;
Tomchek and Dunn, 2007; Stiegler and Davis, 2010; Bolton et al.,
2012). Despite varying findings when analyzing cortical auditory
sensory processing, neurophysiological studies have consistently
identified atypical neural activity early in the processing stream
in individuals with ASD (Marco et al., 2011).
Common in children with ASD, hyperacusis is a term
used to describe the negative and/or exaggerated response to
environmental stimuli occurring within the auditory pathways
(Asha’ari et al., 2010; American Speech-Language-Hearing
Association, 2016)1. Individuals with hyperacusis have an
increased sensitivity to auditory input (Palumbo et al., 2018),
and report experiencing auditory information at unbearably loud
levels (Kuiper et al., 2019). Although hyperacusis is one of
the most commonly identified auditory responses in children
with ASD (Rogers et al., 2003), the cause of the disorder is
not fully understood. Research suggests that the relationship
between the central auditory system and the limbic system
contribute to the development of the fear and anxiety frequently
experienced with hyperacusis (Brout et al., 2018). In comparison
to neurotypical peers, research on multi-sensory integration
suggests that children with SOR may not process incoming
information in lower level cortical regions. In conjunction with
difficulties with sensory gating, challenges with modulation
may prevent the central nervous system from appropriately
identifying the intensity, frequency, duration, and complexity
of environmental stimuli lending to issues filtering meaningful
from non-meaningful sounds in the environment (Miller et al.,
1https://www.asha.org/uploadedFiles/AIS-Hyperacusis.pdf
2009). This inability to filter may lead to an overwhelming
amount of incoming stimuli, resulting in hyper-reactions due
to sensory overload (Kuiper et al., 2019). The continual stress
from perceived noxious stimuli and sensory overload can result
in physiological changes (Rance et al., 2017). More specifically,
decreased basal respiratory sinus arrhythmia and basal heart rate
hyperarousal have been associated with social, language, and
cognitive difficulties (Kushki et al., 2014).
Additionally, recent research has examined the role of
medial olivocochlear efferent reflexes (MOC) in hyperacusis.
Findings suggest that when comparing those with ASD with
severe hyperacusis, those with ASD without hyperacusis, and
neurotypicals, the MOC reflexes were twice as strong in
individuals who have ASD with severe hyperacusis (Wilson et al.,
2017). Despite this new understanding of the MOC reflexes and
hyperacusis, research is inconsistent in identifying physiological
differences in auditory pathways in individuals with hyperacusis
(Tharpe et al., 2006; Jones et al., 2009).
Some emerging evidence suggests that SOR, such as
hyperacusis, is associated with decreased inhibitory processes.
For example, a fMRI study found slower habituation in youth
with ASD and SOR in the amygdala and somatosensory cortex
from both tactile and auditory input, as compared to youth
with ASD without SOR (Green et al., 2015). Chang et al.
(2012) found a significant association between electrodermal
activity (EDA) and parent reported problem behaviors on the
Sensory Processing Measure (SPM) Hearing and Total scale
score categories. As discussed previously it is thought that
hyperacusis may be linked to a difficulty in sensory modulation
for children with ASD. Supporting this, Chang et al. (2012)
found that participants with strong sympathetic reactivity were
reported to have behaviors indicative of both over- and under-
responsiveness. Through use of EDA, Schoen et al. (2008) also
found two significant patterns of habituation in response to
sensory stimuli (i.e., tone, strobe light, siren, smell, feather, chair
movement). Within the population of children with ASD, their
results grouped to show: (1) high tonic electrodermal arousal,
high reactivity, and slower habituation; and (2) low tonic arousal,
lower reactivity, and faster habituation.
When researching hyperacusis in adults with ASD, however,
Kuiper et al. (2019) found no significant positive correlation
between habituation rate and self-reported auditory hyper-
sensitivity. Despite habituating at similar rates, it is noted that
those with ASD had a higher skin conductance level (SCL) at
baseline, indicating higher physiological arousal (Kuiper et al.,
2019). Another study examined time-course responses of the
auditory cortex to repeated auditory stimuli, as measured by
magnetoencephalography, between boys with ASD who had
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Pfeiffer et al. Noise-Attenuating Headphones
auditory SOR, boys with ASD without auditory SOR, and
neurotypical peers. The boys with ASD and auditory SOR
exhibited prolonged response duration when compared to the
other groups, suggesting decreased inhibition as found in
abnormal sensory gating or dysfunction of inhibitory neurons
(Matsuzaki et al., 2014). This was further supported in autism
model rats that presented with a decrease in morphological size
of the medial nucleus of the trapezoid body in the superior olivary
complex, which holds an inhibitory role in auditory processing
(Ida-Eto et al., 2017).
Regardless of the underlying cause, hyperacusis has been
associated with anxiety and stress surrounding perceived noxious
auditory stimuli, resulting in strong reactions (Jastreboff and
Jastreboff, 2000; Brout et al., 2018). Illustrating this, children
with ASD are frequently reported to cover their ears to block out
sounds, as well as exhibit anxious or distressing reactions to some
sounds (Rimland and Edelson, 1995; Jastreboff and Jastreboff,
2000). Intense and atypical responses to auditory stimuli can
result in increased stress; avoidance of certain environments and
interactions; decreased participation or engagement in key life
activities and events; and distractibility impacting performance
in home and school (Pfeiffer et al., 2019). These adverse effects on
school performance and social interactions have been reported
to influence overall quality of life (Grinker, 2007; Rowe et al.,
2011; Smith and Riccomini, 2013). In a qualitative study by
Landon et al. (2016), adult participants with ASD and noise
sensitivity (NS) described particular sounds as causing physical
discomfort and frustration. One participant with ASD and NS
described ‘‘. . .the buzzing (of the fluorescent lightbulb) was so
annoying that it got to the point where I couldn’t turn it on. So
I sat there in the dark in my room for half the year because I
couldn’t turn the light on (p. 48)’’. Palumbo et al. (2018) note that
characteristically, individuals with hyperacusis tend to become
hyper-focused on listening for trigger sounds within their natural
environments, resulting in a ‘‘perpetual state of anxiety’’ (p. 2)
while they wait for the noxious stimuli to occur. This hyper-
focused state was reported to cause emotional and physical
discomfort by those with hyperacusis (Palumbo et al., 2018).
In addition to experiencing emotional and physical discomfort,
research indicates that chronic stress associated with hyperacusis
may lead to negative mental and physical health conditions
(McEwen and Gianaros, 2011).
Researchers have also examined the impact of noise on
health in the general population. The World Health Organization
(WHO) reports that environmental noise exposure can lead to a
variety of negative health outcomes including sleep disturbance,
cognitive impairments in children, stress-related mental health
risks, as well as tinnitus (World Health Organization, 2018)2.
Research suggests that autonomic nervous system and endocrine
responses to sound correlate with particular night-time noises
such as road traffic, aircrafts and railway noises, resulting in
increased blood pressure, changes in heart rate, and leading
to the release of stress hormones (Münzel et al., 2014).
Research also suggests that individuals with non-supported
coping strategies, such as children, may experience psychological
2http://www.who.int/sustainable-development/transport/health-risks/noise/en/
stress in addition to physiological imbalance due to noise
(Basner et al., 2014).
Due to the impact on participation and overall quality of
life, a number of interventions that target the reduction of
auditory hypersensitivity have been developed and trialed. One
intervention, the listening project protocol (LPP), proposes to
increase the neural tone to the inner ear muscles. During LPP,
participants spend 45 min per day for 5 days listening to
computer altered acoustic stimuli via headphones. This protocol
was developed with insight from the Borg and Counter model,
which suggests that auditory hypersensitivity in ASD may be due
to atypical regulation of the middle ear as it tries to extract human
speech from environmental noise (Porges et al., 2014). Research
on this protocol showed a decrease in auditory hypersensitivity as
well as an increase of spontaneous sharing behavior in children
with ASD (Porges et al., 2014). However, several limitations were
noted; for example, improvements were seen in both treatment
and control groups, suggesting that the social engagement system
utilized may have been a confounder. Although some research on
this protocol has been conducted focusing on reducing auditory
hypersensitivity in those with ASD, most of the clinical trials
focus on its impact on emotional regulation and trauma3.
Another method currently utilized to reduce sound sensitivity
in those with auditory hypersensitivities is auditory integration
training (AIT; Sokhadze et al., 2016). Through filtered and
modulated frequencies, AIT aims to suppress the peaks of
frequency by random dampening of high and low frequencies
in order to normalize the sounds and retrain the brain of
someone who is hypersensitive (Sinha et al., 2006). Although
different types of AIT, including the Listening Program,
Berard Method and Tomatis Method are used, there is limited
scientific research to support their ability to decrease auditory
hypersensitivity (Dawson et al., 2007; Miller and Schoen, 2015;
Sokhadze et al., 2016).
Researchers have also tested whether cognitive behavioral
therapy (CBT) can alter learned patterns and behaviors, as
well as faulty ways of thinking, related to particular noises in
the environment. A randomized controlled trial was conducted
in which a licensed psychologist trained in CBT provided
six therapy sessions using CBT principles, psychoeducation,
exposure therapy, applied relaxation and behavioral activation
(Jüris et al., 2014). The use of CBT limits escape behavior by
role playing potential problem scenarios (i.e., loud sounds or
environments with unwanted noises) and learning to be calm
(American Psychological Association, 2018). One of the benefits
of using CBT for those with hyperacusis is the learning of
new behaviors which can be used long after the study and
intervention are completed (Jüris et al., 2014). The limitation of
using CBT for those with ASD and hyperacusis, however, is that it
requires recognition and awareness of the aversive stimuli. Event-
related potential and magnetic field research on ASD (Orekhova
and Stroganova, 2014) suggest that problems only arose when
novel stimuli were outside of the individual’s focus of attention,
suggesting CBT may be limited with those not aware of the
actual trigger.
3https://integratedlistening.com/bing-safe-sound-protocol/
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Pfeiffer et al. Noise-Attenuating Headphones
One common non-invasive intervention to improve the
auditory environments for individuals with ASD are noise-
attenuating headphones, which block sound transmission to the
ears (Pfeiffer et al., 2019). Ikuta et al. (2016) conducted a pilot
study on the effectiveness of noise-canceling (NC) headphones
in children with ASD of varying intelligence. Participants in
this study had difficulty using the NC headphones when they
had hypersensitivity to human voices. As noted previously, one
theory suggests that auditory hypersensitivity in those with
ASD may be due to the inability of the middle ear to filter
human voices from environmental noise (Porges et al., 2014).
Research did find, however, that behavioral responses improved
for children who perceived environmental noises (i.e., noisy
classroom sounds) as noxious (Ikuta et al., 2016). Additionally, a
single case design study identified an increase in attention to task
for a child with ASD and auditory hypersensitivity when wearing
the headphones (Rowe et al., 2011). Although this is often a
low-cost and easily implemented intervention, there is limited
research documenting its effectiveness. Additionally, to our
knowledge, there is no current research examining the impact
of environmental adaptation, such as use of noise-attenuating
headphones, on the core issue of physiological anxiety and stress
exhibited by individuals with ASD and hyperacusis.
Therefore, the purpose of this study was to examine the
proof of concept for two types of noise attenuating headphones
in reducing physiological stress and anxiety in children with
ASD when in natural environments with noise perceived as
aversive. Further investigation examined how the intervention
provided a buffer for children with ASD against the negative
effects of environmental noises on their physiological stress and
anxiety. Historically, research assessing physiological responses
to noise has been conducted in laboratory environments that
does not reflect the natural environment. In children with
ASD, participation in such studies do not accurately reflect
the milieu of auditory stimuli encountered in the real world.
For example, recent research has used fMRI to provide insight
on neuronal correlations of auditory processing, although there
is a loud noise associated with the imaging (Talavage et al.,
2014) that is not typically encountered in natural environments.
These imaging techniques, along with other psychophysiological
measures such as EDA, require the participant to remain
still for the duration of the recording (Boucsein et al., 2012;
Boucsein, 2012; Wilson et al., 2017). Additionally, participating
in these laboratory-based experiments may lead to increased
stress as the child must deviate from his or her typical routine
while in an unfamiliar environment with unfamiliar people.
Because many children with ASD cannot express their distress
in context-specific situations and their actions/reactions are
often misunderstood, outcome measures of environmentally-
based interventions in the natural context of the child are
important to truly understand their experiences. Therefore, in
this study, wearable sensors were used as the primary source
of data collection with the intervention implemented in the
natural environment. Due to complexities of collecting data
within natural environments, we also evaluated the feasibility
of the study measures and the quality of the wearable
sensor data.
MATERIALS AND METHODS
Design
The purpose of this study was to evaluate the proof of concept
for an intervention to decrease physiological stress and anxiety
among children with ASD within their natural environment.
Single-subject multi-treatment design was used to compare two
different noise attenuating headphone devices. These devices
were over-ear (OE) BOSE Quiet Comfort 15 Acoustic Noise
Attenuating Headphones and in-ear (IE) BOSE QuietComfort
20i Acoustic Noise Attenuating Headphones. The headphones
were used to assess if noise attenuation would impact
physiological responses during identified target activities with
noxious auditory stimuli in natural environments. An ABAC
design was used at random, assigning two different sequences of
the intervention to the participants (Group A: ABAC or Group
B: ACAB). Regardless of sequence, participants completed all
four phases including: (1) a week of baseline data collection;
(2) a week of an intervention; (3) a week of no intervention;
and (4) a week of the other intervention. Participants did
not wear the noise attenuating headphones during baseline
or the week of non-intervention. During the 2 weeks of
intervention, either the OE or IE attenuating headphones
were used. Data collection occurred on 20 occasions per
participant, with five measurements per phase. Participants were
randomly assigned to one of two groups, with one group having
phases sequenced ABAC and the other group having phases
sequenced ACAB.
Participants
In total, six children between the ages of 8 and 16 diagnosed
with an ASD completed the study. Participant demographics
are outlined in Table 1. Participants were only included in
the study if they were diagnosed with a form of Autism using
DSM-IV criteria. Diagnosis was confirmed through parent report
and the completion of the Gilliam Autism Rating Scale 3rd
edition (GARS-3; Gilliam, 2013)4. All participants had a score
of 70 or higher, which is indicative of very high probability of
ASD. Additionally, participants had to score in the probable or
definite difference range on the Auditory Filtering and Auditory
Sensitivity Scales of the Short Sensory Profile (SSP; Dunn, 1999)
for inclusion as an indicator of hyperacusis.
Initially, 12 participants responded to recruitment efforts.
However, six dropped out of the study before completing
data collection. Reasons cited for participant drop-out
included feeling overwhelmed or having problems with
using technology; having no access to child during activities
that were noisy and stimulating (i.e., school); confidentiality
issues with conducting the experiment within the school
setting; disruption of child’s routine; lack of time to do the data
collection; constant monitoring of child to prevent destruction
of headphones; and resistance of the child to wear the wristband
collecting data.
4https://www.pearsonassessments.com/store/usassessments/en/Store/Professional-
Assessments/Behavior/Gilliam-Autism-Rating-Scale-%7C-Third-Edition/p/
100000802.html?tab=product-details
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Pfeiffer et al. Noise-Attenuating Headphones
TABLE 1 | Participant characteristics.
Participant Number Age Diagnosis Order of Intervention
(B = OE; C = IE)
Score Range:
Auditory Filtering
(SSP)
Score Range:
Visual/Auditory
Sensitivity (SSP)
Activities and
Environments
Targeted for the NAH
Ethnicity of Child
3 8 years PDD-NOS (DSM-IV); ADHD ABAC 13/30
Definite Difference
(range 6–19)
18/25
Probable Difference
(range 15–18)
In car; Learning time
with music
Caucasian
4 8 years Autism (DSM-IV) ACAB 14/30
Definite Difference
(range 6–19)
18/25
Probable Difference
(range 15–18)
Therapy session with
music in background
Caucasian
8 8 years Autism (DSM-IV) ABAC 16/30
Definite Difference
(range 6–19)
18/25
Probable Difference
(range 15–18)
After school time; In the
car
Caucasian
10 16 years PDD-NOS (DSM-IV) ABAC 15/30
Definite Difference
(range 6–19)
14/25
Definite Difference
(range 5–15)
Video games; Driving in
car with music on;
Grocery store;
Homework
Caucasian
11 9 years Autism (DSM-IV), ADD, SPD ACAB 13/30
Definite Difference
(range 6–19)
8/25
Definite Difference
(range 5–15)
Playground;
Occupational Therapy
Latin American or
Hispanic
12 11 years Asperger’s Disorder (DSM-IV), SPD, Anxiety ABAC 16/30
Definite Difference
(range 6–19)
15/25
Definite Difference
(range 5–15)
Practicing drums;
Playground
Latin American or
Hispanic
Procedure
Recruitment was conducted through social media, schools with
individuals with ASD, private therapy practices, and community
organizations in the Philadelphia area. Information about the
study was posted on social media sites, such as Facebook.
Flyers were provided to school administration, private therapy
practices, and organizations that support individuals with ASD.
If participants were interested, they contacted the research
coordinator directly.
When an interested participant contacted the researchers,
written informed consent was obtained from the parents of the
participants who also completed a Demographic Questionnaire,
GARS-3, and SSP to determine preliminary inclusion. If scores
on the GARS-3 were 70 or higher, and scores on the Auditory
Scales of the SSP fell in the range of probable to definite
difference, a second meeting was scheduled to obtain child assent.
All children provided assent through either verbal (i.e., verbal
response of yes or no) or non-verbal indicators (i.e., nodding
of head). Additionally, after having the child assent language
read to them, the child signed an assent form if they were
able. Once child assent was obtained, an occupational therapy
evaluation was completed to identify activities/environments
that were avoided or caused stress due to auditory stimuli,
and to provide training on data collection methods. Target
environments varied from child to child, including activities
on the playground, playing with drums or video games, going
grocery shopping, as well as other activities with and without
music present (i.e., driving in a car, doing homework). During
the study no was music played into the headphones so that the
function was limited to providing noise attenuation rather than
noise masking. Parents were provided with step-by-step training
for use of equipment and other data collection procedures. See
Table 2 for the Quick Reference Data Collection document
provided to parents (the full Pictorial Direction Manual can be
requested from authors).
TABLE 2 | iPad with data plan instructions: procedures for BOSE data
collection—quick reference.
Getting Started
Step 1: Put wristband on participant @ 20 min prior to start of activity, cover
with sweatband.
Step 2: Turn on iPod.
Step 3: Press wristband power button for 2 s, it will blink green.
Step 4: Open Empatica RT app.
Step 5: Touch “start a new session,” then select Empatica E4 from
device list.
Step 6: Make a Visual Scan of environment.
Step 7: Make a Decibel Reading of environment.
During Session
Step 8: Make a Visual Scan of Environment at middle and end of activity.
Step 9: Make a Decibel Reading at middle and end of activity.
Ending Session
Step 10: Press the red X on Empatica RT App and confirm “ok?” to end
data collection.
Step 11: Remove wristband from participant and store in carrying case.
AFTER
Step 14: Open Notes App on iPod. Complete Momentary Assessment
Questions.
Step 15: Take screen shots of all decibel readings.
Step 16: Close apps. Turn of iPod. Charge unit for next use.
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Pfeiffer et al. Noise-Attenuating Headphones
Children had the opportunity to wear all study-related devices
for a week before data collection began in order to help the
child feel comfortable with the headphones as well as the
wearable sensors within an environment/activity that was not
targeted for the study. Participants who demonstrated refusal
or discomfort after the one-week trial were excluded from the
study. Throughout the study duration, a research team member
checked in with participants regularly and provided technical
support, as needed, throughout the data collection period. A gift
card was given to the parent of a child who participated in the
culmination of the study.
Intervention
Two different types of noise-attenuating headphones, designed
to block out environmental noises, were used during the
intervention phases. The technology used in these headphones
compares and reacts to environmental sounds. When reacting
to the environmental sound, a signal is provided to counteract
the noise in the environment, thus canceling out the noise
in the environment (BOSE, 2018)5. No music was played
into the headphones during the study, so the headphones
solely provided a noise attenuating function rather than
noise masking. Although BOSE provided the equipment for
the study, it is important to note that there are other
organizations that produce headphone equipment that has noise
attenuating features (e.g., QuietComfort, Velodyne, Etymotic
and Westone).
Two types of noise attenuating headphones, IE and OE, were
used during this study. The IE BOSE design used in this research
has built-in ‘‘aware mode’’ technology. This allows the wearer to
switch the processing applied to the microphones on the outside
of each earbud creating an auditory approximation to removing
the headphones (BOSE, 2018). In essence, the wearer could
control how much of the environmental background noise they
could hear or block out. The second, an OE device, did not have
this mode and continually blocked noise in the environment.
During the trial, the children/students wore these headphones
during activities that had either large amounts of auditory stimuli
or during activities that the child found aversive due to their
perception of the auditory stimuli. Five points of data were
collected in the 4 phases of: (1) a week of baseline data collection;
(2) a week of an intervention; (3) a week of no intervention; and
(4) a week of the other intervention.
EDA was collected using an Empatica E4 wireless wearable
wristband device to measure arousal state. This wristband allows
for researchers to receive data either in real-time or up to 60 h
of data through a secure storage system (Empatica, 2018). For
purposes of this study, the Empatica RT App was utilized to
collect EDA data. Momentary assessment data was collected on
types of daily activity, setting, and the number of people in the
environment. This data was collected via Qualtrics on a provided
iPod or iPad mini (Qualtrics, 2019)6. In addition, a visual scan of
the environment was captured on the device’s camera application
while in video mode to confirm reported data. For researchers
5https://www.bose.com/en_us/products/headphones/noise_cancelling
_headphones.html
6https://www.qualtrics.com
to gain insight on actual environmental sound, smartphone
technologies VenueDB app was used to collect decibel readings
two times per session (EarMachine, 2018)7.
Measures
Child Descriptor Measures
All parents of children participating in the study reported
diagnosis based on the DSM-IV (i.e., PDD-NOS, Asperger
Disorder, Autism), as their children were originally diagnosed
using that classification system (American Psychiatric
Association, 2000). Participant ASD diagnoses included Autism
(n= 3), PDD-NOS (n= 2), and Asperger Disorder (n= 1).
Additionally, ASD diagnosis was confirmed through completion
of the GARS-3 (Gilliam, 2013). The GARS-3 is a widely used
instrument to identify ASD and estimate its severity. A GARS-3
Autism Index score of ≥70 was used to confirm diagnosis
(Gilliam, 2013).
The SSP (Dunn, 1999) was utilized to characterize auditory-
specific sensory processing differences of study participants. This
38-item caregiver questionnaire is standardized for children ages
3–10 years. Using a five-point Likert scale, caregivers report
how their child processes sensory information in day-to-day
situations. On the Auditory Filtering subtests, all participants
(n= 6) scored in the ‘‘definite difference’’ category, indicating
difficulty filtering auditory stimuli in comparison to peers
their age. On the Visual/Auditory Sensitivity subtest, three
children scored ‘‘probable difference’’ and three children scored
‘‘definite difference.’’
Outcome Variables
EDA reflects the skin conductance of the palmar sweat glands
controlled by the sympathetic nervous system (Dawson et al.,
2007), a marker of psychophysiological stress and anxiety. EDA
was collected using the wireless Empatica E4, which was placed
on the child’s non-dominant wrist and covered with a lightweight
fabric band to ensure continuous contact between the electrode
and the child’s wrist. The E4 utilized Ag/AgCl dry electrodes and
sampled data at 8 Hz. Although the wrist is a non-traditional
recording site, it has been found to be correlated with standard
measurement locations (van Dooren et al., 2012) and previous
research has utilized this equipment (or its predecessor, the Q-
sensor) to collect EDA from the wrist in children with ASD
(Baker et al., 2015, 2018, 2019; Fenning et al., 2017; Prince
et al., 2017). EDA was recorded continuously throughout the
study, beginning with a minimum of 20 min in order to allow
sufficient buildup of moisture between the electrodes and the
skin, followed by a baseline period and subsequent application of
the experimental condition phase (baseline, IE, no intervention,
OE). In longer-lasting situations, measurement of tonic SCL
and frequency of non-specific skin conductance responses (NS-
SCRs) are the most useful electrodermal measures (Dawson et al.,
2007). It is well-documented that these tonic EDA readings
increase in stressful or anxiety-producing situations (Dawson
et al., 2007).
7https://www.earmachine.com/venuedb/
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Pfeiffer et al. Noise-Attenuating Headphones
Covariates
Covariates were used to adjust for the estimation of the treatment
effects including: presence of other people (1: 1–2 people; 2:
3–5 people; 3: 5–10 people; 4: 10–20 people, and 5: 20 + people);
levels of visual stimuli (1: minimal, 2: moderate, and 3: a lot);
levels of noise (1: quiet minimal, 2: moderate, and 3: a lot); setting
(1: home, 2: community, and 3: school), and average value of the
two decibel readings per session.
Management of Electrodermal Activity
(EDA) Data
For EDA data, the number of NS-SCRs were totaled for each
participant and converted to a rate of fluctuations per minute and
only counted when the amplitude was greater than or equal to
0.05 µs, as suggested by Dawson et al. (2007). Due to the skewed
nature of our SCL and NS-SCR frequency data, and as is common
practice with EDA data (Dawson et al., 2007), the Yeo-Johnson
transformation (Yeo and Johnson, 2000) was applied to both SCL
and NS-SCR frequency prior to modeling.
Data were visualized and downloaded in CSV format from the
Empatica Connect Webportal for analysis. Data were imported
into the BIOPAC program AcqKnowledge and a low-pass filter
was applied to remove artifacts (Boucsein, 2012). Although
ambulatory EDA datasets often preclude traditional quality
assessment (e.g., ‘‘rigorous and methodical visual inspection
and human coding’’; Kleckner et al., 2018, pp. 1461; Boucsein,
2012; Boucsein et al., 2012), traditional visual inspection was
possible due to the limited duration of each data recording in
this study. Data cleaning was completed by hand, offline using
AcqKnowledge to visualize the data in order to ensure deletion of
movement artifacts (SCR data with a rise time <1 s, indicating
an increase too quick to be attributable to physiological
processes) and/or any abrupt drops which likely reflected the
loss of contact between the skin and E4 electrodes. Both
SCL and NS-SCRs were computer-scored off-line using the
BIOPAC program AcqKnowledge and hand-checked to ensure
no skin conductance responses were missed or incorrectly
marked (Boucsein, 2012; Boucsein et al., 2012). Ten percent
of the hand-coded data were double coded to ensure that
the identification of NS-SCRs was reliable, with a minimum
of 90% agreement (calculated as the number of matching
NS-SCRs divided by the total number of NS-SCRs coded by the
researchers). Overall, 88% of data were usable and included for
analysis. Excluded data were flat line waveforms (SCL <0.1
µs) with zero or few NS-SCRs. The unusable flat-lined data
were due to equipment error/problems and not participants
being electrodermal non-responders (Schoen et al., 2008; Keith
et al., 2018), as other recordings from those participants yielded
usable data. No data from one participant was initially usable;
however, the participant collected a second round of data with
100% usability.
Data Analysis
Analysis of Data Quality
To investigate how data quality varied systematically across
study designs and under different conditions, we used a
random effects ordered logistic model. Random effects models
were chosen because the observations came from the same
participants, violating the assumption of mutual independence.
Ordered logistic models were utilized because the outcome
variable (quality of data) had three ordered categories (1:
not acceptable, 2: acceptable, and 3: highest quality). We
regressed the outcome variable on selected variables, including
the study design factors (intervention sequence: ABAC or
ABAC; study phases: intervention or non-intervention)
and covariates to identify potential associations. Finally, we
conducted the Brant test to investigate the extent to which
the ordered logistic model followed the assumption of parallel
regression with this sample, which requires effects of the
exploratory variables to be consistent across thresholds in the
outcome variable. A cluster-robust estimator was used for
statistical inferences.
Analysis of Preliminary Efficacy
A random-effects model, Moeyaert’s model parametrization
(Manolov and Moeyaert, 2017), was used to evaluate the
intervention’s treatment effects. Radom effects models account
for auto-correlations among observations due to repeated
measurements from the same participants, but can also take into
consideration individual variations; for these reasons, random
effects models are recommended when the main interest is to
estimate the treatment effects of an intervention with repeated
measurements (Manolov and Moeyaert, 2017).
This study adapts Moeyaert et al.’s (2014)multilevel models
(Model 1A and 1B; p. 193). In these model specifications,
the average treatment effects are captured by the mean values
of the outcome variables among the observations during the
specific study phases, adjusted for other covariates. We fitted two
models with transformed NS-SCR frequency and transformed
SCL as the outcomes and selected variables as the predictors
(e.g., study design factors and covariates). We computed the
adjusted average treatment effects using the fitted models and
visualized the data to assist in clear interpretation. To determine
the preliminary efficacy of the interventions, Wald-test was used
to compare the adjusted average treatment effects between and
across phases Although there were six completed cases with
108 observations, we limited our analysis to the acceptable
data, yielding 95 observations across six participants. With this
sample size, the model is limited regarding model complexity
and numbers of independent variables. To better account for
within-person correlations, we used robust estimator, maximum
likelihood, and identity covariance structure in estimations. The
unusable data (excluded and missing values) are of lower concern
for this data as there were fewer than 12% of unusable values.
Finally, we conducted statistical diagnosis and investigated the
distributions of the residuals. Shapiro–Wilk normality test (W
statistics) and Skewness/Kurtosis test (χ2
(2)) were applied to test
for normality.
Additionally, we conducted supplementary testing
to investigate potential mechanisms through which the
interventions can effect SCL and NS-SCR frequency values.
We hypothesized that the interventions could provide protection
against the influence of decibel level of environmental noises on
SCL and NS-SCR frequency. In other words, we sought to test
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Pfeiffer et al. Noise-Attenuating Headphones
the moderating effects of the interventions on the relationships
between decibel levels and psychohysiological outcomes. To
examine this, we completed the following three steps. First,
the study phases were collapsed into three phases: (1) ‘‘No
Intervention’’ (baseline + washout phases); (2) ‘‘In-Ear’’; and
(3) ‘‘Over-Ear.’’ Second, an additional interaction term between
intervention and decibel reading was created and entered into the
model to investigate the interaction effect. Third, a continuous
time variable was created and entered into the model to adjust
for potential time effects to account for the 4 week duration of
the study. For these analyses, the random-effects model was used
with robust estimator to handle clustering effects as we had to
adjust for covariates, including noise levels, presence of other
people, presence of visual stimulation, and activity types. All
statistics were conducted in Stata 13.
RESULTS
Data Quality Analysis
The ordered logistic model passed the Brant test (χ2
(11)= 13.55,
p= 0.259), suggesting that the model did not significantly
violate the parallel regression assumption. As shown in
Table 3, data quality was significantly associated with Phase 2
(first intervention in sequence), Phase 4 (second intervention
sequence), and presence of other people. More specifically, the
first intervention phase (Phase 2) was less likely to have higher
quality data compared to baseline (Phase 1; 80% lower in odds;
a.OR = 0.20, 95% CI: 0.06–0.65, p<0.01). In contrast, the
second intervention phase (Phase 4) was more likely to have
higher quality data compared to baseline (197% higher in odds
(a.OR = 2.97, 95% CI: 1.07–8.26, p<0.05). Additionally, when
an additional unit of people were present (e.g., 1: 1–2 people; 2:
3–5 people; 3: 5–10 people; etc.), the odds of obtaining higher
data quality increased 82% (a.OR = 1.82, 95% CI: 1.48–2.25,
p<0.01). None of the other study design factors and covariates
(e.g., activity type) significantly correlated with the data quality.
Preliminary Intervention Efficacy
Results indicate that the residuals from both models followed
normal distributions (for transformed NS-SCR frequency:
TABLE 3 | Results of random-effect ordered logistic regression for data quality
analysis.
a.OR SE 95% CI
Group B (vs. A) 1.49 0.27 (0.09, 25.99)
Study Phases
Phase 2 (vs. 1) 0.20∗∗ −2.70 (0.06, 0.65)
Phase 3 (vs. 1) 0.16 −0.80 (0.00, 14.27)
Phase 4 (vs. 1) 2.97∗2.09 (1.07, 8.26)
Noise Levels 1.25 0.24 (0.20, 7.92)
Presence of Other People 1.82∗∗ 5.64 (1.48, 2.25)
Presence of Visual Stimulation 0.50 −0.98 (0.13, 1.99)
Activity Types
Stationary (vs. Active) 1.50 0.71 (0.49, 4.59)
Traveling (vs. Active) 3.24 1.21 (0.49, 21.57)
Average Decibel 1.03 1.50 (0.99, 1.08)
Time 1.09 0.40 (0.71, 1.69)
a.OR: adjusted Odds Ratio; ∗p<0.05; ∗∗p<0.01.
W= 0.97931, p= 0.14687 and χ2
(2)= 3.49, p= 0.1747; for
transformed SCL: W= 0.99098, p= 0.78370 and χ2
(2)= 0.71,
p= 0.6997). Additionally, none of the predictors in the two
models were significantly associated with the residuals. These
results suggest that the risks of model misspecifications are low.
The results of the model fitting are summarized in Table 4. For
the transformed NS-SCR frequency, the main effect of Phase 2
(β=−0.58, 95% CI: −0.81 to −0.36, p<0.01) and the interaction
effect between Phase 2 and Group B (β=−0.74, 95% CI: −1.31 to
−0.17, p<0.05) were significantly associated with the outcome.
In contrast, for transformed SCL, the interaction effect between
Phase 2 and Group B (β=−1.16, 95% CI: −1.91 to −0.42,
p<0.01), as well as between Phase 4 and Group B (β=−1.19,
95% CI: −2.07 to −0.31, p<0.01) were significantly associated
with the outcome.
To assist in interpretation, we computed the model-
adjusted average treatment effects across study groups and
study phases, and applied Wald-test (χ2
(1)) to compare their
average treatment effects (see Figures 1A–D). As illustrated
in Figures 1A,B, Groups A and B followed similar patterns
regarding participants’ psychophysiological responses to the
interventions. More specifically, in Group A (Figure 1A),
NS-SCR frequency was significantly lower in Phase 2 (Over Ear)
in comparison with Phase 1 (Baseline; χ2
(1)= 26.35, p<0.001).
Similarly, in Group B (Figure 1B), NS-SCR frequency was
significantly lower in Phase 2 (In Ear) in comparison with
Phase 1 (Baseline; χ2
(1)= 42.75, p<0.001), as well as in
Phase 4 (Over Ear) in comparison with Phase 3 (Washout;
χ2
(1)= 12.76, p<0.001). As illustrated in Figures 1C,D, we
observed similar patterns in the SCL data. Although we did
not find evidence of treatment effects in Group A for SCL
scores (Figure 1C), in Group B (Figure 1D), the SCL scores
were significantly lower in Phase 2 (In Ear) in comparison
with Phase 1 (Baseline; χ2
(1)= 53.72, p<0.001), as well as
in Phase 4 (Over Ear) in comparison with Phase 3 (Washout;
χ2
(1)= 54.72, p<0.001).
Additionally, we investigated potential mechanisms in which
the intervention may lower psychophysiological responses. We
hypothesized that the intervention may provide protection such
that when decibel values increased during the intervention
phases both NS-SCR frequencies and SCL remained low. We
fitted two random-effect models with an interaction effect
between intervention phases and average decibel readings
(see Table 5). Although we did not find evidence for the
interaction effect between intervention and environmental
decibel values for the transformed NS-SCR frequency, some
significant relationships were found for the transformed SCL.
Specifically, the interaction effects between decibel and In Ear
(β=−0.02, 95% CI: −0.04 to −0.002, p<0.05), as well as
decibel and Over Ear (β=−0.02, 95% CI: −0.04 to −0.004,
p<0.05) were significantly associated with the transformed
SCL scores.
To assist in interpretation, we computed model-adjusted
EDA measures across interventions and over the levels of
environmental decibel readings (see Figures 2A,B for NS-SCR
frequency and SCL, respectively). As illustrated in Figure 2A,
when environmental decibel levels increased, NS-SCR frequency
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Pfeiffer et al. Noise-Attenuating Headphones
TABLE 4 | Results of random-effect model for evaluation of intervention effects on electrodermal activity across study phases.
Transformed NS-SCR Frequency Transformed SCL
Beta SE 95% CI Beta SE 95% CI
Group B (vs. A) 0.76 0.87 (−0.93, 2.46) 1.55†0.80 (−0.02, 3.12)
Phase 2 (vs. 1) −0.58∗∗ 0.11 (−0.81, −0.36) −0.34 0.37 (−1.08, 0.39)
Phase 2 ×Group B −0.74∗0.29 (−1.31, −0.17) −1.16∗∗ 0.38 (−1.91, −0.42)
Phase 3 (vs. 1) 0.35 0.66 (−0.95, 1.64) 0.06 0.22 (−0.37, 0.49)
Phase 3 ×Group B 0.70 0.80 (−0.88, 2.27) 0.82 0.52 (−0.20, 1.84)
Phase 4 (vs. 1) −0.03 0.58 (−1.15, 1.10) 0.32 0.41 (−0.48, 1.12)
Phase 4 ×Group B −0.88 0.68 (−2.22, 0.46) −1.19∗∗ 0.45 (−2.07, −0.31)
†p<0.1; ∗p<0.05; ∗∗p<0.01; the results were further controlled for noise levels, presence of other people, presence of visual stimulation, activity types, and average decibel.
FIGURE 1 | Model Adjusted Back-Transformed Outcomes by Electrodermal Activity (EDA), Study Designs, and Study Phases. (A) Group A Average NS-SCR.
(B) Group B Average NS-SCR. (C) Group A Average SCL. (D) Group B Average SCL. Note. Bold broken lines represent the average values, with thin broken lines
representing the 95% CI. Non-transformed values are presented here to increase interpretability; statistical tests were conducted using transformed data.
increased during the stages without intervention, while NS-SCR
frequencies remained flat during the stages of interventions,
although these differences did not reach statistical significance
at 0.05 during formal testing (joint Wald-Test: χ2
(2)= 2.09,
p= 0.3524). Similarly, in Figure 2B, when environmental
decibel levels increased, SCL increased during the stages
without intervention but remained flat during the stages of
intervention. These differences did reach statistical significance
at 0.05 during formal testing (joint Wald-Test: χ2
(2)= 8.07,
p= 0.0177).
DISCUSSION
Research suggests that difficulties with auditory processing are
more commonly reported than any other sensory disorder in
individuals ASD (Tomchek and Dunn, 2007). Specifically,
hyperacusis, a negative and/or exaggerated response to
environmental stimuli related to auditory pathways (Asha’ari
et al., 2010; American Speech-Language-Hearing Association,
2016), is one of the most identified characteristics of auditory
processing differences. Researchers have identified that auditory
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Pfeiffer et al. Noise-Attenuating Headphones
TABLE 5 | Results of random-effect models for moderation effects of intervention on the relationship between average decibel and electrodermal activity.
Model parameter Transformed NS-SCR Frequency Transformed SCL
Beta SE 95% CI Beta SE 95% CI
Intervention
In Ear (vs. No Intervention) 0.51 1.18 (−1.80, 2.81) 1.39 1.02 (−0.62, 3.40)
Average Decibel 0.02 0.01 (0.00, 0.04) 0.02∗0.01 (0.01, 0.04)
In Ear ×Average Decibel −0.01 0.01 (−0.04, 0.01) −0.02∗0.01 (−0.04, −0.002)
Over Ear ×Average Decibel −0.01 0.01 (−0.03, 0.01) −0.02∗0.01 (−0.04, −0.004)
∗p<0.05; the results were further controlled for noise levels, presence of other people, presence of visual stimulation, and activity types.
FIGURE 2 | Model Adjusted Back-Transformed Outcomes by EDA and Intervention Types. (A) Average NS-SCR Frequency. (B) Average SCL. Note.
Non-transformed values are presented here to increase interpretability; statistical tests were conducted using transformed data.
dysfunction may be due to a slower auditory brain stem
response in children with ASD (Lukose et al., 2013; Miron et al.,
2018) and that there are anatomical links between the central
nervous system and the amygdala (Myne and Kennedy, 2018)
contributing to hyperacusis. Although hyperacusis is common
in children with ASD, there is minimal scientific evidence to
support commonly used interventions such as noise attenuating
headphones, which reduces or blocks auditory stimuli in the
environment. Results from the current study provide initial
support for the use of noise attenuating headphones to reduce
psychophysiological stress and anxiety from auditory stimuli,
as measured by EDA. Additionally, results identified a clear
positive relationship between the level of noise and EDA, which
was buffered by the use of noise attenuating headphones.
Despite the neurological links identified between hyperacusis
and ASD in research laboratory settings, to our knowledge, there
is no research examining the impact of interventions in the
natural environment on anxiety and stress levels within this
population. As the greatest levels of over-responsiveness are
found to be in multi-sensory environments full of potentially
unknown experiences (Green et al., 2015), one limitation of
laboratory research is that the testing environment does not
reflect experiences that occur within natural settings. For
example, a child with hyperacusis who has an aversive reaction
to sirens on the highway may begin to associate the car with
negative physiological experiences related to sounds that are
found distressing. Subsequently, this may result in that child
presenting with avoidance behaviors (i.e., tantrums, running
away, crying) in anticipation of the sound when getting into
or traveling in the car, even in the absence of the noise. As
discussed previously, this has shown to increase stress for those
who cannot communicate their feelings and can lead to the child
being misunderstood.
Robertson and Simmons (2015) completed a focus group
examining the sensory experiences of six adults with ASD.
Results identified that all participants reported strong physical
or emotional reactions to sensory stimuli in the environment.
Lack of control over the sensory stimuli was identified as a
factor that increased the perceived level of stress or anxiety.
Prior to this, Smith and Sharp (2013) conducted a qualitative
study in which adults with Asperger Syndrome reported sensory
stress that contributed to strong emotional responses and
coping strategies such as avoidance, fear and social isolation.
Specific to the auditory sensory system, Landon et al. (2016)
conducted qualitative research on adults with ASD and NS.
Participants reported various ways in which hypersensitivity to
noise impacted their participation in their day to day lives and the
emotions experienced due to perceived noxious auditory stimuli.
Despite some participants’ employing strategies such as the use
of earplugs or verbally discussing their discomfort to sounds
with those they knew, escaping from the potential problematic
situation was common. Research indicates that a correlation
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Pfeiffer et al. Noise-Attenuating Headphones
exists among anxiety, SOR and behavior (Mazurek et al., 2013).
Parents and families of children who are over-responsive to
sensory stimuli often report avoiding events and activities due
to the inability to prepare for potential unknown sensory
experiences (Bagby et al., 2012; Demchick et al., 2014; Pfeiffer
et al., 2017; Myne and Kennedy, 2018). A common natural
context for children is school. Noting that the average noise
level in classrooms exceeds WHO noise exposure guidelines,
Keith et al. (2018) found that adolescents with ASD, as well
as their matched neurotypical peers, performed worse on more
difficult tasks when noise was added. Providing individuals with
strategies to manage auditory hypersensitivities has the potential
to aid them in participating in meaningful occupations rather
than experiencing fear and anxiety and engaging in escape
and elopement.
The current study employed the use of noise attenuating
headphones within the natural environments of participants.
On the basis of neural plasticity (Ayres, 1972) and experience-
dependent plasticity (Alwis and Rajan, 2014), it is believed that
active participation in enriched environments promotes neural
change and cognitive behavioral improvements. Research has
indicated that biochemical changes occur from engagement in
meaningful trial and error learning during sensory and motor
tasks (Miller et al., 2009). By decreasing anxiety and stress
through the use of noise attenuating headphones, individuals
can engage in this trial and error learning within their natural
environment. It is further believed that repetition of normal
responses to sensory stimuli creates new neural pathways thus
providing the platform for successful participation in natural
real-world environments (Miller et al., 2009). By providing a
strategy that can be used on a day to day basis, individuals
can develop the experiences and theoretically build the platform
for successful participation. Though limited by small sample
size and quasi-experimental design, past research implemented
in natural environments identified positive behavioral and
academic outcomes when using noise attenuating headphones in
children with learning disabilities (Smith and Riccomini, 2013)
and ASD (Rowe et al., 2011).
Additionally, the majority of evidence is founded in parent
reports via questionnaires and interviews, as well as behavioral
assessment of retrospective videotape analysis (Tomchek and
Dunn, 2007; Myne and Kennedy, 2018). The wearable wireless
technology used in the current study allows for the collection
of physiological data measuring stress and anxiety within
natural environments, creating a real-time picture of events
and experiences of participants. As there are unpredictable
environmental factors, it is important to consider their potential
impact on data collection when using newer measurement
systems such as wearable sensors. Analyses were completed
for the current study on the quality of data collected from
the wearable sensors. Results identified that quality increased
over the course of data collection suggesting improvements
with additional practice in using the technology. Since the
data is collected in natural environments, it is often parents
and caregivers who initiated data collection sessions. Although
there was a high rate (88%) of useable data, additional practice
sessions with the people who collect data may increase the overall
quality when implementing research using wearable sensors.
Additionally, there was an increase in the quality of data when
more people were reported in the environment. It is possible
that this resulted in more support for parents and caregivers
from other people to ensure that data collection methods
were properly implemented (i.e., assistance in maintaining the
devices in proper position; ability to maintain focus on data
collection methods), although this requires consideration in
future research. Most importantly, results identified that neither
the activity of the child nor the environmental setting had an
impact on the quality of data suggesting that this type of data
collection can be used across activity types.
In understanding the physiological responses in conjuction
with the perceived experiences of parents/caregivers and
individuals with ASD, we can develop and design more targeted
interventions for auditory hypersensitivity. Psychologically,
triggers may be more easily identified, and treatment/coping
strategies can be assessed. If an individual can predict when
they will be in an environment leading to this increased
sympathetic activation, they may be able to use previously
identified environmental interventions, such as noise attenuating
headphones or other coping strategies, to continue participation
rather than avoid engagement in important life activities and
events. When triggered by stress, the emotional motor systems
pathway activates one of the branches of the autonomic
nervous system, the hypothalamic-pituitary-adrenal axis (HPA-
axis; Mayer, 2000). The triggering of the emotional motor
systems pathway can lead to emotional feelings and/or vigilance
arousal, autonomic responses, sensory modulation and/or
neuroendocrine responses. As interoceptive and exteroceptive
stress responses occur, the cyclical effects of the triggering of
the emotional motor systems pathways begin once again (Mayer,
2000). Thus, the use of noise attenuating headphones may
decrease physiological responses in perceived auditory aversive
situations, and may also provide opportunity for experiences as
triggering of the HPA-axis may be avoided.
Limitations
Similar to limitations of previous research on this topic, the
study was limited in the sample size. This is due in part to the
substantially varied environments within natural settings and
the individualized nature of participants’ EDA that requires the
use of single-subject design. Another limitation was the high
dropout rate (n= 6; 50%). One suggested method to decrease
drop-out rate would be to reduce the burden of data collection
by using an automated measurement system that is activated
at a designated decibel level, although this does not account
for aversive responses to types of noises vs. levels of noise. In
addition, no data was collected tracking the use of the aware
mode during the IE headphone use. This feature could serve
as a tool to design individualized interventions and should be
explored in future studies.
Future Research
Research has suggested that complete avoidance of sounds can
lead to increased anxiety, therefore exacerbating the negative
effects of hyperacusis (Jüris et al., 2014). Neurologically it is
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Pfeiffer et al. Noise-Attenuating Headphones
suggested that habituation-related plasticity occurs in the central
limb of stress-response circuits allowing the hypothalamic
pituitary adrenal axis to respond normally and possibly habituate
to new environments (Day et al., 2009). Consistent with this
neurological understanding, it has been shown that low levels of
noise exposure may lead to desensitization of unwanted sounds
(Jüris et al., 2014). Therefore, future research should implement
methodology to track the use of the ‘‘aware-mode’’ for the IE
headphones that allows the wearer to turn off the noise-blocking
feature, enabling filtering rather than complete avoidance. This
may prove more beneficial long-term in comparison to OE
headphones without ‘‘aware mode.’’
Although a link has been found between measures of
EDA and parent-report, provider-report, and research-coded
behavioral problems, it is highly recommended that future
research incorporate behavioral measures of stress in order
to examine whether a decrease in sympathetic activity has
any relevant impact on child behavior and attention to task.
Previous research has examined behavioral outcomes of using
NC headphones but did not incorporate measures of sympathetic
activity (Ikuta et al., 2016). Methodology could incorporate
ecological momentary assessment to collect behavioral data in
conjunction with wearable devices to examine the relationship
between noise, sympathetic activity, and behavioral responses.
Additionally, there is a hypothesis in the literature that
internal neuronal noise is a crucial factor influencing perceptual
abilities in ASD. Emerging evidence suggests that high internal
neuronal noise and poor external noise filtering impact
auditory perception in individuals with ASD (Park et al.,
2017). Recent research has implemented new measurement
methods using EEG global coherence to examine the relationship
between internal neuronal noise and the application of external
auditory quasi-Brownian noise vs. absence of external noise
(Mendez-Balbuena et al., 2018). Few studies have examined
the relationship between EDA and EEG. Of those that
have, correlations were found between SCL and specific
EEG waveforms in girls with Attention-Deficit/Hyperactivity
Disorder (Dupuy et al., 2014), as well as between EDA
response amplitude during generalized tonic-clonic seizures
and the duration of postictal generalized EEG suppressions
in individuals with epilepsy (Poh et al., 2012; Onorati et al.,
2017). In order to better understand the influence of auditory
interventions, such as noise attenuating headphones, future
research should examine the relationship between EDA and EEG
global coherence in individuals with ASD during the presence
and absence of targeted interventions. This would further expand
the understanding of the relationship between internal neuronal
noise and external noise filtering that is hypothesized to influence
perceptual abilities.
ETHICS STATEMENT
This study was approved by the Temple University IRB.
AUTHOR CONTRIBUTIONS
BP and LS contributed to the conception and design of the
study. BP completed all the data collection and LS organized
and interpreted all of the physiological data. CS assisted in
data organization and analyzed the data. AM assisted in the
organization of the data and data base, as well as helping in the
writing of the introduction and discussion of the manuscript.
BP (introduction, methodology and discussion), LS (parts of the
methodology) and CS (data analysis and results) wrote the initial
drafts of the manuscript. All authors contributed to manuscript
revision, read and approved the submitted version.
FUNDING
This project was partially funded through a contract with Bose
Incorporated. The funder provided a grant and small equipment
to support the implementation of the study. The funder was not
involved in the study design, collection, analysis, interpretation
of data, the writing of this article or the decision to submit it for
publication. Open access publication fees are provided through
a start-up fund of the primary author provided by the College
of Public Health at Temple University. LS was supported by the
National Institutes of Health under NCMRR K12 HD055929.
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
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