Sleep Research: A Primer for Media Scholars

Abstract

The average amount of sleep people of all ages get has declined sharply in the past fifty years. The detrimental health effects of sleep deprivation are well documented and substantial. Even though electronic media use often takes place in the hours before sleep, the extent to which media use may interact with sleep is understudied and not well understood. Communication scholars are well-positioned to contribute in this area, as a systematic, theoretical understanding of the relationship between media and sleep is still lacking. This primer charts the state of knowledge on electronic media and sleep and explores possible next steps. First, we introduce the problem of sleep deprivation and describe the basic science of sleep with relevant terminology. Then, we review the research on electronic media and sleep and offer an agenda for research.

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Running head: A PRIMER FOR MEDIA SCHOLARS 1
This is a post-print version.
Sleep Research: A Primer for Media Scholars
Article accepted for publication in Health Communication
Please cite as: Exelmans, L., Van den Bulck, J. (ahead of print). Sleep Research: A Primer
for Media Scholars. Health Communication.
https://doi.org/10.1080/10410236.2017.1422100

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Abstract
The average amount of sleep people of all ages get has declined sharply in the past fifty years.
The detrimental health effects of sleep deprivation are well documented and substantial. Even
though electronic media use often takes place in the hours before sleep, the extent to which
media use may interact with sleep is understudied and not well understood. Communication
scholars are well-positioned to contribute in this area, as a systematic, theoretical
understanding of the relationship between media and sleep is still lacking. This primer charts
the state of knowledge on electronic media and sleep and explores possible next steps. First,
we introduce the problem of sleep deprivation and describe the basic science of sleep with
relevant terminology. Then, we review the research on electronic media and sleep and offer an
agenda for research.

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“Technology has decoupled us from the 24-hour day to which our bodies evolved.”
(Charles Czeisler).
The Sleep Problem
Studies have estimated that we sleep 1 to 2 hours less than 50 years ago (Bixler, 2009;
Cappuccio & Miller, 2011). More than one in three (37.1%) adults are now sleeping less than
7 hours per night (Schoenborn & Adams, 2010), an amount at which physiological and
neurobehavioral problems develop and become progressively worse under chronic conditions
(Van Dongen, Maislin, Mullington, & Dinges, 2003). The National Sleep Foundation
(Gradisar et al., 2013) reported that 6 out 10 Americans (13-64 years old) are not getting
enough sleep to function properly. A study by Pallesen et al. (2008) showed an increase in
sleep onset problems among teens between 1983 and 2005 and Matricciani, Olds, and Petkov
(2012) found rapid declines in children’s sleep duration over the course of a century. In all, it
appears that a growing number of people is struggling with sleep, facing sleep problems, or
coping with chronic sleep insufficiency.
The consequences of sleep loss can be far-reaching. It is estimated that around 20% of
serious car accidents are connected to driver sleepiness; and fatigue induced occupational
errors are thought to be partly responsible for major global disasters such as the Exxon Valdez
oil spill or the nuclear reactor meltdown in Chernobyl (Institute of Medicine, 2006). The
cumulative effects of chronic sleep deprivation stretch to a variety of physical and mental
health consequences, including reduced memory function and learning ability, negative mood
states, risk behavior, obesity, reduced immune response, hypertension, and cardiovascular
disease (Luyster, Strollo, Zee, & Walsh, 2012; Strine & Chapman, 2005) . In sum, negative
effects of poor sleep produce a ripple effect, by spreading to a wide range of health issues,
resulting in an overall reduced quality of life and increased mortality (Grandner, Hale, Moore,
& Patel, 2011).

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Given sleep’s pivotal role in health, research into the predictors of poor sleep has
spiked over the past decades. There is mounting evidence that electronic media use
contributes significantly to a shorter sleep duration, sleep disruption, longer sleep latency, and
overall poorer sleep quality (Hale & Guan, 2015). While clearly an interdisciplinary topic,
research on the effects of media use on sleep has mostly been conducted by sleep researchers.
The involvement of communication scholars in this field can have a crucial impact on its
advancement, as their theories and research methods are highly relevant for and transferrable
to sleep research. Consequently, communication scholars have a potentially significant role to
play in tackling the global epidemic of sleep insufficiency.
The goal of this primer is to help bridge the gap between sleep medicine research and
media studies. To that end, we will first describe the basic mechanics of sleep, introducing
relevant vocabulary (that will be highlighted in bold). Next, we will briefly review the
evidence linking media use to sleep and summarize the three most common explanations for
these effects. Finally, we will outline an agenda for research on this topic, suggesting where
the expertise of communication scholars is most valuable.
Sleep: Basic Mechanics and Terminology
What Is Sleep?
Although sleep may seem like a biologically passive state, it involves a complex
interaction of physiological processes (Luyster et al., 2012). In general, sleep is divided into
two states: non-rapid eye movement (NREM) sleep (75-80% of total sleep time) and rapid
eye movement (REM) sleep (20-25% of total sleep time). NREM sleep is divided into three
stages, characterized by a progressive decrease in brain wave activity, eye movement, and
heart rate. NREM stage 1 refers to sleep onset, a light stage of sleep often characterized as
“drifting off”, taking up 5% of total sleep time. During stage 2 of NREM sleep, eye
movement stops, conscious awareness of our surroundings fades, and brain waves slow down.

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In total, we spend 45-55% of our sleep time in stage 2. Stage 3 of NREM is called deep sleep
or slow wave sleep, characterized by extremely slow brain waves (15-25% of total sleep
time). Most of the recovery processes take place during this stage. When awakened during
deep sleep, people feel groggy and disoriented for several minutes. During the last stage -
REM sleep - muscles relax completely, heart rate and blood pressure increases and eyes move
rapidly (20-25% of total sleep time). Information processing and memorization take places.
Because of increased brain activity, we often dream during this stage. We repeat the sleep
cycle of NREM and REM sleep 3-7 times per night, each cycle lasting 90-110 minutes. After
each cycle, we approach wakefulness before drifting off to NREM 1 again. As the night
progresses, the length of deep sleep (stage 3 NREM) decreases and REM sleep increases
(Lee, 2016; Luyster et al., 2012; Markov, Goldman, & Doghramji, 2012).
What Makes Us Sleep – Or Not? Sleep Regulation Processes
The two-process model describes the timing and regulation of sleep and wakefulness as
an interaction between the homeostatic and the circadian process. The homeostatic
process refers to the need for sleep or sleep pressure, which increases the longer you stay
awake. The homeostatic drive reaches its peak in the evening, decreases during sleep and is at
its lowest upon awakening. People suffering from sleep shortage experience a greater
homeostatic drive or a tendency to make up for lost sleep, typically resulting in shorter sleep
latency and longer total sleep time. The popular term for the circadian process is one’s
biological or internal clock, which regulates our circadian rhythm (circa = about; dian = day),
i.e., all the biological variables that fluctuate in a cycle length of approximately 24 hours
(Markov et al., 2012). Apart from the sleep-wake cycle, other variables that follow a circadian
rhythm are one’s body temperature, heart rate, and hormonal regulation. While our circadian
rhythm is intrinsic, meaning that it has an endogenous clock following a 24h cycle, it is also
constantly synchronized to maintain that 24h cycle by obtaining information from the

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environment, a process called entrainment. Based on the information obtained from the
environment, and its own endogenous circadian clock, the circadian system regulates the
body’s sleep and wakefulness according to the time of the day
Both processes interact to regulate sleep: the sleep pressure from the homeostatic drive
increases throughout the day but is opposed by the circadian process, which sends alerting
signals to let us stay awake. When night comes, the circadian process will abruptly stop
sending the alerting signals, which allows the homeostatic sleep drive to take over, so sleep
becomes possible (Gillette & Abbott, 2005; Luyster et al., 2012; Markov et al., 2012).
How Much Sleep Do We Need?
Sleep need varies strongly between individuals (Ferrara & De Gennaro, 2001). For
example, it is well-documented that women have a greater sleep need than men and that sleep
need declines with age (Hume, Van, & Watson, 1998). Some individuals may need
significantly more or less sleep than the average and are categorized as long vs. short sleepers
(Aeschbach et al., 2003). Chronotype, or the extent to which someone can be categorized as
a morning or evening type (Roenneberg, Wirz-Justice, & Merrow, 2003), also influences our
sleep habits. Morning and evening types differ in the timing of sleep and wakefulness (i.e.,
their circadian rhythm). Morning types (referred to as “larks”) have an advanced internal
clock: they prefer earlier bedtimes and rise times, have a lower sleep need and are more alert
upon awakening. Evening types (“owls”) prefer to stay up late, tend to have a greater sleep
need, function at their peak later in the day, and have more irregular sleep schedules (Taillard,
Philip, & Bioulac, 1999). Although the common recommendation of getting 8 hours of sleep
per night represents the average sleep needed to function properly, “the amount of sleep we
need to be at our best is as individual as the amount of food we need” (Ferrara & De Gennaro,
2001, p.4).

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Adolescents and Sleep: A Risk Group.
Over the course of puberty, adolescents develop sleep phase delay: compared to
children, they become increasingly inclined to stay up later at night and rise later in the
morning. They thus evolve from larks to owls. This shift in sleep phase is attributed to a
convergence of biological (such as delayed secretion of melatonin) and psychosocial (such as
increased social pressure, academic demands, and autonomy) changes during puberty
(Carskadon, 2011; Wolfson & Carskadon, 1998). However, concurrently with these changes,
there is a societal pressure on adolescents’ sleep: school’s start times function as the
predominant determinant of their rise time. School times cannot be delayed and often start
even earlier for adolescents than for their younger counterparts. While they are biologically
programmed to stay up later, school’s start times are incongruent with this shift. As a result,
sleep time becomes compressed and there is little opportunity to make up for lost sleep,
typically resulting in a tendency to compensate by sleeping late during the weekend, which
disrupts the sleep cycle further (Carskadon, 1999, 2011; Wolfson & Carskadon, 1998). While
sleep need generally declines with age, it does not change during adolescence: teens need
approximately 9 hours of sleep, the same as children. Research nonetheless shows that they
typically obtain only 6.5 to 7 hours of sleep, resulting in a severe and chronic sleep
deprivation of nearly 2h per day (Calamaro, Mason, & Ratcliffe, 2009; Hysing, Pallesen,
Stormark, Lundervold, & Sivertsen, 2013). In addition, other aspects of adolescent life, such
as academic and social pressure and stress, often interact and also lead to irregular sleep
patterns (Dahl & Lewin, 2002; Wolfson et al., 2003). In sum, adolescents experience a
dramatic sleep change as they mature. Inadequate sleep during this developmental phase can
have severe negative effects, both in the short and long term; marking adolescents as a
particular risk group in sleep research.

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How Do We Measure Sleep?
Analyses of sleep involve the assessment of multiple sleep indicators, which can be
measured objectively or subjectively. The most important indicators are bedtime and rise time,
sleep duration and sleep quality. However, to capture sleep duration accurately, additional
parameters are needed, such as the time it takes to fall asleep (sleep latency) and the
frequency and duration of night wakings (sleep disturbances) (Matricciani, 2013). Sleep
quality is also partly dependent on a person’s sleep efficiency, a ratio of the time spent asleep
to the time spent in bed, and is normally around 85-90% in a healthy population (Buysse,
Reynolds, & Monk, 1989).
The gold standard in the objective measurement of sleep is polysomnography (PSG).
The “poly” in the word refers to the fact that PSG records various sleep parameters: the
electrical activity in the brain, eye movements, respiration rate, cardiac activity and limb
movements. Together, these indicators provide an accurate assessment of the diagnostic
criteria needed to determine sleep disorders, but it requires special equipment and expert
training. This method has excellent internal validity, but the fact that it is measured in the
highly artificial setting of a sleep lab reduces its external validity for media research questions
(Lee, 2016; Markov et al., 2012). Duration, type, access, content, and awareness of media use
in a laboratory setting is likely to be very different from a typical evening spent at home.
The second way of objectively measuring sleep is the use of actigraphy or ambulant
monitoring. An actigraph or accelerometer is usually worn on the wrist and will estimate
whether a subject is awake or asleep based on body movement. Actigraphy has proven to be
reliable and valid in studying sleep in healthy populations and is far less invasive, cheaper,
can be done at home, and makes longitudinal measurement possible. However, it also has

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some notable disadvantages. It has an oversensitivity of scoring nocturnal movements as
wakefulness, thereby deflating the estimate of total sleep (Short, Gradisar, Lack, Wright, &
Carskadon, 2012). Moreover, it cannot discern between various sleep stages or between sleep
disorders, and becomes less reliable in clinical samples. So far, there is a large variety of
actigraphs, but there are no device standards, standardized units of measurement or analytic
methods, which make comparison across studies difficult. Therefore, the application is often
limited to monitoring the circadian rhythm, studying sleep in samples where PSG is less
feasible (such as infants or elderly people), monitoring treatment effects, and estimating
habitual sleep patterns over time (Ancoli-Israel et al., 2003; Lee, 2016; Sadeh, 2011).
In addition to these objective measurements, there exist a multitude of paper-based
self-report assessments of sleep. A meta-analysis by Hale and Guan (2015) showed that 99%
of studies on media and sleep rely on self-reports. There are several review studies on the
various self-report sleep scales that are available for research (Devine, Hakim, & Green,
2005; Spruyt & Gozal, 2011). To measure habitual nighttime sleep, the Pittsburgh Sleep
Quality Index (PSQI) (Buysse et al., 1989) is the most widely used (Lee, 2016). The
measure has undergone extensive validation and can be used in both clinical and non-clinical
samples. The PSQI integrates seven sleep components: sleep duration, subjective sleep
quality, sleep latency, sleep efficiency, use of sleep medication, sleep disturbances, and
daytime dysfunction. Both the sub scores on the components and the global score can be used
in research settings. Although the PSQI is very user friendly for both researcher and
respondent and can distinguish between patients and controls, it has received criticism too, for
instance regarding its measurement sensitivity (the usual cut-off score to discern good from
poor sleepers is argued to be too low), and that it does not pay sufficient attention to daytime
experience (Carpenter & Andrykowski, 1998). It should therefore preferably by accompanied
by a measure of fatigue, such as the Epworth Sleepiness Scale (Johns, 1991) or Flinders

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Fatigue Scale (Gradisar et al., 2007) . When interested in tracking one’s experience with sleep
of having an indicator of the stability in sleep, sleep diaries are also valuable measurement
tools.
Comparison studies with diary data and objective sleep measures have shown that self-
reports offer a valid way to measure sleep variables (Monk et al., 2003; Wolfson et al., 2003),
and research concluded that diary data are superior to actigraphy when it comes to predicting
fatigue (Short et al., 2012) Nonetheless, self-report measures of sleep also have their
downsides, such as their vulnerability to recall bias (Hassan, 2005). It is also well-
documented that insomniacs, for example, underestimate their sleep duration, a phenomenon
called sleep-misperception (Harvey & Tang, 2013). Spruyt and Gozal (2011) have remarked
that there is an abundance of self-report measures for sleep that has not undergone rigorous
psychometric evaluation. There is, therefore, a big distinction between the use of published
versus validated questionnaires, a subtle difference that may escape notice when reading
journal articles or books.
While sleep researchers tend to take comfort in objective sleep assessment tools, it can
be argued that the subjective experience of sleep and fatigue may be at least as important, if
not more important, than objective measures, at least for some issues (Pilcher, Ginter, &
Sadowsky, 1997). This perspective is reflected in an increase in the use of subjective reports
as the outcome variable (Ancoli-Israel et al., 2003). This is further supported by the fact that
there are large individual differences in sleep need: one person will need 9 hours of sleep to
feel rested whereas the next only needs 6 hours (Ferrara & De Gennaro, 2001). In sum,
scholars have recommended, if the use of PSG is not feasible or advisable, to use actigraphy
in concert with subjective data to obtain a full and accurate assessment of sleep (Lee, 2016;
Sadeh, 2011). For complete self-reported research, it is advised to measure multiple

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parameters of sleep, including bedtime, sleep latency, rise time, night awakenings, sleep
quality, and to separate weekdays from weekend days (Cain & Gradisar, 2010).
What about sleep apps?
In recent years, a staggering number of mobile phone apps have emerged that claim to
accurately register sleep related data. Some of these provide digital diaries to monitor sleep
habits (such as Sleep Journal or Yawnlog); while others track movement in bed to measure
sleep and thus require people to keep their phone on the bed (such as Sleep Cycle or Sleep
Bot). Van den Bulck (2014) argued that sleep apps have the potential to expand the field by
introducing a cost-effective way to obtain an unprecedented access to sleep data. There is,
however, a multitude of devices available and a lack of validation studies to assess whether
sleep apps and other wearables can accurately and consistently assess sleep parameters. Given
these limitations concerning the measurement validity of apps, sleep researchers have been
wary of their use in research settings (Behar, Roebuck, Domingos, Gederi, & Clifford, 2013;
Van den Bulck, 2014).
Electronic Media & Sleep: State of the Art
In addition to societal changes, such as longer working hours, shift work schedules,
and the idea that sleep can be easily missed out on, sleep insufficiency is exacerbated by
technology use (Bixler, 2009; Cappuccio & Miller, 2011). The shift towards poor sleep has
coincided with technological revolutions that have intensified media usage. Total daily media
use amounts to approximately 6 hours per day for 8-12 year olds and 9 hours per day for 13-
18 year olds (Common Sense Media, 2015). . In addition, electronic media have gravitated
towards our bedroom over the years (Bovill & Livingstone, 2001), which has been known to

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stimulate evening usage (Cain & Gradisar, 2010). In all, we devote as much time to our
screens as we should be to sleeping.
Over the past decade, a growing number of scholars have studied the interplay
between electronic media and sleep. The most recent review study covered a total of 67
studies (Hale & Guan, 2015). While we cannot provide a comprehensive review of all the
research in this area, we will attempt to give an overview of the main findings on electronic
media and sleep and, most importantly, outline an agenda for future research. Readers who are
interested in a more extensive review are redirected to the meta-analyses by Cain and
Gradisar (2010) and Hale and Guan (2015).
Electronic Media as Inhibitors of Sleep
A poll by the National Sleep Foundation (2011) indicated that 95% of 1508
respondents (13-64 years old) used electronic media within the hour before bed. In another
study, almost 70% of teens indicated that electronic media use was their final evening activity
(Kubiszewski, Fontaine, Rusch, & Hazouard, 2013). The large majority of studies (90%) on
electronic media document negative effects of media use on numerous sleep parameters, such
as delayed bedtime (e.g., Kubiszewski et al., 2013; Oka, Suzuki, & Inoue, 2008; Woods &
Scott, 2016), shorter sleep time (e.g., Arora, Broglia, Thomas, & Taheri, 2014; Paavonen,
Pennonen, Roine, Valkonen, & Lahikainen, 2006), longer sleep latency (e.g., Dworak, Schierl,
Bruns, & Strüder, 2007; King et al., 2013), increased daytime fatigue (e;g., Lemola,
Perkinson-Gloor, Brand, Dewald-Kaufmann, & Grob, 2015; Li et al., 2007), and night
awakenings and nightmares (Van den Bulck, 2004a; Van den Bulck, Çetin, Terzi, & Bushman,
2016). Such results have been replicated across media devices, and have been found in cross-
sectional, longitudinal and experimental designs. The presence of media in the bedroom
appears to exacerbate the problem: those with media in the bedroom report increased usage
(Christakis, Ebel, Rivara, & Zimmerman, 2004), and sleep duration was significantly lower

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among teens who had four or more devices in their bedroom (National Sleep Foundation,
2014). Although variations in effects can be found across cultures based on the access to
media and culturally related sleep problems (Owens, 2004), the negative effects of technology
use on sleep are a worldwide phenomenon.
Electronic Media as Facilitators of Sleep
One of the most common reasons parents have reported for having a television in a
child’s bedroom was the hope that it would help the child fall asleep (Rideout & Hamel,
2006). There has also been an increase in the development of content specifically designed to
help children calm down and transition to sleep at the end of the day (Zimmerman, 2008).
Significant proportions of adolescents (Eggermont &Van den Bulck, 2006), and adults
(Exelmans & Van den Bulck, 2016b) have reported using various media as a sleep aid.
Gooneratne et al. (2011) reported that the most common method of self-treating sleep
problems among older adults was watching television and Harmat, Takacs, and Bodizs (2008)
found that listening to relaxing classical music can reduce sleep problems in students. Overall,
the few studies that have investigated the idea that media may also be beneficial for sleep,
suggest that the practice of using media as a sleep aid appears to be counterproductive: those
who report using media as a sleep aid, also report poorer sleep (Eggermont & Van den Bulck,
2006; Exelmans & Van den Bulck, 2016b).
Underlying Mechanisms
The existing research on electronic media and sleep has mostly focused on charting
the effects, and less on investigating the underlying mechanisms that explain them. Cain and
Gradisar (2010) summarized them in a framework that contains three explanations for the
observed effects.
(Blue) light.

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To attain optimal sleep quality and duration, the circadian clock is aligned with the
sleep-wake cycle. Our internal circadian clock resides in the hypothalamus, just above the
point where optic nerves cross, and, therefore, the most potent external time cue or
“zeitgeber” (zeit = time; geber = giver) for this synchronization is light. The signals received
by our internal clock are sent through various regions of the brain, including the pineal gland,
which responds by reducing the output of melatonin. Melatonin is often called the sleep
hormone, because its levels usually increase when darkness falls, making us sleepy. In
addition, the internal clock will regulate our heart rate, body temperature, and arousal levels to
attain an optimal sleep mood (Luyster et al., 2012; Markov et al., 2012).
Exposure to artificial light may result in misalignment between the sleep-wake cycle
and the internal clock. Exposure to light late in the day or early in the night will slow down
the internal clock, creating a fluctuation rate that exceeds 24 h. Light exposure in the evening
has been found to increase alertness and arousal levels, suppress melatonin production (Wood,
Rea, Plitnick, & Figueiro, 2012), and induce phase delay in the circadian clock (i.e. delay
sleep time) (Cajochen et al., 2011; Wood et al., 2012). The effects of light on the secretion of
melatonin are acute and can extend for hours beyond the light exposure (Berson, Dunn, &
Takao, 2002).
Research highlights that the effects of light on melatonin output and alertness may
vary depending on the (1) light level and spectrum, (2) duration of exposure, (3) size and
proximity to the screen, and (4) type of task. Shortwave length or blue light is most disruptive
to melatonin production. This is commonly the type of light emitted by media screens
(Cajochen et al., 2011). Wood et al. (2012) observed melatonin suppression after 1h of using
self-luminous tablets in young adults. They measured variations of light intensity during such
usage, and found that certain tasks on tablets are more harmful to sleep than others. Chang
and colleagues (2015) found that, compared to reading a printed book, reading a book on a

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light-emitting e-reader before bedtime decreased subjective sleepiness, suppressed melatonin
production, prolonged sleep latency by 10 minutes, and impaired morning alertness. Overall,
these results point at a phase delay of the circadian clock, associated with increased risk of
developing chronic sleep deficiency, or sleep disorders such as delayed sleep phase disorder
or sleep onset insomnia.
Sleep displacement.
To date, the most commonly reported effects of electronic media on sleep are delayed
bedtime, prolonged sleep latency and decreased sleep duration (Cain & Gradisar, 2010).
These findings support the displacement hypothesis, which postulates that the time spent
using media replaces time that would otherwise be spent sleeping (Van den Bulck, 2000). An
early explanation of this process used Kubey's (1986) concept of media use and unstructured
time. Media use takes place during leisure time and has no predefined beginning or end
points. Van den Bulck (2000) argued that unstructured activities are most likely to displace
activities that are similarly unstructured, such as sleep. Indeed, the start and endpoint of sleep
are largely a matter of choice, with the exception, perhaps, of sleep policing attempts by
parents. As media use peaks before bedtime, sleep is vulnerable to displacement by the media
(Cain & Gradisar, 2010; Van den Bulck, 2004b).
According to Exelmans and Van den Bulck (2017a) sleep displacement has evolved
into a two-step process. The first step of sleep displacement occurs when people postpone
going to bed because they prefer spending time using the media. This is the most commonly
used meaning of the concept. People are, however, using media increasingly often and for
increasing amounts of time while already in bed. Consequently, people may not only be
putting off going to bed, but also delaying going to sleep once in bed. In their survey among

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338 young adults, there were almost 40 minutes between people’s bedtime (the time at which
they went to bed) and what they referred to as shuteye-time (the time at which they decided
to try to sleep). The authors defined this second stage of sleep displacement (i.e., between
bedtime and shuteye time) as shuteye latency (Figure 1). Using customary self-reported
sleep measures, half of their participants would have had to be categorized as having sleep
onset insomnia (i.e., they were awake for longer than 30 minutes after going to bed). The
study shows, however, that this particular group spent this time in bed on other activities than
trying to go to sleep. Notably, media use was identified as an important driver of shuteye-
latency: a considerable proportion of the behaviors people reported engaging in in bed
involved media. In sum, the authors concluded that the fast-paced changes in media
necessitate a continuous reevaluation and update of existing survey questions in light of new
trends in both media consumption and sleep behavior (Exelmans & Van den Bulck, 2015,
2017a).
It is worth noting that the displacement hypothesis so far appears to hold exclusively
in young samples. For adults, research shows that media use is associated with later bedtimes,
but also with later rise times, and that, consequently, sleep duration does not appear to suffer.
This process is referred to as time-shifting. It has been hypothesized that many adults have
more control over their daytime schedule, which allows them to adjust both their shuteye- and
rise time to their media use (Custers & Van den Bulck, 2012; Exelmans & Van den Bulck,
2014, 2016a).
[FIGURE 1 AROUND HERE]
Arousal.
Violent and sexual content are as omnipresent in the media as they have ever been
(Brown et al., 2006; Huesmann & Taylor, 2006). It has been shown that exposure to such
content may induce excitement, fright, and stress reactions in children (Harrison & Cantor,

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1999). The heightened arousal resulting from this exposure may be associated with difficulties
falling asleep or poor sleep quality. Paavonen et al. (2006) reported that children who had
viewed adult-targeted programs had a significantly higher risk of having sleep problems. A
more recent study showed that violent daytime media exposure was associated with increased
sleep problems, while this was not true for nonviolent daytime media use (Garrison, Liekweg,
& Christakis, 2011). Viewing frightening content may coincide with having nightmares and
night wakings, thus reducing sleep quality (Van den Bulck, 2004a; Van den Bulck et al.,
2016). An intervention study by Garrison and Christakis (2012) found that young children
whose parents had replaced violent media content with prosocial content reported improved
sleep during follow-up. While there are only a limited number of studies taking into account
the content of the media consumption, they suggest that the type of content may exert a
significant impact on sleep quality, presumably through its effect on arousal.
Agenda for Research
Identifying Sleep Correlates of Types of Media & Usage Styles
The topic of media and sleep covers a wide range of effects, given the wide range of
devices and outcome variables. While there is now a considerable body of research on the
effects of television, video games, and the internet, more recently introduced media such as
smartphones and social networking sites have received less attention. It has been hypothesized
that interactive media are more detrimental than “passive” media (Dworak et al., 2007;
Gradisar et al., 2013). In part this is because social interaction capabilities mean that the
devices have the potential to re-engage the user, even at night, when that user has decided to
stop using them (Arora et al., 2014; Van den Bulck, 2003; Woods & Scott, 2016). The
likelihood of displacing sleep in one user could also be higher when the termination of media
use is partly dependent on another user at the other end of the communication. In all, the
literature remains inconclusive on (1) which aspects of sleep are affected most by media use

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and (2) whether some sleep parameters are more affected by some media than others (Cain &
Gradisar, 2010).
Few studies so far have considered differences in media usage styles. Although media-
multitasking is a well-known topic in media research, it has received scant attention in sleep
research. Calamaro and colleagues (2009) referred to a multitasking index in their study, but
merely divided the time spent on various media by the time frame they were interested in. As
such, the effects of simultaneously using different media devices have not yet been covered.
In addition, the research regarding the effects of television viewing on sleep has yielded
inconsistent findings (Hale & Guan, 2014) leading Bartel and colleagues (2015) to conclude
that television was not a significant risk factor for sleep. The practice of television viewing
has, however, undergone tremendous changes, that warrant a timely update of its
measurement strategy in sleep research. Binge viewing effects on sleep, for instance, are a
particular concern, considering the prevalence of consuming television in drenches rather than
drips (Matrix, 2014). One study reported that binge viewing frequency was associated with
poorer sleep quality, a relationship that was fully mediated by increased cognitive pre-sleep
arousal (Exelmans & Van den Bulck, in press). Excessive or compulsive usage of media or
so-called media addictions are also understudied topics. Finally, there could be merit in
looking at the differential impact of work-related vs. recreational media use.
Improving Measurement of Media Use
Sleep research has focused predominantly on the frequency and duration of media
usage when predicting the effects on sleep. To chart and understand the processes that are
involved in the interaction between media use and sleep, a number of refinements should be
considered. First, measures of media use should include when and where those media were
used, because research shows nightly or in-bed media use has a stronger impact on sleep than
overall media use (Exelmans & Van den Bulck, 2016a; Lemola et al., 2015; Woods & Scott,

A PRIMER FOR MEDIA SCHOLARS 19
2016). Second, while researched in an only limited number of studies (37% according to Hale
and Guan, 2015), researchers should also study the social context of use. It can be
hypothesized that a partner, parent, or child likely co-determine the termination of media
usage or the timing of lights out. Third, little is known about differences in effects of media
use depending on the content. Relaxing, (negatively or positively) arousing, or frightening
types of content are likely to have a different effect on sleep outcomes. Moreover, in the era of
streaming services and digital television, content is hyper-personalized and viewers are
increasingly exposed to sophisticated narrative structures that are aimed at tying the viewer to
the screen (Jenner, 2014, 2015). For social media or smartphones, there is virtually no
research differentiating between the various activities done on the screen or active vs. passive
usage of a social networking site. Fourth, we found only two studies that have examined the
role of media engagement in predicting the effects on sleep: Smith, Gradisar, King, and Short
(2017) demonstrated that increased flow significantly predicted bedtime delay among gamers
and Woods and Scott (2016) found that those who were more emotionally invested in social
media use experienced poorer sleep quality. It would be interesting to study the association
between arousal originating from increased investment in media use (i.e., flow, transportation,
fear of missing out), and pre-sleep arousal. Fifth, a recent study by Exelmans and Van den
Bulck (2017b) led to unexpected results when taking into account media habits. Strong media
habits appeared to prevent bedtime delay. More research on habit formation, habit strength,
regarding the same and other media would further advance our knowledge of the processes
leading to media displacement and time shifting. Six, based on the findings regarding the
effect of blue light emitted by screens, researchers could look at the technical characteristics
of the device such as the proximity, size and color composition of the screen. In all,
developing a systematic understanding of the characteristics in media usage patterns that are
most harmful to sleep could benefit the development of targeted interventions.

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Increasing Diversity in Research Samples
Most studies on the relationship between media use and sleep behavior have been
conducted among children and adolescents, which is unsurprising. In addition to being a risk
group for sleep deprivation, children and teens are far more preoccupied with media than
adults are assumed to be. It seems that adolescents are being set up to fail and become stuck in
a cycle of dysregulation where their unhealthy sleep behavior stimulates increased media
usage and vice versa. The particular concerns over the disruptive effects of media on sleep
aimed at young children and adolescents are therefore certainly justified.
However, this reasoning should not imply that studies among adults are unjustified or less
worthwhile. 90% of people aged 13-64 yrs old use technology around bedtime (Gradisar, Wolfson,
Harvey et al., 2013).
For example, the proportion of video gamers has been found to diminish with age, yet
the average gamer is not an adolescent, but an adult (between 30 and 35 years old). The
frequency of gaming even increases with age (Lenhart, Jones, & Macgill, 2008). It would be
wrong to assume that the potential effects of video games are therefore an issue in adolescents
only. Similarly, it has been hypothesized that sleep quality and duration are progressively
declining because our daytime schedules have become more crammed (Bixler, 2009). In
comparison with children and teens, adults are juggling far more responsibilities, putting more
pressure on their sleep. For instance, most research has looked at entertainment and leisure
media. Work-related late night media use (such as e-mail) has not yet been studied in relation
to sleep. Finally, adults are responsible for their own sleep schedule, while children’s sleep is
often being watched over by their parents. Given these arguments and the fact that sleep
varies highly depending on age, findings from young samples should not be extrapolated to
adults (or vice versa) without further research.
Most research is conducted in normative samples. There is merit in conducting
research in clinical samples or in case-control studies that allow an accurate comparison of the
functionalities of media use between normative and clinical samples. Media use, selection,

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and motives can be entirely different for those coping with a sleep problem. We have argued
earlier in this paper that people often believe that media can be used as a sleep aid, and Mood
Management Theory (Zillmann, 1988) supports the assertion that engaging in media may aid
to recover from aversive mood, stress or strain. The use of media and their functionalities in
clinical samples is an interesting avenue for future research.
Clinical Relevance
While the research on media and sleep has produced mostly significant findings, the
question that often remains unanswered is whether and to what extent these findings are
clinically relevant. For example, one study found that each additional hour of video gaming
significantly delayed bedtime by 6.9 minutes and rise time by 13.8 minutes. While video
gaming was related to more daytime fatigue, but it was not clear whether this delay also
coincided with other noticeable health impairment (Exelmans & Van den Bulck, 2014). There
are some starting points in the literature, nonetheless. King et al. (2013) found that prolonged
violent video gaming (150 min) led to a 7% decrease in adolescents’ sleep efficiency score, a
reduction that categorized these gamers below the established cut-off score (85%) used to
identify sleep disorders such as insomnia. Oka et al. (2008) found that those who played
video gaming or used the internet before bedtime slept two hours longer on weekend nights
than on weeknights, , a discrepancy designated as clinically significant by the American
Academy of Sleep Medicine (2005). To date, experimental research that determines whether
reductions in media use or other evidence-based interventions can clinically improve sleep is
extremely rare.
Explore Causality Issues
Most studies on media and sleep have relied on cross-sectional data. Some scholars
therefore wonder whether the relationship between media and sleep might also be reversed
(sleep difficulties lead to more media use) or even be bidirectional (i.e. media use has a

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negative effect on sleep, which is associated with increased media use). Results from
longitudinal studies are mixed. Johnson, Cohen, Kasen, First, and Brook (2004) indicated that
television viewing (>3h per day) during adolescence was associated with a significantly
higher risk of having sleep problems during early adulthood and a two-wave study by
Nuutinen, Ray, and Roos (2013) found that computer use, television viewing and the presence
of media in the bedroom reduced sleep duration in children. A 3-year longitudinal study by
Tavernier & Willoughby (2014) among university students, however, found that media use
was an outcome of sleep problems instead of the reverse. They explained these unexpected
effects by hypothesizing that the relationship between media use and sleep quality evolves
across the life span. The results of Johnson et al. (2004) suggested a positive effect of reduced
media use at age 14 on reduced sleep problems at age 16, but no effect of reduced media use
at age 16 on reduced sleep problems at age 22. Research on the use of media as sleep aid
emphasized the necessity of a longitudinal design to ascertain whether those who use
electronic media to aid sleep may be even more tired if they did not do so. In sum, more
longitudinal studies are needed to examine the temporal or cyclical relationship between
media and sleep (Hysing, Stormark, Jakobsen, & Lundervold, 2015).
Conclusion
Sleep and media use both compete for a similar slice of our time. More and more, one cannot
increase without limiting the time available for the other. The growing availability, portability,
and even wearability of today’s “old” and “new” media exacerbate the issues. At the
crossroads where media uses and effects research and sleep medicine meet, fascinating new
insights await for both disciplines.

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References
Aeschbach, D., Sher, L., Postolache, T. T., Matthews, J. R., Jackson, M. A., & Wehr, T. A.
(2003). A longer biological night in long sleepers than in short sleepers. Journal of
Clinical Endocrinology and Metabolism, 88, 26–30. https://doi.org/10.1210/jc.2002-
020827
American Academy of Sleep Medicine. (2005). The international classification of sleep
disorders diagnostic and coding manual. Westchester, IL: American
Ancoli-Israel, S., Cole, R., Alessi, C., Chambers, M., Moorcroft, W., & Pollak, C. P. (2003).
The role of actigraphy in the study of sleep and circadian rhythms. Sleep, 26, 342–
392.
Arora, T., Broglia, E., Thomas, G. N., & Taheri, S. (2014). Associations between specific
technologies and adolescent sleep quantity, sleep quality, and parasomnias. Sleep
Medicine, 15, 240–247. https://doi.org/10.1016/j.sleep.2013.08.799
Bartel, K. A., Gradisar, M., & Williamson, P. (2015). Protective and risk factors for adolescent
sleep: A meta-analytic review. Sleep Medicine Reviews, 21, 72–85.
https://doi.org/10.1016/j.smrv.2014.08.002
Behar, J., Roebuck, A., Domingos, J. S., Gederi, E., & Clifford, G. D. (2013). A review of
current sleeps screening applications for smartphones. Physiological Measurement, 34,
R29-46. https://doi.org/10.1088/0967-3334/34/7/R29
Berson, D. M., Dunn, F. A., & Takao, M. (2002). Phototransduction by retinal ganglion cells
that set the circadian clock. Science, 295, 1070–1073.
https://doi.org/10.1126/science.1067262
Bixler, E. (2009). Sleep and society: An epidemiological perspective. Sleep Medicine, 10, S3-
6. https://doi.org/10.1016/j.sleep.2009.07.005

A PRIMER FOR MEDIA SCHOLARS 24
Bovill, M., & Livingstone, S. M. (2001). Bedroom culture and the privatization of media use.
In M. Bovill, & S.M. Livingstone (Eds). Children and Their Changing Media
Environment: A European Comparative Study, (pp.179–200). Mahwah, NJ: Lawrence
Erlbaum Associates.
Brown, J. D., L’Engle, K. L., Pardun, C. J., Guo, G., Kenneavy, K., & Jackson, C. (2006).
Sexy media matter: Exposure to sexual content in music, movies, television, and
magazines predicts black and white adolescents’ sexual behavior. Pediatrics, 117,
1018–1027. https://doi.org/10.1542/peds.2005-1406
Buysse, D. J., Reynolds, C., & Monk, T. (1989). The Pittsburgh Sleep Quality Index: A new
instrument for psychiatric practice and research. Psychiatry Research, 28, 193–213.
https://doi.org/http://dx.doi.org/10.1016/0165-1781(89)90047-4
Cain, N., & Gradisar, M. (2010). Electronic media use and sleep in school-aged children and
adolescents: A review. Sleep Medicine, 11, 735–742.
https://doi.org/10.1016/j.sleep.2010.02.006
Cajochen, C., Frey, S., Anders, D., Späti, J., Bues, M., Pross, A., … Stefani, O. (2011).
Evening exposure to a light emitting diodes (LED)-backlit computer screen affects
circadian physiology and cognitive performance. Journal of Applied Physiology, 110,
1432–1438. https://doi.org/10.1152/japplphysiol.00165.2011
Calamaro, C. J., Mason, T. B. A., & Ratcliffe, S. J. (2009). Adolescents living the 24/7
lifestyle: Effects of caffeine and technology on sleep duration and daytime
functioning. Pediatrics, 123, e1005-10. https://doi.org/10.1542/peds.2008-3641
Cappuccio, F. P., & Miller, M. A. (2011). Is prolonged lack of sleep associated with obesity?
BMJ, 10–12. https://doi.org/10.1136/bmj.d3306

A PRIMER FOR MEDIA SCHOLARS 25
Carpenter, J. S., & Andrykowski, M. A. (1998). Psychometric evaluation of the Pittsburgh
Sleep Quality Index. Journal of Psychosomatic Research, 45, 5–13.
https://doi.org/10.1016/S0022-3999(97)00298-5
Carskadon, M. A. (1999). When worlds collide: Adolescent need for sleep versus societal
demands. Phi Delta Kappan, 80, 348–353.
Carskadon, M. A. (2011). Sleep in adolescents: The perfect storm. Pediatric Clinics of North
America, 58, 637–647. https://doi.org/10.1038/jid.2014.371
Chang, A., Aeschbach, D., Duffy, J. F., & Czeisler, C. A. (2015). Evening use of light-emitting
eReaders negatively affects sleep, circadian timing , and next-morning alertness.
Proceedings of the National Academy of Sciences, 112, 1232–1237.
https://doi.org/10.1073/pnas.1418490112
Christakis, D. A., Ebel, B. E., Rivara, F. P., & Zimmerman, F. J. (2004). Television, video, and
computer game usage in children under 11 years of age. The Journal of Pediatrics,
145, 652–656. https://doi.org/10.1016/j.jpeds.2004.06.078
Custers, K., & Van den Bulck, J. (2012). Television viewing, internet use, and self-reported
bedtime and rise time in adults: Implications for sleep hygiene recommendations from
an exploratory cross-sectional study. Behavioral Sleep Medicine, 10, 96–105.
https://doi.org/10.1080/15402002.2011.596599
Dahl, R. E., & Lewin, D. S. (2002). Pathways to adolescent health: Sleep regulation and
behavior. The Journal of Adolescent Health, 31, 175–84. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/12470913
Devine, E. B., Hakim, Z., & Green, J. (2005). A systematic review of patient-reported
outcome instruments measuring sleep dysfunction in adults. PharmacoEconomics, 23,
889–912. https://doi.org/10.2165/00019053-200523090-00003

A PRIMER FOR MEDIA SCHOLARS 26
Dworak, M., Schierl, T., Bruns, T., & Strüder, H. K. (2007). Impact of singular excessive
computer game and television exposure on sleep patterns and memory performance of
school-aged children. Pediatrics, 120, 978–85. https://doi.org/10.1542/peds.2007-0476
Eggermont, S., & Van den Bulck, J. (2006). Nodding off or switching off? The use of popular
media as a sleep aid in secondary-school children. Journal of Paediatrics and Child
Health, 42, 428–33. https://doi.org/10.1111/j.1440-1754.2006.00892.x
Exelmans, L., & Van den Bulck, J. (2014). Sleep quality is negatively related to video gaming
volume in adults. Journal of Sleep Research, 24, 189–196.
https://doi.org/10.1111/jsr.12255
Exelmans, L., & Van den Bulck, J. (2015). Technology and sleep: How electronic media
exposure has impacted core concepts of sleep medicine. Behavioral Sleep Medicine,
13, 439–441. https://doi.org/10.1080/15402002.2015.1083025
Exelmans, L., & Van den Bulck, J. (2016a). Bedtime mobile phone use and sleep in adults.
Social Science & Medicine, 148, 93–101.
https://doi.org/10.1016/j.socscimed.2015.11.037
Exelmans, L., & Van den Bulck, J. (2016b). The use of media as a sleep aid in adults.
Behavioral Sleep Medicine, 14, 121–133.
https://doi.org/10.1080/15402002.2014.963582
Exelmans, L., & Van den Bulck, J. (2017a). Bedtime, shuteye time and electronic media:
Sleep displacement is a two-step process. Journal of Sleep Research, 26, 363-370.
https://doi.org/10.1111/jsr.12510
Exelmans, L., & Van den Bulck, J. (in press). Binge viewing, sleep and the role of pre-sleep
arousal. Journal of Clinical Sleep Medicine.

A PRIMER FOR MEDIA SCHOLARS 27
Exelmans, L., & Van den Bulck, J. (2017b). “Glued to the tube”: The interplay between self-
control, evening television viewing, and bedtime procrastination. Communication
Research. https://doi.org/10.1177/0093650216686877
Ferrara, M., & De Gennaro, L. (2001). How much sleep do we need? Sleep Medicine
Reviews, 5, 155–179. https://doi.org/10.1053/smrv.2000.0138
Garrison, M. M., & Christakis, D. A. (2012). The impact of a healthy media use intervention
on sleep in preschool children. Pediatrics, 130, 492–9.
https://doi.org/10.1542/peds.2011-3153
Garrison, M. M., Liekweg, K., & Christakis, D. A. (2011). Media use and child sleep: The
impact of content, timing, and environment. Pediatrics, 128, 29–35.
https://doi.org/10.1542/peds.2010-3304
Gillette, M. U., & Abbott, S. M. (2005). Basic mechanisms of circadian rhythms and their
relation to the sleep/wake cycle. In D. P. Cardinali & S. R. Pandi-Perumal (Eds.),
Neuroendocrine Correlates of Sleep/Wakefulness (pp. 19–40). Springer US.
Gooneratne, N. S., Tavaria, A., Patel, N., Madhusudan, L., Nadaraja, D., Onen, F., &
Richards, K. C. (2011). Perceived effectiveness of diverse sleep treatments in older
adults. Journal of the American Geriatrics Society, 59, 297–303.
https://doi.org/10.1111/j.1532-5415.2010.03247.x
Gradisar, M., Ph, D., Lack, L., Richards, H., Hon, B. P., Harris, J., & Gallasch, J. (2007). The
Flinders Fatigue Scale: Preliminary psychometric properties and clinical sensitivity of
a new scale for measuring daytime fatigue associated with insomnia. Journal of
Clinical Sleep Medicine, 3, 722–728.
Gradisar, M., Wolfson, A. R., Harvey, A. G., Hale, L., Rosenberg, R., & Czeisler, C. A.
(2013). The sleep and technology use of Americans: Findings from the National Sleep

A PRIMER FOR MEDIA SCHOLARS 28
Foundation’s 2011 Sleep in America Poll. Journal of Clinical Sleep Medicine, 9,
1291–1299. https://doi.org/10.5664/jcsm.3272
Grandner, M. A., Hale, L., Moore, M., & Patel, N. P. (2011). Mortality associated with short
sleep duration: The evidence, the possible mechanism and the future. Sleep Medicine
Reviews, 14, 191–203. https://doi.org/10.1016/j.smrv.2009.07.006.Mortality
Hale, L., & Guan, S. (2015). Screen time and sleep among school-aged children and
adolescents: A systematic literature review. Sleep Medicine Reviews, 21, 50–58.
https://doi.org/10.1016/j.smrv.2014.07.007
Harmat, L., Takacs, J., & Bodizs, R. (2008). Music improves sleep quality in students.
Journal of Advanced Nursing, 62, 327–336. https://doi.org/10.1111/j.1365-
2648.2008.04602.x
Harrison, K., & Cantor, J. (1999). Tales from the screen: Enduring fright reactions to scary
media. Media Psychology, 1, 97–116. https://doi.org/10.1207/s1532785xmep0102_1
Harvey, A. G., & Tang, N. (2013). (Mis)perception of sleep in insomnia: A puzzle and a
resolution. Psychological Bulletin, 138, 77–101. https://doi.org/10.1037/a0025730.
(Mis)Perception
Hassan, E. (2005). Recall bias can be a threat to retrospective and prospective research
designs. The Internet Journal of Epidemiology, 3, 1–11. https://doi.org/10.5580/2732
Huesmann, L. R., & Taylor, L. D. (2006). The role of media violence in violent behavior.
Annual Review of Public Health, 27, 393–415.
https://doi.org/10.1146/annurev.publhealth.26.021304.144640
Hume, K. I., Van, F., & Watson, A. (1998). A field study of age and gender differences in
habitual adult sleep. Journal of Sleep Research, 7, 85–94.

A PRIMER FOR MEDIA SCHOLARS 29
Hysing, M., Pallesen, S., Stormark, K. M., Lundervold, A. J., & Sivertsen, B. (2013). Sleep
patterns and insomnia among adolescents: A population-based study. Journal of Sleep
Research, 22, 549–556. https://doi.org/10.1111/jsr.12055
Hysing, M., Stormark, K. M., Jakobsen, R., & Lundervold, A. J. (2015). Sleep and use of
electronic devices in adolescence: Results from a large population-based study. BMJ
Open, 5, 1–8. https://doi.org/10.1136/bmjopen-2014-006748
Institute of Medicine. Sleep Disorders and Sleep Deprivation: An Unmet Public Health
Problem. Washington, DC: The National Academies Press; 2006.
Jenner, M. (2014). Is this TVIV? On Netflix, TVIII and binge-watching. New Media &
Society, , 1–17. https://doi.org/10.1177/1461444814541523
Jenner, M. (2015). Binge-watching: Video-on-demand, quality TV and mainstreaming
fandom. International Journal of Cultural Studies, 20, 304-320.
https://doi.org/10.1177/1367877915606485
Johns, M. (1991). A new method for measuring daytime sleepiness: the Epworth Sleepiness
Scale. Sleep, 14, 540–545. Retrieved from http://epworthsleepinessscale.com/wp-
content/uploads/2008/12/a-new-method-for-measuring-daytime-sleepiness-the-
epworth-sleepiness-scale2.pdf
Johnson, J. G., Cohen, P., Kasen, S., First, M. B., & Brook, J. S. (2004). Association between
television viewing and sleep problems during adolescence and early adulthood.
Archives of Pediatrics & Adolescent Medicine, 158, 562–568.
https://doi.org/10.1001/archpedi.158.6.562
King, D. L., Gradisar, M., Drummond, A., Lovato, N., Wessel, J., Micic, G., … Delfabbro, P.
(2013). The impact of prolonged violent video-gaming on adolescent sleep: An
experimental study. Journal of Sleep Research, 22, 137–43.
https://doi.org/10.1111/j.1365-2869.2012.01060.x

A PRIMER FOR MEDIA SCHOLARS 30
Kubey, R. (1986). Television use in everyday life: Coping with unstructured time. Journal of
Communication, 36, 108–123. https://doi.org/10.1111/j.1460-2466.1986.tb01441.x
Kubiszewski, V., Fontaine, R., Rusch, E., & Hazouard, E. (2013). Association between
electronic media use and sleep habits: An eight-day follow-up study. International
Journal of Adolescence and Youth, 19, 395–407.
https://doi.org/10.1080/02673843.2012.751039
Lee, D. R. (2016). Teaching the world to sleep. Psychological and behavioural assessment
and treatment strategies for people with sleeping problems and insomnia. London:
Karnac Books.
Lemola, S., Perkinson-Gloor, N., Brand, S., Dewald-Kaufmann, J. F., & Grob, A. (2015).
Adolescents’ electronic media use at night, sleep disturbance, and depressive
symptoms in the smartphone age. Journal of Youth and Adolescence, 44, 405–418.
https://doi.org/10.1007/s10964-014-0176-x
Lenhart, A., Jones, S., & Macgill, A. R. (2008). Adults and video games. Pew internet project
data memo.
Li, S., Jin, X., Wu, S., Jiang, F., Yan, C., & Shen, X. (2007). The impact of media use on sleep
patterns and sleep disorders among school-aged children in China. Sleep, 30, 361–367.
Luyster, F. S., Strollo, P. J., Zee, P. C., & Walsh, J. K. (2012). Sleep: A health imperative.
Sleep, 35, 727–34. https://doi.org/10.5665/sleep.1846
Markov, D., Goldman, M., & Doghramji, K. (2012). Normal sleep and circadian rhythms.
Sleep Medicine Clinics, 7, 417–426. https://doi.org/10.1016/j.jsmc.2012.06.015
Matricciani, L. (2013). Subjective reports of children’s sleep duration: Does the question
matter? A literature review. Sleep Medicine, 14, 303–311.
https://doi.org/10.1016/j.sleep.2013.01.002

A PRIMER FOR MEDIA SCHOLARS 31
Matricciani, L., Olds, T., & Petkov, J. (2012). In search of lost sleep: Secular trends in the
sleep time of school-aged children and adolescents. Sleep Medicine Reviews, 16, 203–
11. https://doi.org/10.1016/j.smrv.2011.03.005
Matrix, S. (2014). The Netflix effect: Teens, binge watching, and on-demand digital media
trend. Jeunesse: Your People, Texts, Cultures, 6.
Monk, T. H., Buysse, D. J., Kennedy, K. S., Pods, J. M., DeGrazia, J. M., & Miewald, J. M.
(2003). Measuring sleep habits without using a diary: The Sleep Timing
Questionnaire. Sleep, 26, 208–12. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/12683481
National Sleep Foundation. (2014). 2014 Sleep in America Poll: Sleep in the modern family.
Washington, DC.
Nuutinen, T., Ray, C., & Roos, E. (2013). Do computer use, TV viewing, and the presence of
the media in the bedroom predict school-aged children’s sleep habits in a longitudinal
study? BMC Public Health, 13, 684. https://doi.org/10.1186/1471-2458-13-684
Oka, Y., Suzuki, S., & Inoue, Y. (2008). Bedtime activities, sleep environment, and
sleep/wake patterns of Japanese elementary school children. Behavioral Sleep
Medicine, 6, 220–233. https://doi.org/10.1080/15402000802371338
Owens, J. A. (2004). Sleep in children: Cross-cultural perspectives. Sleep and Biological
Rhythms, 2, 165–173. https://doi.org/10.1111/j.1479-8425.2004.00147.x
Paavonen, E. J., Pennonen, M., Roine, M., Valkonen, S., & Lahikainen, A. R. (2006). TV
exposure associated with sleep disturbances in 5 - to 6-year-old children. Journal of
Sleep Research, 15, 154–61. https://doi.org/10.1111/j.1365-2869.2006.00525.x
Pallesen, S., Hetland, J., Sivertsen, B., Samdal, O., Torsheim, T., & Nordhus, I. H. (2008).
Time trends in sleep-onset difficulties among Norwegian adolescents: 1983--2005.

A PRIMER FOR MEDIA SCHOLARS 32
Scandinavian Journal of Public Health, 36, 889–895.
https://doi.org/10.1177/1403494808095953
Pilcher, J. J., Ginter, D. R., & Sadowsky, B. (1997). Sleep quality versus sleep quantity:
Relationships between sleep and measures of health, well-being and sleepiness in
college students. Journal of Psychosomatic Research, 42, 583–96.
Rideout, V., & Hamel, E. (2006). The media family: Electronic media in the lives of infants,
toddlers, preschoolers and their parents. Menlo Park, CA: Kaiser Family Foundation.
Rideout, V. (2015). The common sense census: Media use by tweens and teens. San
Francisco, CA: Common Sense Media. Retrieved from https://www.
commonsensemedia.org/research/the-common-sense-census-media-use-bytweens-and-
teens
Roenneberg, T., Wirz-Justice, A., & Merrow, M. (2003). Life between clocks: daily temporal
patterns of human chronotypes. Journal of Biological Rhythms, 18, 80–90.
https://doi.org/10.1177/0748730402239679
Sadeh, A. (2011). The role and validity of actigraphy in sleep medicine: An update. Sleep
Medicine Reviews, 15, 259–267. https://doi.org/10.1016/j.smrv.2010.10.001
Schoenborn, C.A., Adams, P.F. (2010) Health behaviors of adults: United States, 2005–2007.
National Center for Health Statistics. Retrieved from
https://www.cdc.gov/nchs/data/series/sr_10/sr10_245.pdf
Short, M. A., Gradisar, M., Lack, L. C., Wright, H., & Carskadon, M. A. (2012). The
discrepancy between actigraphic and sleep diary measures of sleep in adolescents.
Sleep Medicine, 13, 378–384. https://doi.org/10.1016/j.sleep.2011.11.005
Smith, L. J., Gradisar, M., King, D. L., & Short, M. (2017). Intrinsic and extrinsic predictors
of video gaming behavior and adolescent bedtimes : The influence of flow states, self-

A PRIMER FOR MEDIA SCHOLARS 33
perceived risk-taking, device accessibility, parental-regulation of media and bedtime.
Sleep Medicine, 30, 64-70. http://dx.doi.org/10.1016/j.sleep.2016.01.009
Spruyt, K., & Gozal, D. (2011). Pediatric sleep questionnaires as diagnostic or
epidemiological tools: A review of currently available instruments. Sleep Medicine
Reviews, 15, 19–32. https://doi.org/10.1016/j.smrv.2010.07.005
Strine, T. W., & Chapman, D. P. (2005). Associations of frequent sleep insufficiency with
health-related quality of life and health behaviors. Sleep Medicine, 6, 23–27.
https://doi.org/10.1016/j.sleep.2004.06.003
Taillard, J., Philip, P., & Bioulac, B. (1999). Morningness/eveningness and the need for sleep.
Journal of Sleep Research, 8, 291–295. https://doi.org/10.1046/j.1365-
2869.1999.00176.x
Tavernier, R., & Willoughby, T. (2014). Sleep problems: Predictor or outcome of media use
among emerging adults at university? Journal of Sleep Research, 23, 389–396.
https://doi.org/10.1111/jsr.12132
Van den Bulck, J. (2000). Is television bad for your health? Behavior and body image of the
adolescent “couch potato.” Journal of Youth and Adolescence, 29, 273–288.
https://doi.org/10.1023/A:1005102523848
Van den Bulck, J. (2003). Text messaging as a cause of sleep interruption in adolescents,
evidence from a cross-sectional study. Journal of Sleep Research, 12, 263–263.
Van den Bulck, J. (2004a). Media use and dreaming: The relationship among television
viewing, computer game play, and nightmares or pleasant dreams. Dreaming, 14, 43–
49. https://doi.org/10.1037/1053-0797.14.1.43
Van den Bulck, J. (2004b). Television viewing, computer game playing, and internet use and
self-reported time to bed and time out of bed in secondary-school children. Sleep, 27,
101–104.

A PRIMER FOR MEDIA SCHOLARS 34
Van den Bulck, J. (2014). Sleep apps and the quantified self: Blessing or curse? Journal of
Sleep Research, 1–3. https://doi.org/10.1111/jsr.12270
Van den Bulck, J., Çetin, Y., Terzi, Ö., & Bushman, B. J. (2016). Violence, sex, and dreams:
Violent and sexual media content infiltrate our dreams at night. Dreaming, 26, 271–
279. https://doi.org/10.1037/drm0000036
Van Dongen, H. P. A., Maislin, G., Mullington, J. M., & Dinges, D. F. (2003). The Cumulative
cost of additional wakefulness: dose-response effects on neurobehavioral functions
and sleep physiology from chronic sleep restriction and total sleep deprivation. Sleep,
26, 117–126. https://doi.org/10.1001/archsurg.2011.121
Wolfson, A. R., & Carskadon, M. A. (1998). Sleep schedules and daytime functioning in
adolescents. Child Development, 69, 875–887.
Wolfson, A. R., Carskadon, M. A., Acebo, C., Seifer, R., Fallone, G., Labyak, S. E., & Martin,
J. L. (2003). Evidence for the validity of a Sleep Habits Survey for adolescents. Sleep,
26, 213–6.
Wood, B., Rea, M. S., Plitnick, B., & Figueiro, M. G. (2012). Light level and duration of
exposure determine the impact of self-luminous tablets on melatonin suppression.
Applied Ergonomics, 44, 237–40. https://doi.org/10.1016/j.apergo.2012.07.008
Woods, H. C., & Scott, H. (2016). #Sleepyteens: Social media use in adolescence is
associated with poor sleep quality, anxiety, depression and low self-esteem. Journal of
Adolescence, 51, 41–49. https://doi.org/10.1016/j.adolescence.2016.05.008
Zillmann, D. (1988). Mood management through communication choices. American
Behavioral Scientist, 31, 327–340. https://doi.org/10.1177/000276488031003005
Zimmerman, F. (2008). Children’s media use and sleep problems: Issues and unanswered
questions. Research brief. Menlo Park, CA: Kaiser Family Foundation.

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Figure 1
A. Traditional Sleep Displacement Model
B. E
xe
l
mans & Van den Bulck (2017a) Sleep Displacement Model