Running head: A PRIMER FOR MEDIA SCHOLARS
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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. In total, we spend 45-55%
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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 environment, a
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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 some
notable disadvantages. It has an oversensitivity of scoring nocturnal movements as
wakefulness, thereby deflating the estimate of total sleep (Short, Gradisar, Lack, Wright, &
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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 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.
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Comparison studies with diary data and objective sleep measures have shown that selfreports 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 sleepmisperception (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 parameters of sleep, including
bedtime, sleep latency, rise time, night awakenings, sleep quality, and to separate weekdays
from weekend days (Cain & Gradisar, 2010).
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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 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,
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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 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.
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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.
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
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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 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.
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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 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,
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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,
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
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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 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 mediamultitasking 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
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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, 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
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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.
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.
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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, 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
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21
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 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
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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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Figure 1
A. Traditional Sleep Displacement Model
B. Exelmans & Van den Bulck (2017a) Sleep Displacement Model
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