Journal of Pediatric Nursing (2016) 31, 498–504

Adolescent Sleep and the Impact of Technology

Use Before Sleep on Daytime Function
Ann E.E. Johansson BSN, RN ⁎, Maria A. Petrisko BSN, RN, Eileen R. Chasens PhD, RN
University of Pittsburgh School of Nursing, Pittsburgh, PA

Received 9 November 2015; revised 25 April 2016; accepted 25 April 2016

Key words:
Purpose Technology has become pervasive in our culture, particularly among adolescents. The purpose of this
Adolescence;
study is to examine associations between use of technology before sleep and daytime function in adolescents.
Technology;
Design and Methods: This study is a secondary analysis of respondents aged 13 to 21 years (N = 259) from the
Daytime sleepiness
2011 National Sleep Foundation's Sleep in America Poll. The survey included questions on demographics,
sleep habits, and use of technology in the hour before bedtime. Daytime sleepiness was assessed with the
Epworth Sleepiness Scale (ESS). Student's t-tests, Mann–Whitney U, and Fischer's exact tests were performed
to detect differences in demographics, sleep duration, and technology use in the total sample, and between
respondents with “adequate” compared to “inadequate” sleep. Correlations were calculated between
technology frequency and daytime function.
Results: Adolescents had mean sleep duration of 7.3 ± 1.3 h. Almost all respondents (97%) used some form
of technology before sleep. Increased technology use and the frequency of being awoken in the night by a
cell phone were significantly associated with waking too early, waking unrefreshed, and daytime sleepiness
(p b 0.05). Adolescents who reported “inadequate” sleep had shorter sleep duration, greater frequency of
technology use before bedtime, feeling unrefreshed on waking, and greater daytime sleepiness than those
reporting “adequate” sleep (all p-values b 0.05).
Conclusion: Technology use before sleep by adolescents had negative consequences on nighttime sleep and on
daytime function.
Practice Implications: Healthcare professionals who interact with adolescents should encourage technology to
be curtailed before bedtime and for adolescents to value obtaining adequate sleep.
© 2016 Elsevier Inc. All rights reserved.

ADOLESCENTS REQUIRE APPROXIMATELY 8 to 10 This suggests that sleep loss in adolescents may be even greater
h of sleep per night, but many studies suggest that they obtain than many reports indicate (Fossum, Nordnes, Storemark,
much less (Arora, Broglia, Thomas, & Taheri, 2014; Eaton et Bjorvatn, & Pallesen, 2014).
al., 2010; McKnight-Eily et al., 2011; Wolfson & Johnson, The decline in adolescent sleep quantity and quality is
2014). In the past two decades, the number of adolescents multifactorial, and is influenced by biological, environmental,
reporting more than 7 h of sleep per night and the percentage societal, and behavioral factors (Bartel, Gradisar, & Williamson,
reporting adequate sleep per night has steadily decreased 2015; Calamaro, Mason, & Ratcliffe, 2009; Pallesen et al.,
(Iglowstein, Jenni, Molinari, & Largo, 2003; Keyes, 2011). With the marked increase in technology during the 21st
Maslowsky, Hamilton, & Schulenberg, 2015). Studies century, screen time has become an integral part of life for
comparing self-reported adolescent sleep duration with today's adolescent, who values the connectivity offered by
objectively measured sleep duration (i.e. actigraphy) suggest technology and relies on digital interfaces to interact with the
that self-reports frequently overestimate actual sleep duration. world. It is not surprising that technology devices such as
computers, cell phones, video games, tablets, and e-readers are
⁎ Corresponding author: Ann E. E. Johansson being used by adolescents prior to bedtime. In a study of
E-mail address: anj43@pitt.edu. adolescents recruited from a pediatric office (N = 100, ages

http://dx.doi.org/10.1016/j.pedn.2016.04.004
0882-5963/© 2016 Elsevier Inc. All rights reserved.
Adolescent Sleep and Impact of Technology Use 499

12–18-years), the majority (66%) had a television in their associated with an increased rate of motor vehicle accidents
bedroom, almost 30% had a computer, and 90% had a cell (Martiniuk et al., 2013; Owens, 2014; Pizza et al., 2010).
phone (Calamaro et al., 2009). In another, 76% of adolescents It remains unclear if technology use before bed affects
reported using a cell phone before sleep for playing games, daytime function (wake-time, refreshment, and daytime
surfing the Internet, and texting (Fossum et al., 2014). sleepiness). Previous studies of adolescent sleep and technology
Adolescents have been found to simultaneously engage with use have used small sample sizes and narrow age ranges within
an average of four technology devices after 9:00 pm. This adolescence. The purpose of this study is to describe sleep in
usage was positively correlated with severity of insomnia adolescents, examine associations between use of technology in
symptoms (Pilcher, Ginter, & Sadowsky, 1997). Engaging in a the hour before sleep and daytime function, and to compare
higher number of technologies, such as computer games and technology use in adolescents who report adequate sleep with
television, at bedtime has been associated with less nighttime those who report inadequate sleep.
sleep and more daytime sleepiness (Eggermont & Van Den
Bulck, 2006). Methods
Adolescent sleep quantity is also greatly influenced by Parent Study and Sampling Methodology
biological processes. The two-process model of sleep regula- The National Sleep Foundation's 2011 Sleep in America
tion describes sleep propensity (the likelihood of falling asleep) Poll (National Sleep Foundation, 2011) was a cross-sectional
as the interaction between a homeostatic process (sleep need) survey of a nationally-representative, random sample of
and a circadian process (sleep-wake rhythm or “biological 1,508 Americans aged 13–64 years. The survey, which was
clock”). The homeostatic process increases sleep propensity developed by a panel of sleep experts, examined the use of a
while awake, and decreases it during sleep; while the circadian range of technologies in the bedroom.
process is independent of sleep/wake state. The intersection Data were collected using telephone (n = 750) and Internet
between homeostatic and circadian processes determines (n = 758) surveys. Telephone surveys were performed by
wake-time (Borbély, Daan, Wirz-Justice, & Deboer, 2016). random digit dialing (SDR Consulting Inc., Atlanta, USA)
The rate at which homeostatic sleep propensity accumulates based on population sampling quotas by United States region.
varies between prepubertal and postpubertal stages, and is The telephone survey took approximately 18 min to complete.
correlated with secondary sex development (Carskadon et al., Internet surveys were distributed to members of an e-rewards
1979). There is evidence to suggest that older adolescents panel. Maximum sampling error was ± 2.5 percentage points
accumulate sleep propensity more slowly, resulting in a later (95% CI) for the total sample. Incentives were not offered by the
bedtime and a later preferred wake-time (Jenni, Achermann, & National Sleep Foundation (NSF) for completion of the survey.
Carskadon, 2005; Wolfson & Carskadon, 1998). This presents De-identified data and details about study methodology
a challenge on weekdays, when early school times prevent were acquired from the NSF. The subsample studied was
adolescents from obtaining the sleep that they need. Other composed of 255 adolescents, aged 13–21 years from across
elements of the contemporary adolescent lifestyle, such as the United States. Because one of the aims of this study is to
stress, anxiety, social pursuits, and caffeine use may interact describe sleep through adolescence, the subsample was
with biological processes to further exacerbate sleep irregu- divided into younger adolescents (13–17-years, n = 140) and
larities within this age group (Astill, Van der Heijden, Van older adolescents (18–-21-years, n = 118). Maximum
Ijzendoorn, & Van Someren, 2012; Calamaro, Yang, Ratcliffe, sampling error for the subsample was ± 7.5 percentage points
& Chasens, 2012). (95% CI). The institutional review board at the University of
Insufficient sleep in adolescence has been linked to Pittsburgh approved this secondary analysis of the NSF data.
negative physiological consequences, including an increased
risk of obesity and metabolic dysfunction; and psychological Survey Instrument
and behavioral consequences, such as an increased risk of The survey instrument was designed by content experts in
anxiety, depression, mood disturbances, suicidal ideation, and sleep across the life-span. Questions fit into four categories:
drug and alcohol use (Chen, Beydoun, & Wang, 2008; Gupta, demographics, sleep habits, sleep quality, and technology use
Mueller, Chan, & Meininger, 2002; Lowry et al., 2012). in the hour before bed and during the night. Demographic
Adolescents with poor sleep quality and decreased sleep items included age, gender, race, ethnicity, and school and/or
duration report a lower sense of well-being and a decreased employment status. Sleep habits included bedtime, wake-time,
quality of life (Pilcher et al., 1997). Chronic sleep loss in sleep duration, and naps on week days and weekend days.
adolescents has also been linked to poor judgment, lack of Bedtimes were recoded as “early” (7:00–8:59pm), “standard”
motivation, and inattention (Gradisar, Terrill, Johnston, & (9:00–10:59pm), “late” (11:00pm-1:59am), and “very late
Douglas, 2008; Owens, 2014; Wolfson & Carskadon, 1998), “(2:00–5:00am). Rise times were recoded as “early” (before
and consequently with an increase in risk-taking behaviors, 6:00am), “standard” (6:00–8:59am), “late” (9:00–11:59am),
such as drinking and driving, smoking, and delinquency and “very late” (after 12:00pm). Subjective sleep quality and
(Catrett & Gaultney, 2009; O’Brien & Mindell, 2005). Driving daytime function for a two-week period were assessed using a
while drowsy is a frequent complaint among older adolescents, 4-item Likert-type scale—1 (never) to 4 (every night or almost
and daytime sleepiness in this demographic has been every night)—reporting how often in the last two weeks the
500 A.E.E. Johansson et al.

participant responded they “had difficulty falling asleep,” “woke Table 1 Comparison of sample characteristics in adolescents
during the night,” “woke too early,” and “woke unrefreshed.” reporting adequate and inadequate sleep
Other variables included time needed to fall asleep, difficulty Total Adequate Inadequate p
falling asleep, number and length of awakenings during the sample a sleep b sleep c
night, and if adolescents believe their weekday routine allows
Mean age in 17.1 (b 0.01) 17.1 (2.6) 17.1 (2.5) ns
for “adequate” or “inadequate” sleep duration.
years (SD)
The survey included questions from the Epworth Sleepi- 13–17 yrs 136 (53.3) 76 (51.0) 60 (56.7) ns
ness Scale (ESS), a validated 8-item self-report questionnaire 18–21 yrs 119 (46.3) 73 (49.9) 46 (43.4)
about subjective sleep propensity in different situations (Johns, Gender d
1991). In initial studies, Johns (1992) found that the ESS has Male 133 (52.2) 93 (62.4) 40 (37.7) b 0.01
strong test–retest reliability (ρ = 0.82) and internal consistency Female 122 (47.8) 56 (37.6) 66 (62.3)
(ɑ = 0.88), and in factor analysis consists of only one factor. Race d
It has since been validated and used in a wide range of Caucasian 171 (68.1) 103 (70.5) 68 (64.2) ns
populations, and translated in several different languages. African 21 (8.6) 15 (10.3) 6 (5.7)
Items are scored from 0 (“no chance of dozing”) to 3 (“high American
Asian 33 (12.8) 13 (8.9) 20 (18.9)
chance of dozing”), and summed for a total score. Scores above
Other 9 (3.5) 17 (16.0) 25 (23.6)
10 indicate excess daytime sleepiness. Finally, questions
Hispanic d 30 (11.8) 18 (12.2) 12 (11.3) ns
pertaining to technology use in the hour before going to bed or Occupation d
during the night focused on if and how frequently per week ten Student 210 (82.4) 124 (83.2) 86 (81.1) b 0.01
technologies were used. Follow-up questions included where Employed 14 (5.4) 13 (8.7) 1 (0.9)
and what type of TV shows were watched; the type and content Both 28 (10.8) 11 (7.4) 17 (16.0)
of video games played (violence, crude humor, etc.), and what Neither 3 (1.2) 1 (0.7) 2 (1.9)
functions were used on the cell phone and computer (surf Survey method d
Internet, watch videos, etc.). Phone 77 (30.2) 58 (38.9) 19 (17.9) b 0.01
Web 178 (69.8) 91 (61.1) 87 (82.1)
Statistical Analysis a
N = 255.
b
Analyses focused on weekday sleep habits. Descriptive n = 149.
c
statistical analyses were used to characterize respondents, n = 106.
d
Data are n (%) unless otherwise indicated.
weekday sleep habits, and frequency of technology use.
Descriptive statistics were analyzed as means and standard
deviations for continuous variables, and frequencies and
percentages for categorical variables. The decision to use majority of respondents were non-Hispanic Caucasian, with a
parametric versus non-parametric tests was based on level smaller proportion of African-Americans (12%) and Hispanics
of measurement. Comparisons were made using indepen- (7%) than expected based on 2010 census data (United States
dent Student's t tests for continuous variables, Mann– Census Bureau, 2010). Most respondents completed the
Whitney U for ordinal variables, and Fisher's exact test for survey via the Internet (70%). The majority of adolescents
categorical variables. Relationships between dependent and were full-time students (82%); more than 10% were both in
independent variables were investigated using Pearson's r school and working. There were no significant differences
for continuous variables, and Spearman's rho for ordinal between those reporting “adequate” or “inadequate” sleep in
variables. Frequencies coded using the 4-item Likert-type terms of age or race. Respondents reporting “inadequate” sleep
scale were collapsed to make a binary variable (“never/ were significantly more likely to be female, both work and go
rarely,” “a few nights a week/almost every night/every night”). to school, and complete the survey via the web.
Group comparisons were made between adolescents who
reported that their weekday routine allows for “adequate” or Sleep Characteristics
“inadequate” sleep duration. Only significant correlations Description of Sleep Patterns
≥ 0.15 are reported. The level of significance was set at Sleep characteristics are described in Table 2. Mean sleep
p b 0.05. Statistical analyses were performed on IBM SPSS duration for this sample was 7.3 ± 1.3 h. The average sleep
Version 23.0. duration decreased steadily from 8.3 h at age 13 to 6.7 h at age
19, except for a spike at age 17 to 7.4 h. After age 19, sleep
Results duration began increasing again to 7.6 h at age 21. No
Sample significant difference existed in sleep patterns between males
The characteristics of the full sample (N = 255), and of the and females, except that females were significantly more likely
subsamples of respondents categorized by subjective report of to report “woke unrefreshed” (p b 0.05). There was no
“adequate” or “inadequate sleep,” are shown in Table 1. The significant difference in sleep duration between students and
sample of adolescents was well-balanced by gender with the participants who worked. During the week, one-half of
number of males and females almost equal (males: 52%). The respondents went to bed “late” (11:00pm and 2:00am), and
Adolescent Sleep and Impact of Technology Use 501

Table 2 Comparison of daytime function between p b 0.01). Significantly more adolescents reporting “inadequate”
adolescents reporting adequate and inadequate sleep sleep reported driving drowsy once or more per week than those
Total Adequate Inadequate p reporting “adequate” sleep (p b 0.01).
sample a sleep b sleep c
Technology Use at Bedtime
Mean sleep 7.3 (1.3) 7.7 (1.4) 6.8 (1.4) b 0.01
Use of technology at bedtime was virtually universal with
duration: (SD)
Excess daytime 64 (25.1) 28 (18.8) 36 (34.0) b 0.01 97% of respondents using some type of technology in the hour
sleepiness d, f before sleep. The most commonly used devices were the cell
Woke too early e, f 64 (25.5) 34 (23.0) 30 (29.1) ns phone (74%), computer (69%), music device (iPod, mp3
Woke unrefreshed e, f 171 (67.1) 85 (57.0) 86 (81.1) b 0.01 player) (61%), and television (55%). Almost half (47%) of
a
N = 255. adolescents used 3 or 4 technologies before bed; as many as
b
n = 149. 10% of the sample used six or more devices. Males were
c
n = 106. significantly more likely than females to play video/computer
d
ESS N 10. games, while females were significantly more likely than
e
“A few nights per week/every or almost every night.”
f males to text, talk on the phone, use social media, receive or
Data are n (%) unless otherwise indicated.
send personal email, and use a word processor (all p b 0.05).
The only difference in technology use between younger and
older adolescents was greater use of music devices by younger
9% percent went to bed “very late” (after 2:00am). These adolescents (p b 0.05). Individuals reporting “inadequate”
bedtimes do not leave enough time for sleep when the majority sleep were significantly more likely than those reporting
(58%) of respondents woke before 7:00 am. Significantly older “adequate” sleep to text, use the Internet and social media, and
adolescents went to bed after 11 pm and woke after 9 am than use a word processor before bed (p b 0.05).
younger adolescents (p b 0.01). Although about half of The number and type of devices used in the hour before sleep
respondents were able to fall asleep in less than fifteen minutes, was associated with daytime function. The number of devices
almost a third (30%) required more than half an hour. About used, television, digital music players, and the phone were
60% of adolescents report that they rarely or never “woke significantly associated with the response “woke too early”
during the night”, and those who did reported an average of (rho = 0.16 to 0.23, p b 0.05). Use of the Internet was
28.2 ± 32.4 min awake. Only a quarter of respondents reported significantly associated with frequency of the response, “woke
they frequently “woke too early.” Approximately 67% of the feeling unrefreshed” (rho = 0.16, p b 0.05). Use of the Internet,
sample responded that they “woke unrefreshed” a few days a social media, games with crude humor or violence, personal
week to every day. Approximately 20% of respondents had email, videos on mobile devices, instant messaging or Skype,
excess daytime sleepiness as indicated by an ESS score N 10 and the phone were significantly associated with excess daytime
(mean ESS score = 7.5 ± 4.2). A modest negative correlation sleepiness (rho = 0.15 to 0.31, p b 0.05). The frequency of being
(r = − 0.16, p b 0.05) exists between age and ESS score awoken by a cell phone was significantly associated with all
showing that as adolescents age, daytime sleepiness tends to three outcomes (rho = 0.18 to 0.23, p b 0.05).
decrease. As many as 40% of adolescents who drive admit to
driving while drowsy at least once in the past month, and 18% Discussion
report driving while drowsy at least once per week. Age was Adequate sleep is vital for optimal physical and mental
significantly positively correlated with frequency of the health, growth, learning, memory, and peak academic
response “woke too early” (r = 0.15, p b 0.05). performance in children and adolescents. However, data
from the NSF's 2011 Sleep in America Poll revealed that
“Adequate” and “Inadequate” Sleep Duration adolescents aged 13–21-years are reporting average sleep
Significant differences exist between adolescents reporting duration an hour or more below that recommended by the
“adequate” and “inadequate” sleep in weekday sleep duration, American Academy of Pediatrics (American Academy of
bedtime, daytime sleepiness, and frequency of reporting “woke Pediatrics, 2014) and the NSF (Hirshkowitz et al., 2015). This
unrefreshed.” Those reporting “inadequate” sleep had almost is exacerbated by many participants' late bedtime, and having
one hour less sleep than those reporting “adequate” sleep to wake early for work or school. Consequently, almost half
(p b 0.01). The most common bedtime of respondents reporting report not getting “adequate” sleep. Although there was no
“adequate” sleep was between 10:00 to 10:59 pm, while the significant difference in sleep duration between younger and
most common bedtime of respondents reporting “inadequate” older age groups, older adolescents tended to go to bed later
sleep was between 11:00–11:59 pm (p b 0.05). Adolescents and wake up later, consistent with changes in sleep patterns as
reporting “inadequate” sleep were significantly more likely to adolescents age (Wolfson & Carskadon, 1998). As expected,
also report “woke unrefreshed” a few nights to every night per the group reporting “inadequate” sleep reported significantly
week (p b 0.01), and significantly more likely to report daytime later bedtime, shorter mean sleep duration, and a greater
sleepiness. Almost twice as many reporting “inadequate” frequency of the response “woke unrefreshed” than those
sleep had scores above the cutoff of ESS N 10 (34% vs. 19%, reporting “adequate” sleep. Although the mean ESS score for
502 A.E.E. Johansson et al.

both groups was below the cutoff for problematic excess routine, it is difficult to comment on how those reporting
daytime sleepiness (ESS N 10), almost twice as many of those “adequate” and “inadequate” sleep differed in their daytime
reporting “inadequate” sleep had scores above the cutoff. In duties. Although those reporting “inadequate” sleep blame it on
addition, it is concerning that significantly more adolescents their daily routine, this group may be underestimating the
reporting “inadequate” sleep also reported driving drowsy influence of technology use on sleep habits and daytime
once or more per week. function. For instance, individuals reporting “inadequate” sleep
Data from the poll also show that, for almost all adolescents, were significantly more likely to also report frequent use of
some form of technology was present and used in the bedroom cognitively stimulating technologies—the Internet, social
in the hour before sleep. In more than half the sample, media, and texting, in the hour before sleep. “Inadequate”
participants used three or more forms of technology within the sleep appears to be a problem across adolescence, as there was
hour before bed, which is noteworthy as the number of devices no significant difference in the number reporting “inadequate”
used was significantly associated with the response, “woke too sleep between younger and older adolescents.
early.” Consistent with other reports (Hysing et al., 2015; Van
den Bulck, 2007), phones were the most frequently used Implications for Nursing
technology before bed, especially among females and those The United States Office of Disease Prevention and
reporting “inadequate” sleep; and use before bed was Health Promotion in their Healthy People 2020 initiative has
correlated with the response “woke too early” and excess set as a goal sufficient sleep duration in high-school aged
daytime sleepiness. In the present study, about a third of adolescents (Office of Disease Prevention and Health
adolescents reported their cell phone waking them a few times Promotion, 2015). Nurses are in the ideal position to address
per week to every night. This was similar to a report of 23% of this goal by educating adolescents, their families, and the
high school students being woken at least once a week by their broader community about good sleep hygiene. Nurses in
phones (Van Den Bulck, 2003). Being awoken by a phone primary care or outpatient clinics should inquire about an
results in shorter total sleep duration, and lighter sleep as the adolescent's sleep habits, sleep quality, and technology use
body is continually aroused (Munezawa et al., 2011; Van Den before bed. In addition, nurses should take the opportunity to
Bulck, 2003; Van den Bulck, 2007). Also consistent with counsel families in healthy sleep habits. School nurses are
findings from the present study are those showing that males well-placed to instruct adolescents in proper sleep hygiene.
are more likely to play video games before bed (Hysing et al., Previous studies found that elementary-school aged and
2015), and that prolonged play of video games is associated younger children are also using technology before bed.
with shorter and less efficient sleep (King et al., 2013). Therefore, school nurses should begin sleep hygiene
Surprisingly, the strength of association between video games education as early as possible. Wing et al. (2015) have
and next-day function was stronger for games with crude shown that an in-school sleep education program is feasible,
humor than those with violence and blood. and has a positive effect in reducing two consequences of
Several mechanisms have been proposed to explain insufficient sleep, hyperactivity and conduct problems.
how technology use affects sleep. One mechanism is
that light, especially short wave-length light, emitted Limitations
from screens may alter circadian processes such as melatonin This study had several factors that may limit generalizing
release (Cajochen et al., 2011; Chellappa et al., 2011; van der the results. First, data were drawn from a larger cross-sectional
Lely et al., 2015). The findings of the present study may lend study of sleep and technology use which limited analyses to the
support to this theory, as all the technology devices found to affect questions asked by the original researchers. The sample of
daytime function, except for traditional phones, have adolescents 13–21-years-old was fairly small, and correlations
light-emitting screens. A second mechanism proposed is that between technology and daytime function were modest. No
cognitive and physiologic arousal from stimulating technologies objective evaluation of sleep or technology use was obtained.
such as video games, computers, or cell phones may make it Thus, data may have been subject to recall bias, or under- or
difficult for the body to “wind-down”. Several studies found that over-estimation of sleep or technology use. In addition, it is
TV, video games, cell phones, music, computers, and social possible that adolescents with difficulty sleeping may use more
media were significantly related to difficulty falling asleep technology while they are awake. This may inflate significant
because of stimulation (Arora et al., 2014; Weaver, Gradisar, correlations between technology use and daytime function.
Dohnt, Lovato, & Douglas, 2010), and the present study found Because of the broad age range of the parent study, the ESS
associations between these devices and excess daytime sleepi- originally developed for adults was used for all participants.
ness, and the responses “woke too early” and “woke unrefreshed.” Some items, (ex. “…in a car, while stopped for a few minutes in
The present study explored the survey question inquiring if the traffic”) may not have applied to all adolescents.
participants' daytime routine allowed for “adequate” or
“inadequate” sleep. “Inadequate” sleep may truly be because Conclusion
of daytime routine, because of demographic characteristics, or it Inadequate sleep is widespread among adolescents, and
may be because of sleep habits and/or technology use. Because technology has been implicated as a potential factor in
the parent study did not ask for details about participants' daily adolescent sleep deprivation. The use of technology, and the
Adolescent Sleep and Impact of Technology Use 503

number of devices used in the hour before sleep were Adolescent Health, 46, 399–401. http://dx.doi.org/10.1016/j.jadohealth.
significantly associated with problems in daytime function, 2009.10.011.
Eggermont, S., & Van Den Bulck, J. (2006). Nodding off or switching off?
increased frequency of waking too early, waking unre- The use of popular media as a sleep aid in secondary-school children.
freshed, and daytime sleepiness. This study suggests that Journal of Paediatrics and Child Health, 42, 428–433. http://dx.doi.
adolescents need to be helped in developing good sleep org/10.1111/j.1440-1754.2006.00892.x.
habits, and that one way to improve sleep in this age group is Fossum, I. N., Nordnes, L. T., Storemark, S. S., Bjorvatn, B., & Pallesen, S.
(2014). The association between use of electronic media in bed before
to encourage disengagement from electronic devices during
going to sleep and insomnia symptoms, daytime sleepiness, morning-
the hour before bed. ness, and chronotype. Behavioral Sleep Medicine, 12, 343–357. http://
dx.doi.org/10.1080/15402002.2013.819468.
National Sleep Foundation (2011). 2011 Sleep in America Poll: Commu-
Acknowledgments nications Technology in the Bedroom. D.C.: Washington (Retrieved
Data were used with permission from the National Sleep from www.sleepfoundation.org/2011poll).
Foundation. Support for the first author (AEEJ) was Gradisar, M., Terrill, G., Johnston, A., & Douglas, P. (2008). Adolescent
provided by the National Institutes of Nursing Research sleep and working memory performance. Sleep and Biological Rhythms,
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