My research topic question is "What affect does overexposure of Mobile phone have on Toddlers?" I have attached instruction on how to write this paper. I have also attached the example essay below to
The Relation Between Use of Mobile ElectronicDevices and Bedtime Resistance, Sleep Duration, and
Daytime Sleepiness Among Preschoolers
Amy I. Nathanson
School of Communication,
The Ohio State University, Columbus, Ohio, USA
Ine Beyens
Amsterdam School of Communication Research,
University of Amsterdam, Amsterdam, The Netherlands
This study investigated the relation between preschoolers ’mobile electronic device (MED) use and
sleep disturbances. A national sample of 402 predominantly college-educated and Caucasian mothers
of 3 –5-year-olds completed a survey assessing their preschoolers ’MED use, bedtime resistance,
sleep duration, and daytime sleepiness. Heavier evening and daily tablet use (and to some extent,
smartphone use) were related to sleep disturbances. Other forms of MED use were not consistently
related to sleep disturbances. In addition, playing games on MEDs at bedtime was related to
compromised sleep duration, although other forms of MED use at bedtime were not related to
sleep outcomes. Although the relations between MED use and sleep disturbances were small in size,
they were larger than the relations between sleep and other predictors in the models. Continued work
should investigate how MED exposure is related to children ’s cognitive, psychological, emotional,
and physiological development, particularly given the popularity and widespread use of these
devices.
Children are increasingly consuming media via mobile electronic devices (MEDs), which allow
them to watch television programs, read books, or play games in practically any physical space
(Common Sense Media, 2013 ). MEDs are unique compared to traditional toys or television
because they offer a combination of features, such as interactivity, reactivity, and portability
(Christakis, 2014 ), which can provide a compelling user experience. But what effects do MEDs
have on children? Experts remain cautiously optimistic that MEDs could benefit children
because the devices encourage interactivity and personal adaptability compared with television
Correspondence should be addressed to Amy I. Nathanson, School of Communication, The Ohio State University, Columbus, OH 43210. E-mail: [email protected]
Behavioral Sleep Medicine , 16:202 –219, 2018 Copyright © Taylor & Francis Group, LLC ISSN: 1540-2002 print/1540-2010 onlineDOI: 10.1080/15402002.2016.1188389
202 viewing (Kirkorian & Pempek, 2013 ). However, MED exposure may have detrimental effects as
well. In particular, MEDs emit short-wavelength blue light that may interfere with young
children ’s sleep (Salti et al., 2006 ). The purpose of this study was to investigate this possibility
and to examine whether MEDs pose a unique risk for disturbed sleep among preschoolers.
MEDs include a number of media devices, such as tablet computers, smartphones, laptop
computers, handheld electronic game players, and iPods. Parents appear to find MEDs valuable,
as evidenced by recent reports documenting children ’s access to and use of MEDs. For example,
according to a national survey of parents of children ages 8 and younger, 40% of children have
access to tablet devices, 63% have access to smartphones, and 75% of children have access to
some MED (Common Sense Media, 2013 ). In addition, children may model their parents ’MED
use since higher MED use among parents is related to higher MED use among children
(Lauricella, Wartella, & Rideout, 2015 ). According to Common Sense Media ’s( 2013 ) report,
children ages 0 to 8 spent an average of 15 min a day with MEDs, a threefold increase in time
since 2011. In their study, although television was the preferred medium for delivering educa-
tional content, 38% of children often or sometimes viewed educational content via MEDs. Other
popular activities performed on MEDs, as noted by Common Sense Media ( 2013 ), included
playing games for fun, watching videos, and reading books.
At this time, little research has been conducted on the effects of MEDs on children.
Regardless, debates over the benefits and harms of MEDs —especially tablets —for children
have emerged in the popular media (e.g., Dobrow, 2014 , January 21) and scholarly writings
(e.g., Christakis, 2014 ). Some consider tablet devices to be superior to television because they
can be used interactively and adapted to the needs of the user, which could theoretically promote
learning (Kirkorian & Pempek, 2013 ). Some scholars have begun to rethink their position on
media use among young children, seriously considering the possible benefits of interactive tablet
devices for this population (Christakis, 2014 ).
Although children enjoy using tablet devices (Couse & Chen, 2010 ), most research suggests
that tablets do not offer a superior learning platform compared with books (Dundar & Akcayir,
2012 ) and may detract from preschoolers ’learning (Krcmar & Cingel, 2014 ; Parish-Morris et al.,
2013 ). In a study of preschoolers, Krcmar and Cingel ( 2014 ) found that electronic versions of
books prompted more conversations about electronic features than story content. However,
Masataka ( 2014 ) showed that preschoolers who received intensive exposure to a digital version
of a book learned more than children who received the same amount of exposure to a
printed one.
Although the current debate surrounding MEDs ’effects on children has centered on its
educational potential, MED use may have other consequences for children. In particular, since
there is evidence that screen time disrupts children ’s sleep (Cain & Gradisar, 2010 ; Hale &
Guan, 2015 ), MEDs may disturb sleep as well. Prior work has found that exposure to televi-
sions, computers, and mobile phones is related to poor sleep quality among both preschool-aged
and school-aged children as well as adolescents (Arora, Broglia, Thomas, & Taheri, 2014 ;
Garmy, Nyberg, & Jakobsson, 2012 ; Garrison, Liekweg, & Christakis, 2011 ; Hale & Guan,
2015 ; Polos et al., 2015 ), especially when exposure occurs in the evening (Garrison et al., 2011 ).
Most of the work on screens and sleep has been conducted among adolescents and adults
(Fossum, Nordnes, Storemark, Bjorvatn, & Pallesen, 2014 ; Kubiszewski, Fontaine, Rusch, &
Hazouard, 2014 ; Nuutinen et al., 2014 ; Pieters et al., 2014 ; Van den Bulck, 2004 ); however,
MOBILE ELECTRONIC DEVICES AND SLEEP 203 there is evidence that the relation between media exposure and sleep exists among younger
children as well (Garmy et al., 2012 ; Marinelli et al., 2014 ).
There are several reasons why MED use could affect sleep. Self-luminous, electronic screens,
such as televisions, computers, and MEDs, emit short-wavelength, or blue, light that is known to
suppress the brain ’s melatonin secretion among both adults (Wood, Rea, Plitnick, & Figueiro,
2013 ) and children (Salti et al., 2006 ). The release of melatonin occurs in the evening, in
response to decreased or dim light exposure, and helps prepare the body for sleep (LeBourgeois
et al., 2013 ). Consequently, it is not surprising that Chellappa et al. ( 2013 ) found that evening
exposure to blue light was related to poorer-quality sleep. Moreover, blocking blue light from a
computer screen via special glasses can reduce melatonin secretion suppression and otherwise
increase the potential for better sleep among adolescents (van der Lely et al., 2015 ). Ocular light
exposure plays a critical role, as extraocular light exposure (e.g., behind the knees) does not
suppress nocturnal melatonin secretion in humans (Hebert, Martin, & Eastman, 1999 ). As a
result, bright-light displays from MEDs are important to consider as potential disruptions to
children ’s sleep quality since light is absorbed through the eyes. Moreover, children may be
especially vulnerable to the effects of evening light because of their larger pupil size (Higuchi,
Nagafuchi, Lee, & Harada, 2014 ).
In addition, MEDs provide opportunities to engage in arousing activities, such as playing
games and sending and receiving text messages. When these activities occur at bedtime, the
increased arousal could harm children ’s transition to sleep (Cain & Gradisar, 2010 ; Hale &
Guan, 2015 ). A few studies have found that adolescents who send and receive texts, or engage in
media multitasking, at bedtime sleep fewer hours than other adolescents (Munezawa et al., 2011 ;
Van den Bulck, 2007 ). With a sample of adults, Higuchi, Motohashi, and Maeda ( 2005 ) found
that playing computer games uniquely interfered with sleep compared with other computer-
based activities. As a result, MEDs may provide a variety of arousing activities that, when
consumed at bedtime, could delay sleep onset.
Although nonmobile media (i.e., televisions, desktop computers) also emit blue light and can
transfer arousing material, MEDs may pose a unique threat to children ’s sleep. First, their
portability means that MEDs may be used in conjunction with bedtime routines in a child ’s
bedroom. Increasingly, parents are using MEDs to read stories to their children (Common Sense
Media, 2013 ). Moreover, parents may try to entice their children into bed by engaging in other
activities available via MEDs, such as playing music or games. As a result, children may receive
a dose of short-wavelength light immediately before going to bed. Overall, the portability and
versatility of MEDs may promote increased use among children, especially during time periods
where blue-light exposure may be detrimental to sleep. In this way, evening MED use might be
distinctly related to children ’s sleep disturbances, above and beyond the influence of television
viewing.
Second, MEDs are held close to the face, which theoretically could exacerbate the effects of
short-wavelength light exposure (Calamaro, Mason, & Ratcliffe, 2009 ). Televisions are viewed
at a distance, thereby allowing blue light intensity from the source to the viewer to decay
(Calamaro et al., 2009 ). However, the opposite situation occurs when children use MEDs. These
devices are held just inches from the face, which may allow children to absorb more concen-
trated levels of blue light. In fact, Figueiro, Wood, Plitnick, and Rea ( 2013 ) found that light from
television was not strong enough to suppress melatonin among adults. In addition, a recent meta-
analysis showed that computer and mobile phone use were related to adolescents ’disrupted
204 NATHANSON AND BEYENS sleep but television viewing was not (Bartel, Gradisar, & Williamson, 2015 ). Other research has
found that the intensity of light exposure affects the degree of disruption to circadian rhythms
(McIntyre, Norman, Burrows, & Armstrong, 1989 ). From this perspective, MED exposure could
bear a stronger relation with children ’s sleep compared with nonmobile media use.
Our study offers a first look at the relations between MED exposure, television exposure, and
sleep disturbances among preschoolers. Understandably, due to MEDs ’relatively recent widespread
adoption, a very small percentage of the research to date has examined MED use, with no studies
examining preschoolers ’MED use and sleep. Given the importance of sleep to children ’sdevelop-
ment, and the rapid adoption and use of MEDs among both parents and children, we investigated
whether MED use was related to preschoolers ’bedtime resistance, sleep duration, and daytime
sleepiness. As Hale and Guan ( 2015 ) noted, due to rapidly evolving screen media, it is important to
consistently monitor whether and how various types of screen technologies affect sleep. Moreover,
very little research has examined how screen time is related to sleep among preschoolers.
Although we focused on MEDs due to their novelty, we aimed to understand whether MEDs
are uniquely related to sleep, above and beyond television exposure. Given the plethora of
MEDs available, we examined multiple forms of MED use, including use of tablets, smart-
phones, video iPods, game players, and laptop computers. We also used a multidimensional
conceptualization of sleep disturbances and focused on indicators of sleep disturbances prior to
sleep time (i.e., bedtime resistance), during sleep (i.e., duration of nighttime sleep), and after
sleep (i.e., daytime sleepiness). We expected that preschoolers ’overall MED use will be related
to greater bedtime resistance (H1a), compromised sleep duration (H1b), and more daytime
sleepiness (H1c), even after accounting for preschoolers ’television exposure time.
In addition to examining overall MED use, we explored how MED use in the evening would
be related to preschoolers ’sleep disturbances. Melatonin is released in the brain at nighttime in
order to prepare the body for sleep; as a result, evening MED use may be particularly disruptive
to sleep. We predicted that preschoolers ’use of MEDs in the evening will be related to more
bedtime resistance (H2a), compromised sleep duration (H2b), and more daytime sleepiness
(H2c), after controlling for preschoolers ’evening television exposure.
Finally, we investigated whether engaging in certain types of activities via MEDs is better or
worse for children ’s sleep. We asked whether game playing, reading, or watching television
programs on MEDs at around bedtime was uniquely related to preschoolers ’sleep disturbances,
above and beyond the influence of preschoolers ’evening television exposure (RQ). Figure 1
presents a conceptual diagram of the hypothesized relationships.
METHODS
Participants and Procedures
A national sample of 402 mothers of 3-, 4-, and 5-year old children in the United States was
gathered for this research using a national panel company. Respondents were required to meet
several eligibility criteria, which included being the mother of a 3 –5-year-old child who was not
born prematurely, weighed at least 5 pounds at birth, who has not been diagnosed with a serious
congenital anomaly or significant birth defect, and who did not experience any major trauma
during delivery.
MOBILE ELECTRONIC DEVICES AND SLEEP 205 On average, the participating mothers were 34.5 years old ( SD = 8.7) and married or
cohabitating (80%). Eighty percent reported their ethnicity as Caucasian, followed by African
American (7%), Hispanic (6%), Asian or Pacific Islander (4%), multiracial (2%), and Native
American (0.5%). Based on a recent report of the population characteristics of families with
children under the age of 18 (U.S. Department of Health and Human Services, 2014 ), this
suggests that our sample was overrepresented by Caucasians and underrepresented by African
Americans and Hispanics. Slightly more mothers reported on male children (52%) than female
children (48%). More information on the demographic characteristics of the sample is reported
in the section on covariates.
Participants completed a 20-min online survey. They were instructed to complete the ques-
tions with their 3 –5-year-old child in mind. If participants had more than one 3 –5-year old child,
they were instructed to select one of their children and to respond consistently with this child in
mind.
Measures
Children ’s overall MED use
With separate questions for each medium, parents reported how many hours their child uses a
tablet, a smartphone, a video iPod, a handheld game player, and a laptop computer on a typical
Children's Sleep Disturbances
Bedtime resistance
Sleep duration
Daytime sleepiness
Children's Overall MED Use
Ta b l e t u s e
Smartphone use
Game player use
Laptop use
iPod use
Children's Evening MED Use
Ta b l e t u s e
Smartphone use
Game player use
Laptop use
iPod use
Children's MED Activities at Bedtime
Reading on MEDs
Playing games on MEDs
Viewing TV programs on MEDs
FIGURE 1 Conceptual diagram of the hypothesized relationships.
206 NATHANSON AND BEYENS weekday and on a typical weekend day during three time periods: morning (defined as “from the
time the child awakens until lunch time ”), afternoon (defined as “between lunch and dinner
time ”), and evening (defined as “between dinner and bedtime ”). Participants were provided with
a list of twelve 30-min increments of time (e.g., “not at all, ”“ 1.5 hours, ”“ 3 hours, ”“ more than
5 hours ”). Consequently, parents responded to a total of six media use questions for each
medium (e.g., average weekday tablet use in the morning, afternoon, and evening and average
weekend tablet use in the morning, afternoon, and evening).
For each MED, we translated the responses into minutes (e.g., “not at all ”was translated into
0, “1.5 hours ”was translated into 90) and summed across the responses from each day part to
calculate a total exposure time score, in minutes, for weekdays and weekends. For each MED,
we then multiplied the sum for the weekday use by 5 and the sum for the weekend use by 2.
These two products were then summed and divided by 7 to produce average daily use, in
minutes, for each MED ( M = 84.15, SD = 126.69 for average daily tablet use, M = 24.56, SD =
66.48 for average daily smartphone use, M = 35.34, SD = 83.61 for average daily game player
use, M = 30.31, SD = 79.70 for average daily laptop use, M = 10.45, SD = 47.02 for average
daily iPod use). For each MED, average daily use in minutes was converted into average daily
use in hours to be included in the regression models. This approach to assessing and calculating
children ’s media use from parent reports has been used in prior work (e.g., Cespedes et al.,
2014 ). Daily exposure estimates were deemed desirable to account for fluctuations that occur
during the week and so that average exposure could be compared with average sleep
disturbances.
Children ’s evening MED use
Average evening MED use was calculated in the same manner as overall MED use by relying
on respondents ’reports of their child ’s weekday and weekend evening exposure ( M = 23.46,
SD = 39.78 for average evening tablet use, M = 7.10, SD = 22.59 for average evening
smartphone use, M = 8.88, SD = 23.43 for average evening game player use, M = 7.53, SD =
21.92 for average evening laptop use, M = 2.74, SD = 13.32 for average evening iPod use). For
each MED, average evening use in minutes was converted into average evening use in hours to
be included in the regression models.
Chi ldren ’s total television viewing
Parents reported how many hours their child watches television during the morning, after-
noon, and evening on a typical weekday and on a typical weekend day. We calculated daily
television time in the same manner as overall MED use ( M = 228.15, SD = 158.42). The
television viewing estimate, in minutes, was converted into hours to be included in the regres-
sion models.
Children ’s evening television viewing
We calculated measures of average daily evening television viewing in the same manner that
we arrived at estimates for evening MED use ( M = 76.77, SD = 63.15). The evening television
viewing estimate, in minutes, was converted into hours to be included in the regression models.
MOBILE ELECTRONIC DEVICES AND SLEEP 207 Children ’s MED activities at bedtime
Parents were asked to “think about the time right around your 3 –5-year-old child ’s bedtime ”
and then to report, for each MED, (a) how often they read stories to their child on a tablet,
smartphone, video iPod or laptop computer at around the child ’s bedtime (4 questions), (b) how
often their child plays games on a tablet, smartphone, video iPod, handheld game player or
laptop computer at around the child ’s bedtime (5 questions), and (c) how often their child
watches TV shows or movies on a tablet, smartphone, video iPod or laptop computer at around
the child ’s bedtime (4 questions). Response options ranged from 0 days per week (1) to 7 days
per week (8). We calculated the average number of days per week engaged in these activities by
averaging across reports of use on each MED ( M = 1.28, SD = .69 for reading on MEDs, M =
1.38, SD = .70 for game playing on MEDs, M = 1.32, SD = .65 for watching television programs
on MEDs).
Children ’s sleep disturbances
The bedtime resistance, sleep duration, and daytime sleepiness subscales of the Children ’s
Sleep Habits Questionnaire (CSHQ; Owens, Spirito, McGuinn, 2000 ) were used to assess sleep
disturbances. Parents rated the frequency of children ’s sleep behaviors on a 3-point scale
including rarely (0 to 1 time per week), sometimes (2 to 4 times per week), and usually (5 to
7 times per week). Responses to the questions for each subscale were summed to create a scale
for bedtime resistance ( M = 9.21, SD = 3.02, α= .78), sleep duration ( M = 4.15, SD = 1.45, α=
.74) and daytime sleepiness ( M = 11.33, SD = 2.58, α= .68). Higher scores were indicative of
greater bedtime resistance, compromised sleep duration, and more daytime sleepiness,
respectively.
Covariates
In our analyses, we controlled for the age of the child, the number of days the child attends
preschool, the mother ’s education, the mother ’s income, and the mother ’s employment status.
Parents indicated the child ’s age by checking one of three boxes that included “3 years old ”
(coded as 1), “4 years old ”(coded as 2), and “5 years old ”(coded as 3). On average, parents
indicated that their child was 4 years old, M = 2.00, SD = .80. Parents reported the number of
days per week their child attends preschool or goes to a childcare provider by checking one of
eight boxes ranging from “0 days per week ”(coded as 0) to “7 days per week ”(coded as 7). On
average, children attended preschool or went to a childcare provider two days per week, M =
2.59, SD = 2.22. Parents also reported how much education they have received by indicating the
highest level of education they have completed, ranging from “less than high school ”(coded
as 1) to “graduate degree ”(coded as 6). On average, parents had received some college
education, M = 3.50, SD = 1.28. Parents reported whether they were working outside of the
home by indicating “yes ”(coded as 1, 43.3% of parents) or “no”(coded as 2, 56.7% of parents),
indicating that our sample was overrepresented by stay-at-home mothers since about 64% of
mothers of children under the age of 6 in the United States work outside of the home (Bureau of
Labor Statistics, 2015 ). Parents also reported their annual household income by checking one of
208 NATHANSON AND BEYENS eight boxes ranging from “less than $10,000 ”(coded as 1) to “$200,000 or more ”(coded as 8).
Average annual household income ranged between $25,000 and $49,999, M = 4.26, SD = 1.39.
Data Analysis
We used multiple regression analysis to analyze the data. For each analysis, we entered the
child ’s age, frequency of attending childcare, the mother ’s education level, the mother ’s employ-
ment status, and household income into the first block of the equation, as these variables are
typically related to children ’s sleep disturbances, media exposure, or both (Magee, Lee, & Vella,
2014 ; Marinelli et al., 2014 ).
We conducted separate analyses to test whether overall MED use or MED use in the evening
is problematic for sleep, with each analysis controlling for TV viewing either overall or in the
evening in the second block of the regression equation, and entering either overall MED use or
evening MED use for each MED separately in the third block of the equation. Overall MED use
and evening MED use were included in the regression models as average daily use in hours.
Finally, we examined the relation between children ’s sleep disturbances and evening reading
on MEDs, evening game playing on MEDs, and evening television program viewing via MEDs.
Evening MED activities were included in the regression models as average weekly use in
number of days.
The descriptive statistics of all measures included in the study are displayed in Table 1 .No
respondents had missing data for the variables included in the model. The absence of missing
data could have been because the online survey notified respondents when they skipped
questions and asked them whether they wished to continue without answering or not.
RESULTS
Children ’s Overall MED Use and Sleep Disturbances
We found evidence that, even with television viewing controlled, MED use was related to sleep
disturbances among preschoolers ( Table 2 ). First, we found that the block of MED use explained
a significant 4% of additional variance in bedtime resistance beyond the block of control
variables and the block of television viewing. Individually, tablet use was significantly related
to bedtime resistance ( β= .17, p< .01), indicating that more tablet use was related to greater
bedtime resistance. For each standard deviation increase in tablet use, there was a .17 standard
deviation increase in bedtime resistance. Smartphone use was marginally significantly related to
bedtime resistance ( β= .09, p< .10), indicating that for each standard deviation increase in
smartphone use, there was a .09 standard deviation increase in bedtime resistance. Children ’s
game player use, laptop use, and iPod use were not significantly related to bedtime resistance.
Second, we found that children ’s tablet use was significantly related to sleep duration ( β=
.13, p< .01), such that more tablet use was related to compromised sleep duration. For each
standard deviation increase in tablet use, there was a .13 standard deviation increase in
compromised sleep duration. Children ’s smartphone use, game player use, laptop use, and
iPod use were not significantly related to sleep duration.
Third, we found that children ’s tablet use ( β= .09, p< .10) was marginally significantly
related to daytime sleepiness, indicating that more tablet use was related to more daytime
MOBILE ELECTRONIC DEVICES AND SLEEP 209 sleepiness. For each standard deviation increase in tablet use, there was a .09 standard deviation
increase in daytime sleepiness. Children ’s smartphone use, game player use, laptop use, and iPod
use were not significantly related to daytime sleepiness.
For children ’s tablet use, H1 was supported for bedtime resistance (H1a) and sleep duration
(H1b), and weakly supported for daytime sleepiness (H1c). For children ’s smartphone use, H1
was weakly supported for bedtime resistance (H1a), but not supported for sleep duration (H1b)
or daytime sleepiness (H1c). For children ’s game player use, laptop use, and iPod use, H1 was
not supported.
Children ’s Evening MED Use and Sleep Disturbances
We found evidence that, with evening television viewing controlled, evening MED use was
related to sleep disturbances among preschoolers ( Table 2 ). First, we found that the block of
evening MED use explained a significant 5% of additional variance in bedtime resistance
beyond the block of control variables and the block of evening television viewing.
TABLE 1 Descriptive Statistics of All Measures
M SD Observed range α
Mother ’s education 3.50 1.28 1 –6 Household income 4.26 1.39 1–8 Child ’s age 2 .80 1–3 Childcare attendance (days/week) 2.59 2.22 0–7 TV viewing (minutes/day) 228.15 158.42 0–990 Evening TV viewing (minutes/day) 76.77 63.15 0–330 Tablet use (minutes/day) 84.15 126.69 0–754.29 Evening tablet use (minutes/day) 23.46 39.78 0–287.14 Smartphone use (minutes/day) 24.56 66.48 0–805.71 Evening smartphone use (minutes/day) 7.10 22.59 0–278.57 Game player use (minutes/day) 35.34 83.61 0–647.14 Evening game player use (minutes/day) 8.88 23.43 0–180 Laptop use (minutes/day) 30.31 79.70 0–672.86 Evening laptop use (minutes/day) 7.53 21.92 0–137.14 iPod use (minutes/day) 10.45 47.02 0–540 Evening iPod use (minutes/day) 2.74 13.32 0–158.57 Reading on MEDs at bedtime (days/week) 1.28 .69 1–8 Playing games on MEDs at bedtime (days/week) 1.38 .70 1–6.60 Viewing television programs on MEDs at bedtime(days/week) 1.32 .65 1–8
Bedtime resistance 9.21 3.02 6–18 .78 Sleep duration 4.15 1.45 3–9 .74 Daytime sleepiness 11.33 2.58 8–23 .68
Note . Categories for mother ’s education were “less than high school ”;“high school or GED ”;“some college ”; “college degree ”;“some graduate school ”; and “graduate degree ”(coded as 1 –6, respectively). Categories of household income were “less than $10,000 ”;“$10,000 to $14,999 ”;“$15,000 to $24,999 ”;“$25,000 to $49,999 ”;“$50,000 to $99,999 ”;“$100,000 to $149,999 ”;“$150,000 to $199,999 ”; and “$200,000 or more ”(coded as 1 –8, respectively). Categories for child age were “3 years old ”;“4 years old ”; and “5 years old ”(coded as 1 –3, respectively).
210 NATHANSON AND BEYENS TABLE 2
Regression Analysis of the Relation Between Children ’s Overall MED Use and Evening MED Use (Hours per Day) and Bedtime Resistance,
Sleep Duration, and Daytime Sleepiness
Overall MED Use Evening MED Use
Bedtime resistance Sleep duration Daytime sleepiness Bedtime resistance Sleep duration Daytime sleepiness
BSE βBSE βBSE β BSEβBSE βBSE β
Step 1 Step 1
Mother ’s education –.09 .13 –.04 .02 .06 .02 -.09 .11 –.04 Mother ’s education –.08 .13 –.04 .02 .06 .02 –.08 .11 –.04
Mother ’s employment .02 .33 .00 .01 .16 .00 –.77 .27 –.15** Mother ’s employment .00 .33 .00 .01 .16 .00 –.73 .27 –.14**
Household income –.08 .12 –.04 –.02 .06 –.02 –.04 .10 –.02 Household income –.10 .12 –.05 –.03 .06 –.03 –.05 .10 –.02
Child ’s age –.62 .19 –.16** –.10 .09 –.06 .17 .16 .05 Child ’s age –.65 .19 –.17** –.10 .09 –.06 .13 .16 .04
Childcare attendance .05 .07 .04 .03 .04 .04 .25 .06 .22*** Childcare attendance .02 .07 .02 .02 .04 .03 .24 .06 .21***
R
2
.03* .01.11***R
2
.03* .01.11***
Step 2 Step 2
TV viewing .15 .06 .13* .09 .03 .17** .18 .05 .18*** Evening TV viewing .43 .15 .15** .21 .07 .15** .45 .12 .19***
Incr. R
2/R2
.03***/.06** .03***/.04* .05***/.16*** Incr. R
2/R2
.04***/.06*** .03***/.04* .05***/.16***
Step 3 Step 3
Tablet use .24 .07 .17** .09 .04 .13* .11 .06 .09
+
Evening tablet use .79 .23 .17** .27 .12 .12* .26 .19 .07
Smartphone use .25 .14 .09
+
– .01 .07 –.01 .05 .11 .02 Evening smartphone use .91 .40 .11* –.12 .20 –.03 .09 .33 .01
Game player use .02 .12 .01 –.00 .06 –.00 .14 .10 .08 Evening game player use –.14 .41 –.02 .20 .20 .06 .30 .34 .05
Laptop use .18 .13 .08–.01 .06 –.01 –.04 .11 –.02 Evening laptop use .76 .44 .09
+
–.01 .22 –.00 .24 .36 .03
iPod use –.24 .20 –.06 .04 .10 .02 .24 .17 .07 Evening iPod use –.83 .73 –.06 .15 .36 .02 1.12 .60 .10
+
Incr. R
2/R2
.04**/.10*** .02/.06* .02
+/.18*** Incr. R
2/R2
.05**/.12*** .02/.06* .03*/.18***
F 4.05***2.12*7.67***F 4.63***2.24*7.89***
Note.
+p< .10. * p< .05. ** p< .01. *** p< .001.
Analyses control for demographics, childcare attendance, and TV viewing/evening TV viewing.
211 Individually, evening tablet use ( β= .17, p< .01) and evening smartphone use ( β=.11, p< .05)
were significantly related to bedtime resistance, indicating that more evening tablet use and more
evening smartphone use were related to greater bedtime resistance. For each standard deviation
increase in evening tablet use, there was a .17 standard deviation increase in bedtime resistance;
for each standard deviation increase in evening smartphone use, there was a .11 standard
deviation increase in bedtime resistance. In addition, evening laptop use ( β= .09, p< .10)
was marginally significantly related to greater bedtime resistance. For each standard deviation
increase in evening laptop use, there was a .09 standard deviation increase in bedtime resistance.
Children ’s evening game player use and evening iPod use were not significantly related to
bedtime resistance.
Second, we found that evening tablet use ( β= .12, p< .05) was significantly related to sleep
duration, indicating that more evening tablet use was related to compromised sleep duration. For
each standard deviation increase in evening tablet use, there was a .12 standard deviation
increase in compromised sleep duration. Children ’s evening smartphone use, evening game
player use, evening laptop use, and evening iPod use were not significantly related to sleep
duration.
Third, we found that the block of evening MED use explained a significant 3% of additional
variance in daytime sleepiness beyond the block of control variables and the block of evening
television viewing. Individually, evening iPod use ( β= .10, p< .10) was marginally significantly
related to daytime sleepiness, indicating that more evening iPod use was related to more daytime
sleepiness. For each standard deviation increase in evening iPod use, there was a .10 standard
deviation increase in daytime sleepiness. Children ’s evening tablet use, evening smartphone use,
evening game player use, and evening laptop use were not significantly related to daytime
sleepiness.
Hence, for children ’s evening tablet use, H2 was supported for bedtime resistance (H2a) and
sleep duration (H2b), but not for daytime sleepiness (H2c). For children ’s evening smartphone
use, H2 was supported for bedtime resistance (H2a), but not supported for sleep duration (H2b)
or daytime sleepiness (H2c). For children ’s evening laptop use, H2 was weakly supported for
bedtime resistance (H2a), but not supported for sleep duration (H2b) or daytime sleepiness
(H2c). For children ’s evening iPod use, H2 was weakly supported for daytime sleepiness (H2c),
but not supported for bedtime resistance (H2a) or sleep duration (H2b). For children ’s evening
game player use, H2 was not supported.
Children ’s Reading, Game Playing, and Television Viewing on MEDs and Sleep
Disturbances
First, we examined whether reading, game playing, and television program viewing via MEDs
would predict bedtime resistance ( Table 3 ). We found that reading, game playing, and television
viewing on MEDs explained a significant 2% ( p< .05) of additional variance in bedtime
resistance beyond the block of control variables and the block of evening television viewing.
However, individually, reading on MEDs, playing games on MEDs, and viewing television
programs via MEDs were not significantly related to bedtime resistance.
In the second regression analysis, we found that reading on MEDs, playing games on MEDs,
and television viewing on MEDs explained a marginally significant 2% of additional variance in
sleep duration beyond the block of control variables and the block of evening television viewing.
212 NATHANSON AND BEYENS Playing games on MEDs was significantly related to sleep duration ( β= .06, p< .05), indicating
that more game playing on MEDs at around bedtime was related to compromised sleep duration.
For each standard deviation increase in playing games on MEDs, there was a .06 standard
deviation increase in compromised sleep duration. Reading on MEDs and television viewing on
MEDs were not significantly related to sleep duration.
In the third regression analysis, we found that reading on MEDs, playing games on MEDs,
and viewing television programs via MEDs explained a marginally significant 2% of additional
variance in daytime sleepiness beyond the block of control variables and the block of evening
television viewing. Individually, reading on MEDs, playing games on MEDs, and television
viewing on MEDs were not related to daytime sleepiness.
DISCUSSION
Recently, Christakis ( 2014 ) outlined the possible benefits and harms of interactive mobile media,
concluding that “there is much work to be done in the laboratory ”(p. 400). With research on the
educational value of MEDs pending, we explored the implications of children ’s MED use for another
TABLE 3 Regression Analysis of the Relation Between Children ’s Reading on MEDs, Playing Games on MEDs, and Viewing Television Programs on MEDs at Around Bedtime (Days per Week) and Bedtime Resistance, Sleep Duration, and Daytime Sleepiness
Bedtime resistance Sleep duration Daytime sleepiness
BSE β BSE β BSE β
Step 1Mother ’s education –.07 .13 –.03 .04 .06 .04 –.08 .11 –.04 Mother ’s employment .06 .33 .01 .03 .16 .01 –.75 .27 –.14** Household income –.11 .12 –.05 –.04 .06 –.04 –.05 .10 –.03 Child ’s age –.52 .19 –.14** –.09 .09 –.05 .17 .15 .05 Childcare attendance .03 .07 .02 .02 .04 .03 .25 .06 .21*** R2 .03* .01 .11***
Step 2Evening TV viewing .49 .14 .17** .23 .07 .17** .50 .12 .21*** Incr. R2/R2 .04***/.06*** .03***/.04* .05***/.16***
Step 3Reading on MEDs .03 .27 .01 –.18 .13 –.09 .12 .22 .03 Playing games on MEDs .29 .29 .07 .12 .14 .06* .32 .24 .09 Television viewing on MEDs .42 .33 .09 .27 .16 .12 .14 .27 .04Incr. R2/R2 .02*/.08*** .02+/.05** .02+/.17*** F 4.01*** 2.51** 9.14***
Note .+p< .10. * p< .05. ** p< .01. *** p< .001. Analyses control for demographics, childcare attendance, and evening TV viewing.
MOBILE ELECTRONIC DEVICES AND SLEEP 213 important outcome related to children ’s development: sleep. We found that some types of MED use
—both overall and in the evening —were related to sleep disturbances among preschoolers.
Of all of the devices we examined, tablets were the only platform to show consistent relations
with children ’s sleep disturbances. Preschoolers ’use of tablet devices alone, above and beyond
the influence of television time and our standard control variables, was related to greater bedtime
resistance and problems with sleep duration. Future work should consider why tablets, of all
MEDs, may be the most potent disruptors of sleep. One possibility is that, compared to most
other MEDs, tablets have larger screen sizes, which may attract more viewer attention
(McNiven, Krugman, & Tinkham, 2012 ) and deliver more light. The influence of screen size
may also explain why television viewing was more consistently related to sleep outcomes
compared to MEDs. Interestingly, some prior work has shown that the sleep quality of adults
and adolescents is more disrupted by MEDs compared with televisions (Bartel et al., 2015 ;
Figueiro et al., 2013 ). Developmental differences in light sensitivity may help explain some of
these discrepancies, since Crowley, Cain, Burns, Acebo, and Carskadon ( 2015 ) found that
younger adolescents were more affected by evening light exposure compared with older
adolescents. Similarly, Higuchi et al. ( 2014 ) found that children were more vulnerable to evening
light compared with adults. However, to our knowledge, no work has extended this inquiry to
investigate young children ’s relative blue light sensitivity. Integrating our findings from those of
prior work, we speculate that perhaps preschoolers are at a greater risk of vulnerability to blue
light exposure compared with other age groups, with relatively larger screens exacerbating that
risk. Future work is needed to address this possibility.
The relations between MED use and sleep disturbances we observed accounted for the
influence of television viewing, either overall or in the evening. It was important to control
for the influence of traditional viewing of television on a television set in order to observe
whether MEDs should be considered independent contributors to children ’s sleep disturbances.
Without these controls, it would be tempting to conclude that our observed relations merely
reflected the influence of overall screen time, which is still dominated by television.
In fact, we did observe that television viewing, both overall and during the evening, was
significantly related to each indicator of sleep disturbances. In some cases, television exposure
was a stronger contributor to sleep disturbances, compared with MEDs. In other cases, their
relations with sleep were quite similar. Given that scholars have long been concerned about the
disruptions of sleep caused by television viewing (Cespedes et al., 2014 ), we should now
consider MED use a possible contributor to children ’s sleep disruption as well.
Perhaps it is the combination of exposure to short-wavelength light, held at short range from
the face, that contributes to sleep difficulties. However, it is possible that this potential is offset
when interacting with MEDs with particularly small screen sizes. Smaller screen sizes are
associated with less viewer attention to content (McNiven et al., 2012 ), as they are perceived
as relatively less emotionally arousing (Detenber & Reeves, 1996 ; Ivory & Magee, 2009 ; Kim &
Sundar, 2013 ). As noted above, this may be why tablets, with their relatively large screens, were
more consistently related to sleep disturbances compared with other MEDs. Experimental work
would be helpful to untangle the relative impact of proximity to blue light and screen size on
sleep disturbances.
In addition, it could be that sleep is disrupted because preschoolers engage in arousing forms
of play when using MEDs. For example, game playing on MEDs was significantly associated
with preschoolers ’sleep disturbances, but calmer activities, such as reading and watching
214 NATHANSON AND BEYENS television on MEDs, were not. In their review of the literature researching the links between
screen time and sleep quality, Hale and Guan ( 2015 ) found that more interactive forms of screen
time, such as video-game playing and computer use, were more consistently related to compro-
mised sleep compared with more passive television viewing. Nevertheless, the majority of
studies examining television viewing and sleep disturbances among children still found that
exposure was detrimental to sleep.
At the same time, we acknowledge that using any type of screen can consume a significant
amount of time that can displace sleep (Hale & Guan, 2015 ). Any type of activity can displace
sleep time. However, screen time may pose a unique risk for sleep hygiene. Children and adults
are susceptible to attentional inertia in which time spent looking at television is related to an
increased probability of continuing to look at the television (Anderson, Choi, & Lorch, 1987).
To our knowledge, attentional inertia has not been studied with MEDs, yet there is no reason to
suspect the same phenomenon would not apply. Regardless, it is sensible to suggest that
increased time with MEDs, especially at around bedtime, directly reduces time available for
sleep.
Of our three indicators of sleep disturbances, MED use was most strongly related to
preschoolers ’bedtime resistance. It is understandable that preschoolers who play with MEDs
find them enticing and do not wish to stop play in order to prepare for sleep. This problem may
be exacerbated when preschoolers play with these devices close to bedtime. Also, children who
use MEDs around bedtime might fall asleep in another room or in another bed, for example,
when using MEDs in a sibling ’s bedroom. As a result, the mobility of MEDs may increase
struggles at bedtime.
The relations we observed appear relatively small in size. And yet, they were larger than the
relations between sleep and other predictors in our models, such as household income, maternal
education, and so on. This suggests that, compared with other factors, MED use is an important
contributor to children ’s sleep. Moreover, as MED use becomes further ingrained in family
rituals and children ’s exposure increases, the strength of these associations could grow. Also, as
children mature, these relations could strengthen because children ’s ownership and use of MEDs
increases (Lauricella, Cingel, Blackwell, Wartella, & Conway, 2014 ). By the time they reach
adolescence, young people also use MEDs for a wider range of activities. For instance,
adolescents are frequent users of smartphones for texting, tablets for social network use, and
online communication (Lauricella et al., 2014 ; Lenhart, 2012 ). Further, studies have shown that
large proportions of adolescents use (smart)phones after lights out (National Sleep Foundation,
2014 ; Oshima et al., 2012 ; Van den Bulck, 2007 ). Research is needed that addresses the
cumulative effects of MEDs over time.
It is also possible that sleep disturbances influence children ’s MED use. Children who resist
bedtime, have trouble sleeping through the night, and are drowsy during the day may be
challenging to satisfy. Sleep-deprived children may become easily agitated or difficult more
generally, leading parents to provide them with MEDs as pacifiers. Parents often observe that
their children become quiet and calm when placed in front of media (Rideout & Hamel, 2006 ).
Given that MEDs are widely conceived as educational and beneficial to children (Common
Sense Media, 2013 ), parents might be especially drawn to them as means of occupying
challenging children.
Also, parents may turn to MEDs as a sleep aid for their children. While using MEDs to help
children transition to bedtime may be effective in the short run, it may also make them
MOBILE ELECTRONIC DEVICES AND SLEEP 215 accustomed and even addicted to the use of MEDs as a sleep aid. In fact, using MEDs as a sleep
aid may produce undesired effects, as it could increase bedtime resistance and decrease chil-
dren ’s sleep quality. A vicious circle might develop, in which the use of MEDs as a sleep aid
leads to sleep disturbances, which, in turn, may encourage parents to use MEDs. Like the
relation between television use, computer use, and sleep (Magee et al., 2014 ), the relation
between children ’s MED use and sleep disturbances could thus be bidirectional. Because our
study was cross-sectional and correlational, we cannot determine whether MED use causes sleep
troubles or whether sleep deprivation leads to more MED use.
Our findings suggest that parents may wish to reconsider their children ’s use of MEDs and
possibly limit exposure. Time with MEDs not only increases children ’s overall screen time, but
MED use may be problematic for children ’s sleep. Sleep is critical to development, as children
who are sleep-deprived perform relatively worse on cognitive and academic tests (Bub,
Buckhalt, & El-Sheikh, 2011 ), exhibit less adaptive social behaviors (Simola, Liukkonen,
Pitkaranta, Pirinen, & Aronen, 2014 ), and experience worse health outcomes (Snell, Adam, &
Duncan, 2007 ). Prior work has found that media use indirectly affects children ’s outcomes by
disrupting sleep (Nathanson & Fries, 2014 ; Nuutinen et al., 2014 ). Likewise, MEDs could have
an indirect effect on cognitive, academic, social, and health behaviors via sleep behaviors.
There are several limitations of this study. First, although we gathered a national sample of
mothers, these participants still constitute a convenience sample of mothers who have consented
to regularly complete online surveys for compensation. Second, our data are based on mothers ’
reports of their children rather than on observations. Given that MED use takes many different
forms and is likely spread throughout the day, sometimes in small increments, it may be difficult
for parents to accurately report the amount of time their children spend with these devices.
Moreover, parent reports of children ’s behaviors (both regarding media use and sleep) can be
biased and represent an inferior method (Hale & Guan, 2014), especially compared to other
methods like time diaries. This issue may be amplified given that there may have been some
variety in how parents interpreted our questions. For example, our measures of preschoolers ’
MED activities asked parents to report on behaviors “at around the child ’s bedtime. ”Some
parents may have interpreted this to mean the hour or so preceding lights out, while others could
have interpreted this to mean the point at which parents say goodnight to the child and leave the
room. Because measurement error would attenuate true relations (Hutcheon, Chiolero, & Hanley,
2010 ), we should expect to observe stronger relations between MED use and sleep when better
measurement is employed. Third, our study did not measure any of the mechanisms we proposed
to underlie the relations between MED use and sleep disturbances. Fourth, because of our
methodology, we were unable to determine whether the relation between MED use and sleep
is causal. And, if we cannot be certain about causal direction, then the interpretation of these
relations is inherently ambiguous. As both Hale and Guan ( 2015 ) and Cain and Gradisar ( 2010 )
discovered in their reviews of the literature on this topic, this is a common problem. They noted
that experimental work, in which children are randomly assigned to either an exposure or a no-
exposure intervention group, is necessary to ascertain whether reducing or eliminating evening
MED time can improve preschoolers ’sleep. Finally, the magnitude of the effects we observed
was relatively small and based on multiple analyses, thus increasing the likelihood of Type 1
error. Future work is needed to discover whether these results can be replicated.
Our study found that MED use, especially tablet use, was related to indicators of sleep
disturbances among preschoolers. These relations held independently of the contributions to
216 NATHANSON AND BEYENS sleep disturbances made by traditional television viewing. If scholars are concerned about how
television viewing affects children ’s sleep, then they should consider the implications of MED
use on children ’s sleep as well. Unlike some other risk factors for poor sleep, MED use can be
modified and might therefore serve as a pathway for improving children ’s sleep quality. We
encourage future research to continue this line of work to gain a fuller understanding of how
MEDs affect young children ’s development.
REFERENCES
Anderson, D. R., Choi, H. P., & Lorch, E. P. (1987). Attentional inertia reduces distractibility during young children's TVviewing. Child Development ,58, 798 –806. Arora, T., Broglia, E., Thomas, G. N., & Taheri, S. (2014). Associations between specific technologies and adolescentsleep quantity, sleep quality, and parasomnias. Sleep Medicine ,15, 240 –247. doi: 10.1016/j.sleep.2013.08.799 Bartel, K. A., Gradisar, M., & Williamson, P. (2015). Protective and risk factors for adolescent sleep: A meta-analyticreview. Sleep Medicine Reviews ,21,72 –85. Bub, K. L., Buckhalt, J. A., & El-Sheikh, M. (2011). Children ’s sleep and cognitive performance: A cross-domain analysis of change over time. Developmental Psychology ,47, 1504 –1514. doi: 10.1037/a0025535 Bureau of Labor Statistics. (2015. April 23). Employment characteristics of families 2014 . (Press release No. USDL -15- 0689). Retrieved from http://www.bls.gov/news.release/pdf/famee.pdf . Cain, N., & Gradisar, M. (2010). Electronic media use and sleep in school-aged children and adolescents: A review. Sleep Medicine ,11, 735 –742. doi: 10.1016/j.smrv.2014.07.007 Calamaro, C. J., Mason, T. B. A., & Ratcliffe, S. J. (2009). Adolescents living the 24/7 lifestyle: Effects of caffeine andtechnology on sleep duration and daytime functioning. Pediatrics ,123 , e1005 –e1010. doi: 10.1542/peds.2008-3641 Cespedes, E. M., Gillman, M. W., Kleinman, K., Rifas-Shiman, S. L, Redline, S., & Taveras, E. M. (2014). Televisionviewing, bedroom television, and sleep duration from infancy to mid-childhood. Pediatrics ,133 , e1163 –e1171. doi: 10.1542/peds.2013-3998 Chellappa, S. L., Steiner, R., Oelhafen, P., Lang, D., Gotz, T., Krebs, J., & Cajochen, C. (2013). Acute exposure toevening blue-enriched light impacts on human sleep. Journal of Sleep Research ,22, 573 –580. doi: 10.1111/jsr.12050 Christakis, D. A. (2014). Interactive media use at younger than the age of 2 years: Time to rethink the AmericanAcademy of Pediatrics Guideline? JAMA Pediatrics ,168 , 399 –400. Common Sense Media. (2013). Zero to eight: Children ’s media use in America 2013 . New York, NY: Common Sense Media.Couse, L. J., & Chen, D. W. (2010). A tablet computer for young children? Exploring its viability for early childhoodeducation. Journal of Research on Technology in Education ,43,75 –96. Crowley, S. J., Cain, S. W., Burns, A. C., Acebo, C., & Carskadon, M. A. (2015). Increased sensitivity of the circadiansystem to light in early/mid-puberty. Journal of Clinical Endocrinology & Metabolism ,100 , 4067 –4073. Detenber, B. H., & Reeves, B. (1996). A bio-informational theory of emotion: Motion and image size effects on viewers.Journal of Communication ,46(3), 66 –84. Dobrow, J. (2014, January 21). Toddlers and tablets. Huffington Post . Retrieved from http://www.huffingtonpost.com/ julie-dobrow/toddlers-ipads_b_4620630.html . Dundar, H., & Akcayir, M. (2012). Tablet vs. paper: The effect on learners ’reading performance. International Electronic Journal of Elementary Education ,4, 441 –450. Figueiro, M. G., Wood, B., Plitnick, B., & Rea, M. S. (2013). The impact of watching television on evening melatoninlevels. Journal of the Society for Information Display ,21, 417 –421. Fossum, I. N., Nordnes, L. T., Storemark, S. S., Bjorvatn, B., & Pallesen, S. (2014). The association between use ofelectronic media in bed before going to sleep and insomnia symptoms, daytime sleepiness, morningness, and chronotype. Behavioral Sleep Medicine ,12, 343 –357. doi: 10.1080/15402002.2013.819468 Garmy, P., Nyberg, P., & Jakobsson, U. (2012). Sleep and television and computer habits of Swedish school-age children.Journal of School Nursing ,28, 469 –476. doi: 10.1177/1059840512444133 Garrison, M. M., Liekweg, K., & Christakis, D. A. (2011). Media use and child sleep: The impact of content, timing, andenvironment. Pediatrics ,128 ,29 –35. doi: 10.1542/peds.2010-3304 .
MOBILE ELECTRONIC DEVICES AND SLEEP 217 Hale, L., & Guan, S. (2015). Screen time and sleep among school-aged children and adolescents: A systematic literaturereview. Sleep Medicine Reviews ,21,50 –58. doi: 10.1016/j.smrv.2014.07.007 Hebert, M., Martin, S. K., & Eastman, C. I. (1999). Nocturnal melatonin secretion is not suppressed by light exposurebehind the knee in humans. Neuroscience Letters ,274 , 127 –130. doi: 10.1016/S0304-3940(99)00685-0 Higuchi, S., Motohashi, Y., Liu, Y., & Maeda, A. (2005). Effects of playing a computer game using a bright display onpresleep physiological variables, sleep latency, slow wave sleep, and REM sleep. Journal of Sleep Research ,14, 267 – 273. doi: 10.1111/j.1365-2869.2005.00463.x Higuchi, S., Nagafuchi, Y., Lee, S., & Harada, T. (2014). Influence of light at night on melatonin suppression in children.Journal of Clinical Endocrinology & Metabolism ,99, 3298 –3303. doi: 10.1210/jc.2014-1629 Hutcheon, J. A., Chiolero, A., & Hanley, J. A. (2010). Random measurement error and regression dilution bias. British Medical Journal ,340 , c2289. doi:http://dx.doi.org/10.1136/bmj.c2289 Ivory, J. D., & Magee, R. G. (2009). You can ’t take it with you? Effects of handheld portable media consoles on physiological and psychological responses to video games and movie content. Cyberpsychology & Behavior ,12, 291 –297. doi: 10.1089/cpb.2008.0279 Kim, K. J., & Sundar, S. S. (2013). Can interface features affect aggression resulting from violent video game play? Anexamination of realistic controller and large screen size. Cyberpsychology Behavior and Social Networking ,16, 329 –334. doi: 10.1089/cyber.2012.0500 Kirkorian, H. L., & Pempek, T. A. (2013). Toddlers and touch screens: Potential for early learning? Zero to Three ,33, 32–37. Krcmar, M., & Cingel, D. P. (2014). Parent-child joint reading in traditional and electronic formats. Media Psychology , 17, 262 –281. doi: 10.1080/15213269.2013.840243 Kubiszewski, V., Fontaine, R., Rusch, E., & Hazouard, E. (2014). Association between electronic media use and sleephabits: An eight-day follow-up study. International Journal of Adolescence and Youth ,19, 395 –407. doi: 10.1080/ 02673843.2012.751039Lauricella, A. R., Cingel, D. P., Blackwell, C., Wartella, E., & Conway, A. (2014). The mobile generation: Youth andadolescent ownership and use of new media. Communication Research Reports ,31, 357 –364. doi: 10.1080/ 08824096.2014.963221Lauricella, A. R., Wartella, E., & Rideout, V. J. (2015). Young children ’s screen time: The complex role of parent and child factors. Journal of Applied Developmental Psychology ,36,11 –17. LeBourgeois, M. K., Carskadon, M. A., Akacem, L. D., Simpkin, C. T., Wright, K. P., Achermann, P., & Jenni, O. G.(2013). Circadian phase and its relationship to nighttime sleep in toddlers. Journal of Biological Rhythms ,28, 322 – 331. doi: 10.1177/0748730413506543 Lenhart, A. (2012). Teens, smartphones & texting . Washington, DC: Pew Internet & American Life Project. Magee, C. A., Lee, J. K., & Vella, S. A. (2014). Bidirectional relationships between sleep duration and screen time inearly childhood. JAMA Pediatrics ,168 , 465 –470. Marinelli, M., Sunyer, J., Alvarez-Pedrerol, M., Iniguez, C., Torrent, M., Vioque, J., . . . Julvez, J . (2014). Hours oftelevision viewing and sleep duration in children: A multicenter birth cohort study. JAMA Pediatrics ,168 ,5, 458 –464. doi: 10.1001/jamapediatrics.2013.3861 Masataka, N. (2014). Development of reading ability is facilitated by intensive exposure to a digital children ’s picture book. Frontiers in Psychology ,5, 396. doi: 10.3389/fpsyg.2014.00396 McIntyre, I. M., Norman, T. R., Burrows, G. D., & Armstrong, S. M. (1989). Human melatonin suppression by light isintensity dependent. Journal of Pineal Research ,6, 149 –156. McNiven, M. D., Krugman, D., & Tinkham, S. F. (2012). The big picture for large-screen television viewing for bothprogramming and advertising: Audiences are more attentive, more absorbed, and less critical. Journal of Advertising Research ,52, 421 –432. doi: 10.2501/JAR-52-4-421-432 Munezawa, T., Kaneita, Y., Osaki, Y., Kanda, H., Minowa, M., Suzuki, K., . . . Ohida, T. (2011). The association betweenuse of mobile phones after lights out and sleep disturbances among Japanese adolescents: A nationwide cross-sectional survey. Sleep ,34, 1013 –1020. doi: 10.5665/SLEEP.1152 Nathanson, A. I., & Fries, P. T. (2014). Television exposure, sleep time, and neuropsychological function amongpreschoolers. Media Psychology ,17, 237 –261. doi: 10.1080/15213269.2014.915197 National Sleep Foundation. (2014). 2014 Sleep in America poll: Sleep in the modern family . Washington, DC: National Sleep Foundation.
218 NATHANSON AND BEYENS Nuutinen, T., Roos, E., Ray, C., Villberg, J., Välimaa, R., Rasmussen, M., . . . Tynjälä, J. (2014). Computer use, sleepduration and health symptoms: A cross-sectional study of 15-year-olds in three countries. International Journal of Public Health ,59, 619 –628. doi: 10.1007/s00038-014-0561-y Oshima, N., Nishida, A., Shimodera, S., Tochigi, M., Ando, S., Yamasaki, S., . . . Sasaki, T. (2012). The suicidal feelings,self-injury, and mobile phone use after lights out in adolescents. Journal of Pediatric Psychology ,37, 1023 –1030. doi: 10.1093/jpepsy/jss072 Owens, J. A., Spirito, A., McGuinn, M. (2000). The children ’s sleep habits questionnaire (CSHQ): Psychometric properties of a survey instrument for school-aged children. Sleep ,23, 1043 –1051. Parish-Morris, J., Mahajan, N., Hirsh-Pasek, K., Golinkoff, R. M., & Collins, M. F. (2013). Once upon a time: Parent-child dialogue and storybook reading in the electronic era. Mind Brain and Education ,7, 200 –211. doi: 10.1111/ mbe.12028Pieters, D., De Valck, E., Vandekerckhove, M., Pirrera, S., Wuyts, J., Exadaktylos, V., . . . Cluydts, R. (2014). Effects ofpre-sleep media use on sleep/wake patterns and daytime functioning among adolescents: The moderating role of parental control. Behavioral Sleep Medicine ,12, 427 –443. doi: 10.1080/15402002.2012.694381 Polos, P. G., Bhat, S., Gupta, D., O ’Malley, R. J., DeBari, V. A., Upadyay, H. (2015). The impact of sleep time-related information and communication technology (STRICT) on sleep patterns and daytime functioning in American adolescents. Journal of Adolescence ,44, 232 –244. Rideout V., & Hamel, H. (2006). The media family: Electronic media in the lives of infants, toddlers, preschoolers and their parents . Menlo Park, CA: Kaiser Family Foundation. Salti, R., Tarquini, R., Stagi, S., Perfetto, F., Cornélissen, G., Laffi, G., . . . Halberg, F. (2006). Age-dependent associationof exposure to television screen with children ’s urinary melatonin excretion? Neuroendocrinology Letter ,27,73 –80. Simola, P., Liukkonen, K., Pitkaranta, A., Pirinen, T., & Aronen, E. T. (2014). Psychosocial and somatic outcomes ofsleep problems in children: A four-year follow-up study. Child: Care, Health, and Development ,40,60 –67. doi: 10.1111/j.1365-2214.2012.01412.x Snell, E. K., Adam, E. K., & Duncan, G. J. (2007). Sleep and body mass index and overweight status of children andadolescents. Child Development ,78, 309 –323. doi: 10.1111/j.1467-8624.2007.00999.x U.S. Department of Health and Human Services. (2014). Child health USA 2014 . (AHCPR Publication No. XXX) Rockville, MD: U.S. Department of Health and Human Services.Van den Bulck, J. (2004). Television viewing, computer game playing, and internet use and self-reported time to bed andtime out of bed in secondary-school children. Sleep ,27,101 –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, 1220 –1223. van der Lely, S., Frey, S., Garbazza, C., Wirz-Justice, A., Jenni, O. G., Steiner, R., . . . Schmidt, C. (2015). Blue blockerglasses as a countermeasure for alerting effects of evening light-emitting diode screen exposure in male teenagers.Journal of Adolescent Health ,56,113 –119. doi: 10.1016/j.jadohealth.2014.08.002 Wood, B., Rea, M. S., Plitnick, B., & Figueiro, M. G. (2013). Light level and duration of exposure determine the impactof self-luminous tablets on melatonin suppression. Applied Ergonomics ,44, 237 –240. doi: 10.1016/j. apergo.2012.07.008
MOBILE ELECTRONIC DEVICES AND SLEEP 219 Copyright
ofBehavioral SleepMedicine isthe property ofTaylor &Francis Ltdand its
content
maynotbecopied oremailed tomultiple sitesorposted toalistserv without the
copyright
holder'sexpresswrittenpermission. However,usersmayprint, download, oremail
articles
forindividual use.
