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RUNNING HEAD : Sleep Quality and Videogames in Adults
This article is a post-print version.
Publisher version available at http://onlinelibrary.wiley.com/doi/10.1111/jsr.12255/abstract
Sleep Quality is Negatively Related to Video Gaming Volume in Adults
Liese Exelmans| liese.exelmans@soc.kuleuven.be
Jan Van den Bulck| jan.vandenbulck@soc.kuleuven.be
School for Mass Communication Research, KU Leuven, Belgium
Article accepted for publication in Journal of Sleep Research.
Please cite as:
Exelmans, L., & Van den Bulck, J. (2015). Sleep quality is negatively related to video gaming
volume in adults. Journal of Sleep Research, 24(2), 189-196.
Corresponding author: Liese Exelmans, School for Mass Communication Research, Parkstraat 45,
3000 Leuven, Belgium. E-mail: liese.exelmans@soc.kuleuven.be. Telephone: 003216323231
Conflict of Interest: Liese Exelmans has no conflicts of interest to report. Jan Van den Bulck has no
conflicts of interest to report.
Author Contributions: Liese Exelmans performed the statistical analyses to examine the research
question. She also conducted the data collection and was the primary author of the manuscript. Jan
Van den Bulck was involved in the original design of the study and served as a statistical consultant.
He also provided the impetus to examine gaming volume and sleep quality in the existing data set.
Summary
Sleep Quality and Videogames in Adults 2
Most literature on the relationship between video gaming and sleep disturbances has looked at children
and adolescents. There is little research on such a relationship in adult samples. The aim of the current
study was to investigate the association of video game volume with sleep quality in adults via face-to-
face interviews using standardized questionnaires. Adults (N = 844, 56.2% women), aged 18-94 years
old, participated in the study. Sleep quality was measured using the Pittsburgh Sleep Quality Index
(PSQI) and gaming volume was assessed by asking the hours of gaming on a regular weekday (Mo-
Thu), Friday and weekend day (Sat-Sun). Adjusting for gender, age, educational level, exercise and
perceived stress, results of hierarchical regression analyses indicated that video gaming volume was a
significant predictor of sleep quality (β =.145), fatigue (β = .109), insomnia (β=.120), bedtime (β= .
100) and rise time (β = .168). Each additional hour of video gaming per day delayed bedtime by 6.9
minutes (95% CI 2.0 – 11.9 minutes) and rise time by 13.8 minutes (95% CI 7.8 - 19.7 minutes).
Attributable risk for having poor sleep quality (PSQI>5) due to gaming >1h/day was 30%. When
examining the components of the PSQI using multinomial regression analysis (odds ratios with 95%
confidence intervals), gaming volume significantly predicted sleep latency, sleep efficiency and use of
sleep medication. In general, findings support the conclusion that gaming volume is negatively related
to the overall sleep quality of adults, which might be due to underlying mechanisms of screen
exposure and arousal.
Sleep Quality and Videogames in Adults 3
Introduction
Media use is being increasingly labeled as a significant risk factor contributing to sleep
difficulties (Cain & Gradisar, 2010). A relationship between sleep problems and media use has been
established for television (Paavonen et al., 2006; Van den Bulck, 2004b), mobile phones (Nathan &
Zeitzer, 2013; Van den Bulck, 2007) and internet use (Custers & Van den Bulck, 2012; Shochat et al.,
2010). A growing body of literature has investigated the effects of video gaming on sleep quality:
frequent video game playing before bedtime or at night has been linked to later bedtimes, a shorter
sleep duration, increased sleep-onset latency, and more daytime tiredness (Adam et al., 2007;
Eggermont & Van den Bulck, 2006; King et al., 2013; Van den Bulck, 2004b). However, most studies
to date have focused on children and adolescents rather than adults. Although video gaming is a
popular pastime among adolescents (Rideout et al., 2010), previous research has identified a
substantial proportion of video gamers among adults too (Lenhart et al., 2008). Indeed, gamers are
found to be between 30 and 35 years old on average (Lenhart et al., 2008; Williams et al., 2008). Over
half of American adults aged 18 years and older played videogames and about one in five (21%) of
them played almost every day. Almost half (45.1%) of the respondents (N = 562) in a study by
Weaver et al. (2009) identified themselves as gamers. In Belgium, where the current study was
conducted, one in four adults were playing video games on a weekly basis (Interactive Software
Federation of Europe, 2012). While it has been found that the younger adults are significantly more
likely to play video games compared to older adults, studies have found that older gamers tend be play
more frequently. Lenhart et al. (2008) found that one third of the gamers aged 65 and older played
almost every day, compared to one in five for younger group.
In sum, the main goal of the current study is to explore the relationship between video gaming
and sleep quality in an adult sample.
Method
Design
A sample of 844 adults in Flanders, Belgium were queried about media habits and sleep in
face-to-face interviews. After having received standardized interviewer training, the effects of which
on improving interview quality has been reported (Billiet & Loosveldt, 1988), 44 undergraduate
Sleep Quality and Videogames in Adults 4
students of Communication Sciences conducted face-to-face interviews by means of a standardized
questionnaire. A two-step sampling method was used. First, 44 cities or villages in Flanders (Belgium)
were randomly selected, one for each interviewer. Second, 20 addresses were selected from the
telephone directory of each city or village. The undergraduate students used a random number
generator for the selection of addresses (i.e. the number of the page, the number of the column and the
number of the line). Interviewers were instructed to go to the addresses in the order in which they had
been selected and to proceed to the first building to the left of the building on the address list. To avoid
limiting the sample to those at home and most likely to open the door, they had to interview the adult
(≤18 years old) member of the household who was the first in line to celebrate his or her birthday (see:
Oldendick & Link,1994) . When nobody answered or when the person selected was not at home they
had to try to initiate contact three times before moving on to a replacement address to reduce
undersampling of those with an active lifestyle. The process stopped when the interviewer had
conducted 20 interviews. The survey was presented as a study on adults’ leisure time activities and
their general well-being. In addition to questions about their sleep behavior and media use, the
questionnaire comprised other well-being measures (stress, depression, life satisfaction) and measures
of leisure time activities (exercise, going out, other types of media use). Respondents were guaranteed
that all answers would be treated anonymously. The study received ethical clearance from the Faculty
of Social Sciences of KU Leuven, and informed consent was obtained from all respondents.
Instruments
Pittsburgh Sleep Quality Index (PSQI). This measure was used with permission of the
developers (Buysse et al., 1989). The index consists of 19 items assessing respondents’ sleep quality
over the past month. The 19 items can be grouped into seven components (subjective sleep quality,
sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication
and daytime dysfunction), each weighed equally on a 0-3 scale. Respondents received an overall score
ranging from 0 to 21, with lower scores indicating a better sleep quality. Respondents scoring below
five are rated as good sleepers.
Fatigue Assessment Scale (FAS). This self-report fatigue scale comprises 10 items indicating
symptoms of fatigue, where respondents assess how they usually feel using a 5-point scale. The scale
Sleep Quality and Videogames in Adults 5
has demonstrated good reliability and validity (Michielsen & Vries, 2004). Items were summed
yielding a total fatigue score, ranging from 0 to 50. A higher score indicates more severe symptoms of
fatigue .
Bergen Insomnia Scale (BIS). BIS (Pallesen et al., 2008) comprises six items referring to
symptoms of insomnia. Participants indicated how many days per week during the last month they
experienced a specific symptom related to insomnia. A total insomnia score was computed, ranging
from 0 to 42.
Bedtime & Rise time. Respondents were asked to indicate at what time they usually went to
bed. For the purpose of the analyses, minutes were divided by 60 and multiplied by 100. Hours were
counted from 0 to 24. Hours after midnight were counted as 25 (for 1:00), 26 (for 2:00), etc. to create a
metric variable.
Video gaming volume. Respondents were asked: “On average, how many hours do you play
video games (1) on a regular weekday (Monday - Thursday); (2) on a regular Friday and (3) on a
regular weekend day (Saturday - Sunday)? Include playing videogames on a computer or console and
games you played on the internet or social media.” We distinguished Fridays from weekdays because
Friday is the beginning of the weekend and many respondents may therefore play more hours of video
games compared to other weekdays. Total video game playing volume per week was estimated by first
multiplying volume on an average weekday by four and multiplying volume on an average weekend
day by two. Next, summing these average measures of volume on weekdays and weekend days and
average viewing on Fridays led to an estimated weekly average of video game playing per week.
Dividing this variable by 7 produced an estimate of average daily video game playing (hours). This
continuous variable was also recoded into 3 categories: (0) 0 hours/day (66.4%), (1) ≤1 hour a day
(22.4%), (2) >1 hour a day (11.2%) .
Control variables. We entered gender (0 = male, 1 = female), age, educational level, exercise
and perceived stress as control variables. Among adults, the likelihood of being a video gamer has
been found to be associated with age (older<younger), gender (women<men) and educational level
(low or highly educated<middle educated) (Interactive Software Federation of Europe, 2012; Lenhart
et al., 2008). The sample was divided into three age groups: 18-35 years old (31.6%), 36-55 years old
Sleep Quality and Videogames in Adults 6
(36.2%) and 56-94 years old (32.2%). Educational level was measured by asking respondents about
the highest educational degree they obtained: no degree, finished primary school (K6 equivalent),
finished secondary school (K12 equivalent), college degree, university degree. Respondents who were
students (N=77) were included as if they had finished that level of education. Physical activity has
been found to be beneficial for overall sleep quality (Youngstedt, 2005) and daily exercise is listed as
one of the ten health tips for better sleep by the National Sleep Foundation. Contrary, higher levels of
perceived stress have been associated with shorter sleep duration, more fragmented sleep, more
nightmares and delayed sleep onset (Akerstedt, 2006; Lund, Reider, Whiting & Prichard, 2010). Hours
of exercise were measured by asking respondents how many hours a week they exercised to the extent
where they became out of breath. Perceived level of stress was assessed by a summed score on the
Perceived Stress Scale (Cohen et al., 1983).
Analyses
Data were analyzed using statistical package SPSS for Windows (Version 22.0, Chicago, IL,
USA). Correlations between sleep variables, gaming volume and control variables were computed
using bivariate Pearson (for continuous data) and Spearman (for ranked data) correlation analyses.
Hierarchical multiple regression analysis was used to examine the relationship between gaming
volume and sleep variables. For each sleep variable, gender, age, educational level, hours of exercise
per week and perceived stress were entered into the first step of a regression analysis. In the second
step, video gaming volume was entered . Additionally, binomial logistic regression analysis was used
to investigate the relationship between gaming volume and the binary PSQI score (PSQI-score ≤5 = 0,
PSQI score >5 = 1) and multinomial logistic regression analyses for the relationship between gaming
volume and the PSQI components (scores ranging from 0 to 3).
Results
Response rate was 43.1%. A total of 1957 addresses were visited, of which 687 (35.1%) refused
participation and 426 (21.7%) did not answer the door for three separate visits. Data were carefully
examined for coding errors and other abnormalities such as outliers, true impossible values and
Sleep Quality and Videogames in Adults 7
missing values. A total of 844 questionnaires were retained for analysis, provided completed
questionnaires.
The sample consisted of 56.2% women and 43.8% men. Respondents were between 18 and 94
years old, with an average of 46.0 years (SD = 17.76). About one in five (20.7%) of the respondents
had at least obtained a university degree, 31.8 % had received a college degree, 29.9% had a degree of
secondary school and 5.7% had received a degree of primary school. Almost one in ten respondents
(9.2%) were still studying and 2.6% had no degree. Differences between the population and the
sample were not significant for gender (χ² (1, N=844) = 1, p = .317) or age (χ² (2, N=844) = 2.67, p = .
263). Our sample was overrepresented by highly educated respondents (χ² (2, N=844) = 217.7, p < .
01). Studies have found that higher educated people are more likely to participate in surveys than less
educated people (Curtin, Presser, and Singer, 2000; Goyder, Warriner, & Miller, 2002).
Reliability indicators (Cronbach’s alpha) of the measures were as follows: PSQI ( α= .61),
Fatigue Assessment Scale (α = .83), Bergen Insomnia Scale (α = .76), Perceived Stress Scale (α = .83).
Descriptive statistics and correlations among study variables are presented in Table 1. Around one in
three respondents identified themselves as a gamer (34.4%). Across the whole sample, respondents
played video games for 22.87 minutes a day (median = 0.00; IQR = 21.4). Among the gamers, the
mean volume of gameplay per day was 67.0 minutes (median = 42.86, IQR = 57.14). Mean bedtime
and rise time was 23:25 h (SD = 1:05) and 7:30 h (SD = 1:25), respectively. The mean score for the
PSQI was 4.56 (SD = 2.66), which borders on the cut-off score for poor sleep quality (PSQI score >5).
Gender (Spearman rho = -.184, p<.01) and age (Pearson r = -.287, p<. .01) were negatively
correlated with gaming volume. Men and women differed significantly in their average gaming
volume (min/day): whereas men played 34.9 minutes per day on average (SD = 70.68), women played
13.52 minutes per day on average (SD = 34.22) (t(504.70) = 5.307, p<.001)). There was a significant
difference between age groups for gaming volume (min/day) as determined by one-way ANOVA ( F
(2,823) = 37.884, p<.001). The youngest age group (18-35 years old) (M = 45.93, SD = 74.63) played
video games significantly more often than the middle (36-55 years old) (M = 16.45, SD = 44.80,
p<.001) or oldest age group (56-94 years old) ( M = 8.11, SD = 29.24; p<.001). There was no
significant difference between the middle age and oldest age group (p =.178).
Sleep Quality and Videogames in Adults 8
Correlations between sleep variables and gaming volume were computed using bivariate
Pearson correlation analyses. Gaming volume was positively related with all sleeping variables, with
coefficients ranging between .104 and .189 (p<.01), which, while significant, are considered to be
small (Field, 2009). Age and educational level were negatively correlated with the fatigue score and
rise time. Hours of exercise per week were negatively correlated with the PSQI-score, the Fatigue
Score and the Insomnia Score, and level of perceived stress showed a positive correlation with these
variables.
[TABLE 1 AROUND HERE]
Gaming volume & sleep variables
After entering the various control variables to the model, gaming volume positively predicted all
sleeping variables (see Table 2). In other words, more hours of video game play per day was
associated with higher levels of fatigue (β= .109, p<.01), more symptoms of insomnia (β= .120,
p<.01), later bedtime (β= .100, p<.01) and later rise time (β= .168, p<.001) (see Fig. 1). The changes
in R² for each model were significant when adding gaming volume as a predictor in the final step. For
bedtime and rise time, the degree of change was calculated using the unstandardized b-values. Each
additional hour of video gaming per day delayed bedtime by 6.9 minutes (95% CI 2.0 – 11.9 minutes)
and rise time by 13.8 minutes (95% CI 7.8 - 19.7 minutes).
Gaming volume and PSQI score
For the PSQI score, results indicated that the more hours respondents played videogames per
day, the worse was their overall sleep quality (β= .145, p<.001) (see Fig.1). Logistic regression
analysis indicated that the odds of having a poor sleep quality (PSQI-score >5) increased by 31.0% per
extra hour of video gameplay per day (Exp(B) = 1.309, p<.01, 95%CI:1.093-1.567). Compared to
respondents who did not play videogames, respondents who played >1 hour per day (N =93) were 2.75
times more likely of having a poor sleep quality (Exp(B) = 2.746, p<.001, 95% CI: 1.596-4.723).
Playing videogames for more than 1 hour a day doubles the odds of having a poor sleep quality.
Attributable risk is the difference in prevalence between exposed and non-exposed groups and thus
Sleep Quality and Videogames in Adults 9
reflects the proportion of prevalence (in our case: poor sleep quality) attributable to the exposure (in
our case: video gaming) in relation to all cases (see: Webb, Bain & Pirozzo, 2006). In the group of
respondents who play >1 hour of video games per day, 29.5% of the prevalence of poor sleep quality
could be attributed to their game play. There was no significant increase in odds when comparing
those who did not play videogames and those who played less than one hour a day, and thus no
attributable risks were calculated.
[TABLE 2 AROUND HERE]
[FIGURE 1 AROUND HERE]
Gaming volume & PSQI-components
Every component has a range of 0 to 3 points in the PSQI: a score of 0 indicates no difficulty, while a
score of 3 indicates severe difficulty (Buysse et al., 1989). In the current study, the last two categories
of every component were merged, given the small proportion of respondents in the highest category.
The results are therefore computed with components ranging from 0 to 2.
[TABLE 3 AROUND HERE]
Gaming volume significantly predicted sleep latency, sleep efficiency and – to a lesser extent –
subjective sleep quality and the use of sleep medication.
First, regarding sleep latency, respondents are more likely to need more time to fall asleep
when their video gaming volume increases (see Table 3). Compared to those who do not play
computer games, those who played less than one hour of videogames a day were 1.5 times more likely
to have a sleep latency score of 2, although this was only marginally significant (p=.098). Those who
played more than one hour per day were 3.4 times more likely of having a sleep latency score of 2.
Second, video gaming volume positively predicted the sleep efficiency score. Sleep efficiency
refers to the proportion of hours slept versus the hours spent in bed. A high sleep efficiency score
means that this balance is optimal: time spent in bed is almost completely spent sleeping. Table 3
Sleep Quality and Videogames in Adults 10
shows that compared to those who do not play videogames, the odds of having a sleep efficiency
score of 1 (rather than 0) are 2.7 times higher for those who played less than one hour per day. The
odds of having a sleep efficiency score of 2 (rather than 0) were 2.8 times higher for respondents who
played videogames >1 hour a day, compared to those who never played videogames.
Third, the results showed that video gaming volume significantly predicted the use of sleep
medication. Respondents who played more than 1 hour a day were 2.2 times more likely to use sleep
medication at least once or twice a week (rather than not during the past month), compared to
respondent who never played videogames, although this was only marginally significant (p=.098). The
analysis with categorical data for gaming volume could not be conducted for respondents who had a
score of 1 on use of sleep medication, because some subcategories of gaming volume and use of sleep
medication contained no respondents.
Finally, gaming volume appeared to be related to subjective sleep quality. Those who played
videogames for more than one hour a day were 2.3 times more likely to perceive their sleep quality as
rather bad than very good, compared to those who do not play video games (Exp (B) = 2.290, p <.05).
When examining these results, it is noteworthy to point out that (1) there was often only a
significant result when comparing the lowest and highest scores on the components, (2) there were
almost no significant differences when comparing those who did not play videogames and those who
played them ≤1 hour a day overall
Discussion
The present study explored the relationship between gaming volume and sleep quality in a
sample of 844 Flemish adults, ranging between 18 and 94 years old. In this adult sample, one in three
(34.4%) respondents identified themselves as a gamer, which is a bit lower than the proportion
(45.1%) that was found in a study by Weaver et al. (2009), who examined adults of the same age range
and used the same dichotomization as this study did. Those who played videogames in our sample,
played 67 minutes (1.11 hours) per day on average, versus 0.87 hours in the study by Weaver et al.
(2009).
Sleep Quality and Videogames in Adults 11
The findings from the current study suggest two conclusions. First, gaming volume is
significantly and negatively related to fatigue, insomnia, bedtime and rise time in adults: the more
adults play video games, the higher their reported levels of fatigue and insomnia, and the later their
bedtime and rise time. Gaming volume was a significant predictor of sleep quality in adults: per
additional hour of video gaming per day respondents had a significantly greater risk (31%) of having a
poor sleep quality, characterized by a PSQI-score above five. Attributable risk calculation further
suggested that 30% of the prevalence of poor sleep quality among respondents who play >1 hour of
video gaming per day was attributable to their being in that game volume category. Although the
observed relationships were small, gaming volume significantly increased the explained variance in
each dependent variable.
The reported delay in bedtime and rise time associated with video game play confirm previous
research demonstrating that media use among adults coincides with later bedtimes, but also with later
rise times, a process called time shifting (Custers & Van den Bulck, 2012). This can be considered
surprising when taking into account adults’ daytime commitments. An interesting hypothesis for future
research may be to examine whether adults who engage in heavier media use, also have a lifestyle that
is less restrained by daytime commitments which allows them to delay their sleep.
Second, the more adults play video games, the more likely they are of needing more time to
fall asleep, of having a lower sleep efficiency and of using sleep medication more frequently. The
observation that most results were only significant when comparing those who play videogames more
than one hour a day is striking. Previous experimental research has documented an effect of video
gaming on sleep only for game play of over 60 minutes (Dworak et al. 2007, King et al., 2013). It
therefore seems that video gaming becomes most detrimental to sleep quality when it exceeds a
volume of 1 hour per day.
The results of this study may be explained by two mechanisms that have been described to
explain the relationship between media use and sleep quality. First, exposure to bright light emanating
from electronic screens suppresses the secretion of melatonin, which in turn delays sleep onset
(Higuchi, Motohashi, Liu & Maeda, 2005). This is particularly applicable to media use where the user
is closer to the screen, such as video gaming or computer use (Christakis & Zimmerman, 2006).
Sleep Quality and Videogames in Adults 12
Second, our findings may support the idea of arousal as an underlying mechanism between video
gaming and sleep (Cain & Gradisar, 2010; Zimmerman, 2008). Playing video games can affect arousal
parameters such as respiratory rate, blood pressure and heart rate (Anderson & Bushman, 2001).
Increased arousal has been previously associated with difficulties falling asleep and night wakings
(Paavonen et al., 2006; Van den Bulck, 2004a). This may be a valid explanation especially for
exciting, competitive and violent video games, given that research has reported stronger effects on
arousal than when playing non-violent games (Fleming & Rickwood, 2001). Future research should
therefore look into the role of gaming genre in the relationship between video gaming and sleep
quality.
Several limitations need to be acknowledged. First, while we used random sampling methods,
these data only apply to Flemish adults and conclusions might thus not be transferable to other age
groups or countries. There was an overrepresentation of highly educated people, which might have led
to some bias in our results. Second, while the fact that one in three of the respondents in our sample
was identified as a gamer shows that gaming is prevalent among adults, it also means that our findings
indicate a relationship between video game volume and sleep for a limited proportion of adult
population. We suspect this is partly explained by the wide age range of our sample. However,
possible moderation effects between video gaming volume and age were not significant and limiting
the sample to a smaller age group did not appear to affect the relationships between gaming volume
and sleep variables. Studies have nonetheless found that younger adults are significantly more likely
than older adults to play videogames (Lenhart, 2008). Additionally, it has been well documented that
age has a significant impact on a multitude of sleep parameters (see Ohayon, Carskadon,
Guilleminault, Vitiello, 2004). We thus believe that the possible interactions between age, video
gaming and sleep might be an interesting avenue for future scholars. Third, the beta coefficients of our
regression analyses were small, ranging between .100 and .170. While we added several important
control variables in our final regression model, we also suspect that our gaming measure may partly
account for these small coefficients. Our measure of gaming volume probed the hours of video gaming
on a regular weekday, Friday and weekend day, which did not allow us to (1) measure gaming before
bedtime and (2) make a distinction between Saturday and Sunday. First, it can be expected that the
Sleep Quality and Videogames in Adults 13
likelihood of an effect of video gaming on sleeping variables is greater when the gaming activity
approximates bedtime, considering processes or arousal and screen light. Delineating the gaming
activity prior to bedtime or even after bedtime, may result in additional insights on its relationship
with sleep. Second, research between media use and sleep might benefit from measuring video gaming
volume during weekdays (Sunday-Thursday) and weekend days (Friday and Saturday). While Sunday
is part of the weekend, Sunday night ought, perhaps, to be considered a week night and thus adults
gamers may be less likely to play videogames until late at night. Finally and most importantly, our
findings are limited by a cross-sectional design. We can make no causal inferences and cannot
conclude that gaming volume reduces overall sleep quality. The reversed hypothesis that respondents
who experience sleep difficulties may be self-treating such a problem by playing video games (e.g. to
distract themselves), cannot be ruled out and is a valid suggestion for future research. In particular,
some studies have outlined a sleep-facilitating role for media use (Eggermont & Van den Bulck, 2006;
Gooneratne et al., 2011), although support for such a hypothesis remains mixed.
Taken together, the present study corroborates previous research and appears to provide
additional evidence that playing video games can be considered to be part of bad sleep hygiene. While
results indicated a significant negative relationship, the coefficients of the regression analyses suggest
that the possible impact of video gaming volume on sleep is smaller for adults than for children and
adolescents. Although there is a significant subsection of the adult population that plays a lot of
computer games, this number is still considerably smaller than the number of teen gamers (Lenhart et
al., 2008). Our observation that adults can compensate for lost sleep by time shifting may also explain
the discrepancy in effects among adults vs. teens. Results from research on media use and sleep in
children and adolescents should therefore not be extrapolated to adult populations without further
scrutiny, as other patterns, processes and problems are likely to occur (see Custers & Van den Bulck,
2012).
Finally, although sleep deficiency remains a problem that affects all age groups, we tend to
be particularly alarmed about effects at a young age. It is indeed crucial to initiate adoption of good
sleep hygiene practices at a young age, yet the importance of the maintance of healthy sleep habits as
we become responsible for our own sleep schedule should not be undervalued by sleep scholars.
Sleep Quality and Videogames in Adults 14
Sleep Quality and Videogames in Adults 15
References
Adam, E. K., Snell, E. K., & Pendry, P. (2007). Sleep timing and quantity in ecological and family
context: a nationally representative time-diary study. Journal of Family Psychology : JFP :
Journal of the Division of Family Psychology of the American Psychological Association
(Division 43), 21(1), 4–19. doi:10.1037/0893-3200.21.1.4
Akerstedt, T. (2006). Psychosocial stress and impaired sleep. Scandinavian Journal of Work,
Environment and Health, 32(6), 493–501.
Anderson, C. a, & Bushman, B. J. (2001). Effects of violent video games on aggressive behavior,
aggressive cognition, aggressive affect, physiological arousal, and prosocial behavior: a meta-
analytic review of the scientific literature. Psychological Science, 12(5), 353–9. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/11554666
Buysse, D., Reynolds, C., & Monk, T. (1989). The Pittsburgh Sleep Quality Index: a new instrument
for psychiatric practice and research. Psychiatry Research, 28(2), 193–213.
doi: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(8), 735–42. doi:10.1016/j.sleep.2010.02.006
Christakis, D. a, & Zimmerman, F. J. (2006). Media as a public health issue. Archives of Pediatrics &
Adolescent Medicine, 160(4), 445–6. doi:10.1001/archpedi.160.4.445
Curtin, R., Presser, S., & Singer, E. (2000). The effects of response rate changes on the index of
consumer sentiment. Public Opinion Quarterly, 64(4), 413–28. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/11171024
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(2), 96–105.
doi:10.1080/15402002.2011.596599
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(5), 978-985.
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(7-8), 428–
33. doi:10.1111/j.1440-1754.2006.00892.x
Fleming, M. J., & Rickwood, D. J. (2001). Effects of Violent Versus Nonviolent Video Games on
Children’s Arousal, Aggressive Mood, and Positive Mood. Journal of Applied Social Psychology,
31(10), 2047–2071. doi:10.1111/j.1559-1816.2001.tb00163.x
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(2), 297–303. doi:10.1111/j.1532-5415.2010.03247.x
Goyder, J., Warriner, K., & Miller, S. (2002). Evaluating Socio-economic Status ( SES ) Bias in
Survey Nonresponse. Journal of Official Statistics, 18(1), 1–11.
Higuchi, S., Motohashi, Y., Liu, Y., & Maeda, A. (2005). Effects of playing a computer game using a
bright display on presleep physiological variables, sleep latency, slow wave sleep and REM
sleep. Journal of Sleep Research, 14(3), 267–73. doi:10.1111/j.1365-2869.2005.00463.x
Interactive Software Federation of Europe. (2012). Videogames in Europe - Consumer Study: Belgium.
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(2), 137–43. doi:10.1111/j.1365-2869.2012.01060.x
Lenhart, A., Jones, S., & Macgill, A. R. (2008). Adults and video games (pp. 1–9).
Loosveldt, G., & Billiet, J. (1988). American Association for Public Opinion Research Improvement
of the quality of responses to factual survey questions by interviewer training. The Public
Opinion Quarterly, 52(2), 190–211.
Lund, H. G., Reider, B. D., Whiting, A. B., & Prichard, J. R. (2010). Sleep patterns and predictors of
disturbed sleep in a large population of college students. The Journal of Adolescent Health :
Sleep Quality and Videogames in Adults 16
Official Publication of the Society for Adolescent Medicine, 46(2), 124–32.
doi:10.1016/j.jadohealth.2009.06.016
Michielsen, H., & Vries, J. De. (2004). Examination of the Dimensionality of Fatigue: The
Construction of the Fatigue Assessment Scale (FAS). … Assessment, 20(1), 39–48.
doi:10.1027//1015-5759.20.1.39
Nathan, N., & Zeitzer, J. (2013). A survey study of the association between mobile phone use and
daytime sleepiness in California high school students. BMC Public Health, 13(1), 840.
doi:10.1186/1471-2458-13-840
Ohayon, M. M., Carskadon, M. a, Guilleminault, C., & Vitiello, M. V. (2004). Meta-analysis of
quantitative sleep parameters from childhood to old age in healthy individuals: developing
normative sleep values across the human lifespan. Sleep, 27(7), 1255–73. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/15586779
Oldendick, R. W., & Link, M. W. (1994). The Answering Machine Generation : Who Are They and
What Problem do They Pose for Survey Research. The Public Opinion Quarterly, 58(2), 264–
273.
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(2),
154–61. doi:10.1111/j.1365-2869.2006.00525.x
Pallesen, S., Bjorvatn, B., Nordhus, I. H., Sivertsen, B., Hjørnevik, M., & Morin, C. M. (2008). A new
scale for measuring insomnia: the Bergen Insomnia Scale. Perceptual and Motor Skills, 107(3),
691–706. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/19235401
Rideout, V., Foehr, U., & Roberts, D. (2010). Generation M : Media in the Lives of 8-to 18-Year-Olds.
Henry J. Kaiser Family Foundation. Retrieved from
http://www.eric.ed.gov/ERICWebPortal/recordDetail?accno=ED527859
Shochat, T., Flint-Bretler, O., & Tzischinsky, O. (2010). Sleep patterns, electronic media exposure and
daytime sleep-related behaviours among Israeli adolescents. Acta Paediatrica (Oslo, Norway :
1992), 99(9), 1396–400. doi:10.1111/j.1651-2227.2010.01821.x
Van den Bulck, J. (2004a). Media Use and Dreaming: The Relationship Among Television Viewing,
Computer Game Play, and Nightmares or Pleasant Dreams. Dreaming, 14(1), 43–49.
doi: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(1), 101–
104.
Van den Bulck, J. (2007). Adolescent use of mobile phones for calling and for sending text messages
after lights out: results from a prospective cohort study with a one-year follow-up. Sleep, 30(9),
1220–3. Retrieved from http://www.pubmedcentral.nih.gov/articlerender.fcgi?
artid=1978406&tool=pmcentrez&rendertype=abstract
Weaver, J. B., Mays, D., Sargent Weaver, S., Kannenberg, W., Hopkins, G. L., Eroğlu, D., &
Bernhardt, J. M. (2009). Health-risk correlates of video-game playing among adults. American
Journal of Preventive Medicine, 37(4), 299–305. doi:10.1016/j.amepre.2009.06.014
Williams, D., Yee, N., & Caplan, S. E. (2008). Who plays, how much, and why? Debunking the
stereotypical gamer profile. Journal of Computer-Mediated Communication, 13(4), 993–1018.
doi:10.1111/j.1083-6101.2008.00428.x
Youngstedt, S. D. (2005). Effects of exercise on sleep. Clinics in Sports Medicine, 24(2), 355–65, xi.
doi:10.1016/j.csm.2004.12.003
Zimmerman, F. (2008). Children’s Media Use and Sleep Problems: Issues and Unanswered Questions.
Research Brief. Henry J. Kaiser Family Foundation, (June). Retrieved from
http://www.eric.ed.gov/ERICWebPortal/recordDetail?accno=ED527857
