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All authors have seen and approved the manuscript. The manuscript does not report on a clinical trial.
All authors declare absence of financial support. Off-label or investigational use not applicable. The
authors have no conflict of interest to disclose.
Article published in Journal of Clinical Sleep Medicine.
Please cite as:
Exelmans, L., & Van den Bulck, J. (2017). Binge Viewing, Sleep, and the Role of Pre-Sleep
Arousal. Journal of Clinical Sleep Medicine, 13(8), 1001–1008.
http://dx.doi.org/10.5664/jcsm.6704
Publisher’s version available at: http://www.aasmnet.org/JCSM/ViewAbstract.aspx?pid=31062
Binge Viewing, Sleep, and the Role of Pre-Sleep Arousal
Liese Exelmansa| M.A., liese.exelmans@soc.kuleuven.be
Jan Van den Bulckb| Ph.D., jvdbulck@umich.edu
aLeuven School for Mass Communication Research, KU Leuven, Leuven, Belgium (institution where
the work was performed)
bDepartment of Communication Studies, University of Michigan, USA
Liese Exelmans, MA (corresponding author)
Leuven School for Mass Communication Research, KU Leuven, Parkstraat 45 (PO box 3603),
B- 3000 Leuven (Belgium)
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E-mail: liese.exelmans@kuleuven.be | Telephone: +32 16 32 32 31 | Fax: +32 16 32 33 12
Abstract
Study Objectives To investigate the prevalence of binge viewing, its association with sleep and
examine arousal as an underlying mechanism of this association.
Methods 423 emerging adults (aged 18-25 years old, 61.9% female) completed an online survey
assessing regular television viewing, binge viewing, sleep quality (Pittsburgh Sleep Quality Index),
fatigue (Fatigue Assessment Scale), insomnia (Bergen Insomnia Scale), and pre-sleep arousal (Pre-
Sleep Arousal Scale). Regression analyses were conducted. Mediation analysis was performed using
PROCESS Macro.
Results 80.6% identified themselves as a binge viewer. Among those who binge viewed (N = 341),
20.2% had binge viewed at least a few times a week during the past month. Among poor sleepers
(PSQI >5), 32.6% had a poor sleep quality associated with being a binge viewer. Higher binge
viewing frequency was associated with a poorer sleep quality, increased fatigue and more symptoms
of insomnia, while regular television viewing was not. Cognitive pre-sleep arousal fully mediated
these relationships.
Conclusions New viewing styles such as binge viewing are increasingly prevalent and may pose a
threat to sleep. Increased cognitive arousal functions as the mechanism explaining these effects.
Measures of media exposure should take into account the user’s level of engagement with media.
Interventions aimed at (1) alerting viewers about excessive viewing duration and (2) reducing arousal
before sleep may be useful ways to tackle sleep problems in binge viewers.
Keywords binge viewing, sleep quality, PSQI, fatigue, insomnia, arousal
Brief Summary
Whether regular TV viewing has much impact on sleep is debated. New viewing patterns, such as
binge viewing, in which consumers watch an excessive amount of TV in one sitting, have, however,
not been studied. There is also a lack of understanding of the underlying mechanism of the association
between technology use and sleep. The current study shows binge viewing is prevalent among young
adults and is the first to demonstrate a link with poorer sleep quality, more fatigue, and increased
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insomnia. Importantly, the mechanism explaining this relationship appears to be increased cognitive
arousal, resulting from binge viewing. While this has been explained by viewers’ higher level of
engagement with the story, future research should verify this hypothesis.
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Introduction
The way we watch television has dramatically changed over the past decades. New
technologies such as digital recorders and streaming services have ended the era of appointment
viewing: 63% of American households use a streaming service,1 and the use of digital video recorders
and the Internet increased the amount of viewing in college students. 2 The unprecedented access to
television content has also introduced a new viewing style: binge viewing, defined as watching
multiple episodes of the same series in one sitting.3 Statistics indicate that binge viewing is on the rise:
3 out of 4 respondents in the study by Sung, Kang & Lee self-identified as a binge viewer,4 and
research showed that 70% of television viewers between 13 and 49 years old binge viewed at least
sometimes.5 In general, viewers are increasingly watching television in larger doses at a time of their
choosing.
The term binge viewing hints at an overindulgence or addiction regarding television viewing,
and concerns have been raised over its harmful effects. Prior research has indicated that media
bingeing was associated with more anxiety, depression, and fatigue. 6,7 Binge viewers also reported
higher levels of loneliness and depression.4 While some researchers worry that binge viewing may
lead to a reduction in social skills in the long term,6,8 binge viewers also reported the behavior has a
social value: the ability to participate in conversations about a show with friends creates a sense of
belonging.5, 9,10
Despite the growing popularity of binge viewing, the phenomenon has received little
scholarly attention so far. In sleep research, it is recognized that screen exposure negatively affects
sleep. For television viewing, findings have been inconsistent: a review study indicated that 32 out of
42 studies that examined television viewing and sleep outcomes found significant negative
associations.11 Conversely, Bartel and colleagues found television viewing not to be a significant risk
factor for sleep.12 The majority of the research on this topic focuses on regular viewing volume. To
our knowledge, this study is the first to investigate the association between binge viewing and sleep.
Dworak and colleagues posited that excessive media consumption would negatively impact
sleep,13 and research found that watching television for >2 hours per day increased sleep onset delay
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among children.14 Considering that binge viewing signifies an intense or more extreme viewing habit,
we expect that binge watching will negatively affect sleep outcomes.
Addressing the call for more research into the underlying mechanisms explaining the
relationship between media use and sleep,15,16 this study will explore arousal as an explanatory factor
(i.e., mediator) of the aforementioned relationship. In addition to displacing sleep and affecting
melatonin output, screen exposure is presumed to affect sleep through its impact on arousal.15 The
existing research has found that playing videogames increased activity in the central and autonomic
nervous system, which in turn prolonged sleep onset.13, 17,18 One recent study reported that cognitive
pre-sleep arousal mediated the relationship between social media use and sleep onset latency.19
Although it has been argued that these results can be extrapolated to other media platforms,14,20
evidence of this hypothesis is scarce.
The limited literature on binge viewing provides some indications to propose arousal as a
mediator of the relationship with sleep. Television shows that are binge viewed are characterized by a
complex narrative structure and intense character development. Binge viewers become strongly
immersed into the story, identify with the characters and experience increased difficulty to stop
viewing.10, 21,22 In other words, because of the higher emotional and cognitive involvement during
binge viewing, we expect that binge viewing will affect sleep through its impact on arousal. In sum,
we formulate two research questions:
RQ1: Does binge viewing affect sleep (i.e., sleep quality, fatigue, insomnia)?
RQ2: Is arousal a significant mediator of the relationship between binge viewing and sleep?
Methods
Sample and Procedure
Arnett identified “emerging adults”, the group of 18 to 25 year olds that are between
adolescence and adulthood, as a group particularly sensitive to risk behaviors. 23 As young adults are
also often considered to be the most avid binge viewers,5 18-25 years olds were invited to participate
in an online survey in February 2016. A call for participation was disseminated via Facebook postings
which highlighted the topic of the research project and the voluntary nature of participation. The study
was presented as a study on young people’s leisure time and well-being to blind the relationships we
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were studying. Anonymous participation was guaranteed. The study was conducted in accordance
with the ethics requirements of [institution blinded], and informed consent was obtained from all
respondents.
A total of 463 questionnaires were completed. Respondents who reported that they had a
clinical history of sleep problems (i.e., who indicated they had consulted a doctor regarding sleep
difficulties, N = 40) were dropped from the analyses, resulting in a final sample of 423 respondents.
There was a higher proportion of women (61.9%), and 74.2% of respondents were students, 23.0%
was working, and 2.8% was unemployed. Respondents’ average age was 22.17 (SD = 1.86).
Measures
Sleep Quality. The Pittsburgh Sleep Quality Index (PSQI)24 is a 19-item self-report measure
that assesses sleep quality over the past month. The index consists of 7 component scores (sleep
duration, subjective sleep quality, sleep efficiency, sleep latency, sleep disturbances, daytime
dysfunction, use of sleep medication) ranging between 0 and 3, with a higher score indicating more
problems in that component. An overall sleep quality score is computed, and respondents scoring >5
are categorized as poor sleepers. The index showed acceptable internal consistency (α = .60).
Insomnia. The Bergen Insomnia Scale (BIS)25 comprises six items measuring how frequently
respondents experienced different symptoms of insomnia during the past month (0 = 0 days per week
over the last month, 7 = every day over de last month). A total score was computed, ranging between
0 and 42. Cronbach’s alpha (α) for the BIS was .76.
Fatigue. We used the Fatigue Assessment Scale (FAS),26 consisting of 10 items that describe
symptoms of daytime fatigue experienced during the past month, rated on a 5-point scale (1 = never, 5
= always). The total scores ranges between 0 and 50 and the scale showed good internal consistency
(α = .87)
Pre-Sleep Arousal. We assessed pre-sleep arousal using the Pre-Sleep Arousal Scale (PSA).27
The scale taps into somatic (e.g. heart racing, pounding, or beating irregularly) and cognitive (e.g.
being mentally alert, active) manifestations of arousal experienced when trying to fall asleep, with
eight items in each subscale. Respondents rated on a 5-point scale (1 = not at all, 5 = extremely) how
intensely they experienced each element during the last month as they attempted to fall asleep. The
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scale has been broadly used and shows satisfactory internal consistency and test-retest reliability.27-30
Validity has been demonstrated for populations with insomnia vs. good sleeper controls.27 Both
subscales (αsom = .73; αcog = .88 ) showed good internal consistency in our sample.
Binge Viewing. Appendix A shows our operationalization of binge viewing, which is based on
previous research.4 The first question was a screening question to identify binge viewers in the
sample. As there is considerable inconsistency in the number of episodes that are required before the
literature defines a session as a “binge”,31 we provided a definition of binge viewing as "watching
multiple consecutive episodes of the same TV-show in one sitting on a screen, be it a television-,
laptop-, computer- or tablet computer screen". Those who identified themselves as a binge viewer
continued to answer questions about their frequency of binge viewing during the last month (1 = once
during the past month, 5 = (almost) every day), the duration of an average binge viewing session
(hours and minutes), and the number of episodes they usually watched (2, 3-4, 5-6, more than 6).
Control variables. We incorporated gender (0 = male, 1 = female), age, status (1 = students
living on campus, 2 = students living at home, 3 = full time employment, 4 = part-time employment, 5
= unemployed), shiftwork ( 0 = no, 1 = yes), perceived physical health (“In general, would you say
your health is: 1 = poor, 2 = fair, 3 = good, 4 = very good, 5 = excellent”),32 exercise level (“how many
hours per week do your exercise to the extent of becoming out of breath?”), and bedtime television
viewing volume as control variables. For the latter, respondents reported how much (hours and
minutes) television they usually watched during the final two hours before bedtime on an average
weekday and an average weekend day. Weekly bedtime television viewing volume was computed by
multiplying weekday volume by 5 and adding it to the weekend day volume multiplied by 2.33
Analyses
SPSS for Windows (Version 22.0, Chicago, IL, USA) was used to execute all analyses.
Descriptive statistics and zero-order correlations were computed, and regression analyses (hierarchical
and logistic) were conducted. Hayes’ PROCESS computational tool with 5000 bootstrap samples was
used to test the proposed mediation hypothesis.34,35 The results are represented as bias-corrected
confidence intervals: 95% confidence intervals that do not contain zero indicate a significant indirect
or mediating effect.
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Results
Descriptive statistics and correlations are reported in Table 1. The majority of the sample
(80.6%) reported that they had binge viewed. Among those who binge viewed (N = 341), 39.6% did
this once during the month preceding the study, 28.4% a few times, 11.7% once a week, 13.5% a few
times a week and 6.7% had binge viewed almost every day during the preceding month. They spent 3
hours and 8 minutes on a binge viewing session on average (M = 3.14, SD = 1.63). Men (M = 1.01,
SD = 1.38) binge viewed less frequently than women (M = 1.46, SD = 1.91) (t(314.43) = -2.469,
p<.05), but binge viewing sessions lasted longer among men (M = 3.50, SD = 1.68) compared to
women (M = 2.93, SD = 1.57) (t (235.361) = 3.077, p<.01). Binge viewing frequency and duration
were negatively related (r = -.144, p<.01): the more frequently one had binge viewed during the past
month, the less time they spent on a binge viewing session. Finally, one in four (25.6%) watched on
average of 2 episodes per binge viewing session, one in two (52.4%) watched 3 to 4 episodes in one
sitting, 16.2% watched 5 to 6 episodes, and 5.9% watched more than six episodes in one sitting.
[TABLE 1 AROUND HERE]
The sample scored on average 5.04 (SD = 2.35) on the PSQI, which borders on the cut-off
point for having poor sleep quality. More than one in three respondents (37.4%) were categorized as a
poor sleeper. Respondents went to bed at 23:32 (SD = 1:04) and got up at 08:11 (SD = 01:28). On
average, they slept 7 hours and 37 minutes (SD = 01:02). Sleep quality was positively related to all
other sleep indicators: a poorer sleep quality was thus associated with more symptoms of fatigue,
insomnia, and pre-sleep arousal (Table 1).
Those who identified as a binge viewer reported more fatigue (Mbinge = 12.58, SDbinge = 6.30;
Mnon-binge = 10.73, SDnon-binge = 5.76 ; t(130.633) = -2.549, p < .05) and a poorer sleep quality (Mbinge =
5.14, SDbinge = 2.35; Mnon-binge = 4.60, SDnon-binge = 2.32 ; t(117.592) = -1.845, p =.068) compared to those
who had never binge viewed. Logistic regression analyses showed that those who identified as a binge
viewer had a 98% higher likelihood of having a poor sleep quality compared to those who did not
identify as a binge viewer (Exp(B) = 1.981, p<.05). Attributable risk, which is an epidemiological
indicator reflecting the difference in prevalence of a phenomenon between the exposed and the non-
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exposed group,36 was 32.6%. A PSQI score over 5 could thus be attributed to binge viewing in almost
one in three cases.
Binge viewing frequency was positively associated with all sleep indicators, whereas binge
viewing duration did not have a significant relationship with our sleep variables. Binge viewing
frequency was also positively associated with cognitive pre-sleep arousal, but there was no
relationship with somatic pre-sleep arousal (Table 1). In hierarchical regression analyses (Table 2)
control variables were added in Step 1, and binge viewing frequency was added in Step 2. Results
show that those who binge viewed more frequently reported a poorer sleep quality (β = .145, p<.01),
more daytime fatigue (β = .131, p<.05), and more symptoms of insomnia (β = .161, p<.01). Bedtime
television viewing volume was not a significant predictor of these sleep indicators.
[TABLE 2 AROUND HERE]
Mediation analyses were performed using PROCESS macro. Cognitive pre-sleep arousal was
used as a mediator, and all of the control variables were taken into account (Table 3). Results showed
that binge viewing frequency was significantly related to cognitive pre-sleep arousal (Model PSQI: β
= .133, p < .05; Model FAS: β = .122, p < .05; Model BIS: β = .135, p < .05) and that cognitive pre-
sleep arousal was strongly related to each sleep indicator (Model PSQI: β = .589, p < .001; Model
FAS: β = .462, p < .001; Model BIS: β = .565, p < .001). Cognitive pre-sleep arousal fully mediated
the relationship between binge viewing and sleep quality (effect size = .078; Boot SE = .033; CI95%
[.014; .145]), daytime fatigue (effect size = .056; Boot SE = .027; CI95% [.008; .110]), and insomnia
(effect size .077; Boot SE = .032; CI95% [.014; .137]). In other words, the more frequently
respondents binge viewed, the more cognitive pre-sleep arousal they reported, which in turn affected
their sleep quality, daytime fatigue and insomnia symptoms.
[TABLE 3 AROUND HERE]
Discussion
Consuming television content in larger bursts instead of, or in addition to, regular daily
viewing, is an increasingly common practice. Although extensive research has been carried out on the
effects of television viewing on sleep, no earlier study exists which explicitly investigates the
association between binge viewing and sleep. Moreover, there is little understanding of how media
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exposure affects sleep. The current study, therefore, examined the relationship between binge viewing
and sleep outcomes (sleep quality, fatigue, insomnia) and explored the role of arousal as an underlying
mechanism.
Approximately 80% of our participants considered themselves to be a binge viewer, and more
than one in five respondents (20.2%) had binge viewed at least a few times a week in the month
preceding the study. This prevalence of binge viewing is congruent with that found by Sung and
colleagues, who researched a similar American age group.4 Additionally, the results showed that (1) a
higher frequency of binge viewing was related to a poorer sleep quality, more fatigue, and insomnia,
and (2) that cognitive pre-sleep arousal fully mediated these relationships. Cognitive pre-sleep arousal
thus appeared to be the explanatory mechanism for the effects of binge viewing on sleep. These
results are consistent with those of Harbard and colleagues who found that cognitive pre-sleep arousal
mediated the relationship between social media use and sleep.19
Only cognitive pre-sleep arousal was a significant mediator, while somatic arousal was not. A
possible explanation might be that binge viewing leads to a stronger sense of involvement into the
narrative and identification with its characters than regular viewing does. This would also explain why
regular bedtime television viewing was not related to our sleep indicators or arousal measure. The
narrative structure that characterizes “bingeable” television shows involves a (1) larger number of (2)
more diverse storylines that (3) extend beyond one episode, and that often (4) intersect during a
season or (5) turn out to be connected with each other in the end.37,38 As such, the narrative complexity
in these shows leaves viewers thinking about episodes and their sequel after viewing them. This
prolongs sleep onset or, in other words, requires a longer period to “cool down” before going to sleep,
thus affecting sleep overall. Whether the increase in cognitive pre-sleep arousal can be attributed to
the higher degree of involvement with the content is an interesting avenue for future research.
Our results may shed a new light on the fact that the findings on the effects of television
viewing on sleep have been somewhat inconsistent,11 and a recent meta-analysis by Bartel, Gradisar,
and Williamson concluded that in comparison with other media such as video games and computer
use, television viewing is not a significant risk factor for sleep.12 Different types of television content
and different types of television viewing behavior are likely to have different effects on sleep. Our
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study suggests that intense exposure leads to cognitive arousal. It would be interesting to study
whether, for instance, relaxing media content does the opposite.
In a broader sense, we think this study signals a need to move beyond a focus on “how much”
people are using media and incorporate measures on usage styles and experience. It is interesting to
note that while television viewing has undergone a remarkable transformation, the ways of measuring
television viewing have not. One recommendation could be to incorporate people’s media habits: the
definition of “watching a lot” can vary strongly between individuals. Wagner found that among
respondents who regularly watch only a little television, binge watching ignited more guilt compared
to those who watch a lot of television habitually.31 What qualifies as a “binge” can thus vary strongly
between individuals. Accounting for the length of shows or identifying concrete cut-off scores for
binge viewing that take into account viewing history are challenges for future research. In addition,
repeated exposure to arousing media content can also lead to habituation of physiological and
emotional reactions, which is often associated with so-called “desensitization”. These differences in
how people react to media content may explain the differences in effects on sleep. For instance, King
et al. hypothesized that violent video games may elicit only minimal impact on sleep parameters
among older adolescents and adults because they have grown accustomed to these stimuli by using
them more frequently.39
Relevance and guidelines
Our exploratory, cross-sectional design and the modest size of the beta coefficients mean that
recommendations based on our conclusions have to be cautious and tentative. However, our study
signals that binge viewing is prevalent in young adults and that it may be harmful to their sleep.
Research has also shown that binge viewing signifies an overall passive or sedentary lifestyle8, which
in turn has been associated with increased health risk and sleep problems.40,41 Curiously, binge
viewing appears to be unintentional: reports indicate that 71% of binge viewing happens by accident,
when people wound up watching more than they wanted to.42 De Feijter and colleagues argued that
the first step to avoid viewing too much television is to become aware of short and long term viewing
behavior. They suggested implementing a system – such as an app - that allows viewers to determine
their optimal viewing duration, such that the viewer can engage with the content, without leading to a
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disconnect between the intended and actual viewing time. According to their research, optimal
viewing time is generally passed after five episodes.8 Some online streaming services already alert
viewers when a number of consecutive episodes have been watched.
Also, given that the relationship between binge viewing and sleep was fully mediated by pre-
sleep arousal, interventions and treatments aimed at reducing arousal before sleep (such as relaxation
techniques and mindfulness)43 can be valuable approaches to target sleep problems associated with
media use.
Strengths and Limitations
As is the case with all cross-sectional studies, we cannot determine causality, thus making the
reversed hypothesis (i.e. that poor sleep leads to increased binge viewing) also possible. However, the
observation that binge viewing appears to be unintentional behavior8, and the support found for the
temporal order of the mediation model (in which sleep outcomes were preceded by arousal, which
was predicted by binge viewing), strengthens the hypothesized direction of the effects.
We conducted this research in a sample of younger Facebook users. Even though we did not
explicitly state the focus of the study, recruitment through Facebook may have introduced self-
selection bias. This caveat, and the restricted age group of this study, hinders the generalizability of
the results. While an online survey via social networks ensures the privacy of our respondents, we had
no control over the respondents participating and increased the odds of tapping into groups of people
with similar interests. Nonetheless, social media have been found to be useful tools for exploratory
studies on a new topic, aimed to investigate young people, an age group known to be active on social
media.44 A study on personality factors compared data gathered through Facebook with (1) online data
from a large scale web survey and (2) data from an online study among university students, and
concluded that Facebook data are unlikely to exhibit systematic biases. In all, our sample may thus be
non-representative but appears to represent those young adults who consume a lot of television, i.e., a
“risk group”.45
While we used clinically validated sleep measures and constructed a binge viewing measure
with a clear definition of the concept and multiple indicators of the behavior, this study relied on self-
report measures which could have biased our results. Objective measures of sleep and arousal are
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already available, but future research would benefit from objectively measuring binge viewing too.
Experience sampling studies or partnering with streaming services are possible approaches in this
regard.
Finally, there has been much inconsistency in the definition and operationalization of binge
viewing. Binge viewing is a relatively new concept and what little literature there is, is still evolving.
We composed a measure for binge viewing based on the study of Sung and colleagues. 4 Our measure
focuses on global frequency and time estimates, which are among the most common form of
measurement in media research.46 However, Robinson & Godbey posited that these estimates can be
complex for respondents and require significant cognitive effort to answer correctly. Probing media
use within delineated time slots or aiding recall with the use of graphic formats such as timelines are
suggestions for improvement.47 In addition, because we used a formative measurement model - and
the scores on the items are thus not the result of an underlying latent construct - we cannot report
reliability or validity estimates. Assessment of internal validity and reliability is only possible for
reflective measurement models,48 because covariance between the items can be zero, positive or
negative in formative models.49 The design of a reflective measurement model for binge viewing is
therefore recommended in future research.
Conclusion
Convergence between traditional en new media has diversified television’s technology,
distribution, and use. This study provides initial evidence that modern viewing styles such as binge
viewing may negatively affect overall sleep quality, and identified cognitive pre-sleep arousal as the
explanatory mechanism. Despite television’s status as a form of “old media”, the rise of binge viewing
shows that viewers are more engaged with television content than ever. Although sleep research is
increasingly devoted to uncovering the effects of media on sleep, continued efforts are essential to
monitor the dynamic relationship between leisure time and sleep. As quoted by Mikos (2016, p.160)10:
“Television will not disappear: it will only become available on all existing screens—and so become
more present and more important.”
Disclosure Statement
14
The authors have nothing to disclose.
15
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20
Table 1 Descriptive statistics and correlations for variables of interest
1. 2. 3. 4. 5. 6. 7. 8.
1. Binge Viewing Frequency --
2. Binge Viewing Duration -.144** --
3. TV viewing (h/week) .166** .084 --
4. PSQI .148 ** .061 -.003 --
5. FAS .144** .011 -.028 .532*** --
6. BIS .154** -.003 .007 .700*** .557*** --
7. PSAcog .150** .021 .028 .563*** .479*** .590*** --
8. PSAsom .091 -.010 .007 .509*** .491*** .515*** .591*** --
M 2.190 3.137 7.578 5.039 12.219 10.802 19.710 12.884
SD 1.276 1.629 3.466 2.353 6.238 7.101 6.959 4.464
Note. PSQI = Pittsburgh Sleep Quality Index, FAS = Fatigue Assessment Scale, BIS = Bergen Insomnia Scale,
PSAcog = Cognitive Pre-Sleep Arousal, PSAsom = Somatic Pre-Sleep Arousal.
*p<.05,**p<.01, ***p<.001
21
Table 2 Hierarchical Regression Analyses Predicting Sleep Quality, Fatigue, and Insomnia
PSQI
(N = 326)
FAS
(N = 331)
BIS
(N = 336)
β SE β SE β SE
Step 1 R² .065* .100*** .039
Step 2 Gender -.037 .288 .004 .748 .017 .847
Age -.049 .081 .005 .214 -.045 .242
S1 .068 .859 .045 2.251 -.024 2.570
S2 .115 .860 .068 2.252 .070 2.571
S3 .007 1.218 -.082 3.189 .065 3.642
S4 .125 .879 -.107 2.297 .068 2.621
Shiftwork -.057 .788 .041 1.903 -.077 2.170
General Health -.224*** .167 -.267*** .435 -.143* .493
Exercise (h/week) .108 .090 -.002 .234 .076 .266
TV Viewing (h/week) -.035 .039 -.082 .101 -.061 .114
Binge Viewing Frequency .145** .101 .131* .265 .161** .300
R²/ΔR² .085/.020** .116/.016* .064/.025**
Note. PSQI = Pittsburgh Sleep Quality Index, FAS = Fatigue Assessment Scale, BIS = Bergen Insomnia Scale. S1:
(0 = students on campus, 1 = students at home), S2: (0 = students on campus, 1 = full time employment),
S3: (0 = students on campus, 1 = part-time employment), S4: (0 = students on campus, 1 = unemployed).
*p<.05; **p<.01, ***p<.001
22
23
Table 3 Results of the Mediation Analyses using PROCESS
PSQI (N = 324) FAS (N = 329) BIS (N = 334)
B β SE (β) B β SE (β) B β SE (β)
Outcome (PSAcog)
Binge Viewing Frequency .724 .133* .348 .666 .122* .055 .739 .135* .054
Gender 1.558 .224 .054 1.482 .213 .122 1.569 .225 .120
Age .048 .013 .120 .042 .011 .065 .014 .004 .064
S1 -1.555 -.224 .064 -1.398 -.201 .365 -1.555 -.224 .363
S2 -1.139 -.164 .359 -.983 -.141 .365 -1.088 -.156 .363
S3 -1.310 -.188 .508 -1.326 -.191 .517 -1.327 -.191 .515
S4 -3.751 -.539 .368 -3.804 -.547 .373 -3.604 -.518 .371
Shiftwork -1.541 -.222 .330 -.567 -.082 .309 -.755 -.109 .308
General Health -1.358 -.152** .059 -1.185 -.133* .055 -1.121 -.126* .054
Exercise (h/week) .279 .062 .055 .199 .044 .059 .169 .038 .059
TV Viewing (h/week) .051 .026 .056 .115 .005 .057 .013 .007 .056
Outcome (dependents)
PSAcog .199 .589*** .047 .414 .462*** .049 .576 .565*** .046
Binge Viewing Frequency .115 .063 .045 .365 .075 .049 .440 .079 .045
Gender -.459 -.195 .100 -.521 -.084 .107 -.556 -.078 .099
Age -.089 -.070 .053 -.020 -.006 .057 -.224 -.059 .053
S1 .607 .258 .297 1.130 .181 .321 .500 .071 .298
S2 .798 .339 .297 1.315 .211 .321 1.675 .236 .298
24
S3 .342 .145 .421 -3.053 -.489 .454 3.900 .549 .422
S4 1.430 .608* .306 -.026 -.004 .329 3.319 .467 .305
Shiftwork -.414 -.176 .253 1.537 .246 .272 -2.498 -.352 .252
General Health -.403 -.134 .049 -1.638 -.205*** .049 -.629 -.069 .048
Exercise (h/week) .095 .063** .046 -.115 -.029 .052 -.191 .042 .045
TV Viewing (h/week) -.040 -.059 .046 -.163 -.090 .050 -.148 -.072 .046
Unstand.
Indirect
effect
.144; Boot
SE = .059;
CI95%[.032;
.264]
.276; Boot
SE = .131;
CI95%
[.030;.542]
.426; Boot SE = .175; CI95% [.091, .767]
Stand.
Indirect
effect
.078; Boot
SE = .033;
CI95%[.014;
.145]
.056; Boot
SE = .027;
CI95%
[.008;.110]
.077; Boot SE = .032; CI95% [.014; .137]
Note. PSQI = Pittsburgh Sleep Quality Index, FAS = Fatigue Assessment Scale, BIS = Bergen Insomnia Scale, PSAcog = Cognitive Pre-Sleep Arousal. S1: (0 =
students on campus, 1 = students at home), S2: (0 = students on campus, 1 = full time employment), S3: (0 = students on campus, 1 = part-time employment), S4: (0
= students on campus, 1 = unemployed). Analyses are based on 5000 bootstrap samples. S1: (0 = students on campus, 1 = students at home), S2: (0 = students on
campus, 1 = full time employment), S3: (0 = students on campus, 1 = part-time employment), S4: (0 = students on campus, 1 = unemployed).
*p<.05; **p<.01, ***p<.001
25
Appendix A: Binge Viewing Measure
“Binge viewing” is defined as watching multiple, consecutive episodes of the same TV-show in one
sitting, be it on a television-, laptop-, computer-, tablet- or smartphone screen. Binge viewing can be
done by streaming via the Internet, using streaming services such as Netflix, downloading, via
Youtube, using DVD box sets or digital video recorders.
When reading this definition of binge viewing, do you consider yourself a binge viewer?
oYes
oNo
How often have you engaged in binge viewing during the past month?
oApproximately once during the past month
oA few times during the past month
oApproximately once a week during the past month
oA few times a week during the past month
o(almost) every day during the past month
Please estimate how much time you usually spent on one binge viewing session during the past
month.
Approximately … hours and … minutes
How many episodes did you usually watch in one binge viewing session during the past month?
oUsually 2 episodes
oUsually 3 – 4 episodes
oUsually 5– 6 episodes
oUsually more than 6 episodes