Sleep Predictors and Sleep Patterns _ Carskadon

8/9/2019 Sleep Predictors and Sleep Patterns _ Carskadon

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Original article

Sleep Patterns and Predictors of Disturbed Sleep in a Large Pof College Students

Hannah G. Lund, B.A.a

, Brian D. Reider, B.A.b

, Annie B. Whiting,and J. Roxanne Prichard, Ph.D. b ,*a Department of Psychology, Virginia Commonwealth University, Richmond, Virginia

b Department of Psychology, Univers ity of St. Thomas, St. Paul, Minnesotac Massachusetts General Hospital, Boston, Massachusetts

Manuscript received March 26, 2009; manuscript accepted June 16, 2009

See Editorial p. 97

Abstract Purpose: To characterize sleep patterns and predictors of poor sleep quality in a large populaticollege students. This study extends the 2006 National Sleep Foundation examination of sleep in adolescence by examining sleep in older adolescents.Method: One thousand one hundred twenty-ve students aged 17 to 24 years from an Midwestern university completed a cross-sectional online survey about sleep habits that incluthe Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale, the Horne-Ostberg MingnessEveningness Scale, the Prole of Mood States, the Subjective Units of Distress Scale,questions about academic performance, physical health, and psychoactive drug use.Results: Students reported disturbed sleep; over 60% were categorized as poor-quality sleepethe PSQI, bedtimes and risetimes were delayed during weekends, and students reported frequetaking prescription, over the counter, and recreational psychoactive drugs to alter sleep/wakefulnStudents classied as poor-quality sleepers reported signicantly more problems with physicalpsychological health than did good-quality sleepers. Students overwhelmingly stated that emotiand academic stress negatively impacted sleep. Multiple regression analyses revealed that tenand stress accounted for 24% of the variance in the PSQI score, whereas exercise, alcohol and caffconsumption, and consistency of sleep schedule were not signicant predictors of sleep quality.Conclusions: These results demonstrate that insufcient sleep and irregular sleepwake pattwhich have been extensively documented in younger adolescents, are also present at alarming le

in the college student population. Given the close relationships between sleep quality and physicalmental health, intervention programs for sleep disturbance in this population should be consid

2010 Society for Adolescent Medicine. All rights reserved.

Keywords: Sleep quality; Sleep disturbance; Adolescence; Stress; Mood; College students

Journal of Adolescent Health 46 (2010) 124132


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it physically harder to maintain earlier bedtimes [6,7].Second, external factors like increasing caffeine consumption

and late night use of electronics further delay sleep onset [5,8,9] . Third, early start times for middle schools and highschools demand earlier weekday risetimes [1012] . Finally,even with sufcient sleep times, adolescents have increaseddaytime sleepiness and a greater physiological need for sleepcompared to prepubertal children, which may result frommaturational changes in neuronal connectivity [13,14] .

The consequences of this sleep deprivation are severe,impacting adolescents physical and mental health, as wellas daytime functioning. Population and clinic-based studiesin younger adolescents (ages 1117) have shown strong asso-ciations between chronic sleep restriction and anxiety,depression, and somatic pain [9,1517] . Younger adoles-cents who report shorter sleep also show decrements inacademic performance [5,18], and increased risk-takingbehaviors including drug use and drowsy driving [9,19]. A12-month prospective study by Roberts et al. [20] demon-

strated that insomnia in younger adolescents signicantlyincreased the risk for subsequent declines in social, psycho-logical, physical, and mental health.

By comparison, fewer studies have examined how sleeppatterns change when older adolescents enter college, a timeof minimal adult supervision, erratic schedules, and easyaccess to over-the-counter (OTC), prescription, and recrea-tional drugs. Of these publications, most have focused onsleep patterns, fatigue, and academic performance[17,21,22] . Little is known about what factors contribute toor exacerbate sleep difculties in this population. The current study measures the extent of sleep deprivation and poor-quality sleep in a large population of college students (ages1724), and extends the current literature on adolescent sleepby examining factors that are both precipitating and perpetu-ating of poor sleep in this age group. Using a multibehavioralanalysis in a nonclinical population, we focusedon three main

questions: (a) What are the sleep habits of college students?(b) What behavioral outcomes are associated with poor sleepquality? (c) What physical, emotional, and psychosocialfactors predict poor-quality sleep in college students?

Method

and the remaining 3% identied as orespond. However, there was a fe

although males and females were enrtions, females comprised 63% of the s

Measures

The online survey included ve pubsleep,mood, and stress: (a) the Pittsbur(PSQI), (b) theEpworth Sleepiness ScalOstberg Morningness Eveningness SSubjective Units of Distress Scale (SUof Mood States (POMS). The PSQI poor- and good-quality sleepersareas: subjective sleep quality, sleep lhabitual sleepefciency, sleepdisturbancation, and daytime dysfunction overScoring is based on a 03 Likert scareects the negative extreme. A global

indicative of a poor-quality sleeper, wless is indicative of a good-quality sglobal PSQI scores were split into thr( 5),borderline (67),andpoor ( 8) sgories were created using the speciepurpose of achieving relatively even grconsistencyof thePSQI,estimated byC

The Epworth Sleepiness Scale is aidentify excessive sleepiness associasleep debt or clinical sleepdisorders [2assesses how sleepy one has felt in theipants indicate the likelihood that they wdoing certain activities (e.g., watching to someone, or stopped at a trafc ligh(0 would never doze to 3 high chrange from 0 to 24,with scoresover 10 ilevels of daytime sleepiness. Internal co

estimated by Cronbachs alpha, is .75.TheHorne-Ostberg MorningnessEv

to distinguish between chronotypes (anistic describing ones preference for eithpatterns of activity) [25]. Scores rangsponding to extreme eveningness (lowemorningness (higher numbers) Quest

H.G. Lund et al. / Journal of Adolescent Health 46 (2010) 124132


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and anger [27]. The shortened version includes a list of 30adjectives that relate to the six different mood states; partic-

ipants are asked to rank on a ve-point scale from not at all to extremely how much they experience these mooddescriptors on a typical day. Internal consistency for thePOMS, estimated by Cronbachs alpha, is .79.

In addition to the ve published scales and basic demo-graphic information, we also included questions relating toacademic performance, physical health, and psychoactivedrug use. To assess academic performance, we asked partic-ipants to provide their grade-point average (on a 4.0 scale)and information about class attendance. To assess physicalhealth, we asked students about regularity of exercise andfrequency of missing class because of illness. To assesspsychoactive drug use, we assessed the average week andweekend frequency and intake of caffeine, alcohol, nicotine,marijuana, and prescription and OTC stimulants and sleepaids. Alcohol use was measured by total number of drinks(glass of wine, bottle/can of beer, shot of liquor, etc.) during

the week (SundayThursday) and during the weekend(FridaySaturday); caffeine was measured in drinks per weekday or weekend day (8 oz. serving of coffee, espresso,tea, soft drinks, hot chocolate, or 1.5 oz. of chocolate); nico-tine was measured by the number of cigarettes per day, andmarijuana was measured by the number of uses per week,as well as the number of inhalations per use. Students wereasked to identify motivations for using particular drugs(e.g., to increase wakefulness, to increase alertness, to besocial, to complement meals, to promote sleep, etc.).

Procedure

Participants were recruited through an e-mail sent to allfull-time undergraduate students (n 5,401). The rst pageof the survey informed participants of the purpose and natureof the study, assured them of their anonymity, and askedparticipants to provide informed consent by clicking a state-ment before proceeding to the rst data collection page of thesurvey. As incentives for participation, participants receivedeither class credit if they were enrolled in select psychologycourses (the type of credit depended on the class), or wereentered into a rafe for a chance to win one of four monetaryprizes ($25$150 gift certicates). After the survey was

weekend behaviors. Multivariate anused to explore differences among

and poor-quality sleepers on a numbermood (POMS), stress (SUDS), and caChi-squared analyses were used between optimal-, borderline-, and pordinal and nominal variables. Multipanalyses were employed to determiquality. Variables that have beenstudies to correlate with sleep qualitycomponents of the POMS, stress eveningness [MES], caffeine and alcexercise, and regularity of sleep schesleep and bedtime delay]), but thcomponents of the PSQI score (e.g.,latency, pain during sleep), or direct (e.g., Epworth Sleepiness Score or of the POMS), were used as indeperegression.

Results

Sleeping behavior: quantity and qualit

Overall, college students reportedsleep. Mean total sleep time (time sas opposed to being awake in (SD 1.15).Twenty-ve percent of stless than 6.5 hours of sleep a nighstudents reported getting 8 or more hper night, the average amount requ[28]. Sleep was particularly restrmean weekday bedtime was 12:17 aand weekday rise time was 8:02 a.Sleep schedules were erratic. MeanSD 79 minutes) were delayed and

a.m., SD 88 minutes) were extendtionally, 20% of students reported stayonce in the last month, and 35% rep3 a.m. at least once a week.

Figure 1 outlines the differences inby year, beginning in ninth grade andend of college (high school data are f



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bedtimes and risetimes during the week than did females

a week. Over one-third of students cfor sleep disturbancesat least once a we

the most common reasons provided were stress (35%), excess noise (3(sharing the bed with a partner; 7%).

Students also reported signicdecrements in daytime performance (on the Epworth Sleepiness Scale wand 6.7 for weekends; 25% of studenon the scale, indicating signicant levness. Seventy-ve percent of studdragged out, tired, or sleepy once15% reported falling asleep in class o

Sleep quality, mood, and health

Poor sleep qualitywas associated wself-reported negative moods. Partic

having poor-quality sleep (PSQI scoregreater negative mood subscale scodepression, fatigue, and tension), cogood-quality sleep; for all cases, F ((Table 3 ). Poor-quality sleepers also of stress during the week and weoptimal-quality sleepers, F (2,916) 7 p < .001 (Table 3 ).

Poor-quality sleepers also reportphysical illness than optimal- and bordec

2 (12, n 947) 39.9, p < .05. Twquality sleepers reported missing clasin the last month because of illness, 4% of borderline- or good-quality slon the PSQI were also associated withinstances of falling asleep in class areasons other than illness ( Table 3 ).

Sleep quality was also related to tOTC, and recreational drugs to help refulness (Table 3 ). Specically, those were more than twice as likely to prescription stimulant medications athelp keep them awake, compared to quality However the number of caffe

10:51

10:32

10:15

11:02

12:22 12:1412:20

12:11

11:5312:03

12:25

12:45

1:58

1:44 1:44

1:27

Year in School

B e d

T i m e

week

weekend

week

weekend

7:598:037:598:08

6:316:236:23

6:28

9:49

10:0810:0910:26

9:5110:06

9:529:54

HS 9 HS 10 HS 11 HS 12 CollegeFresh.

CollegeSoph.

College Jr. College Sr.

HS 9 HS 10 HS 11 HS 12 CollegeFresh.

CollegeSoph.

College Jr. College Sr.

Year in School

R i s e

T i m e

Figure 1. Bedtimes and rise times by year in school. Data from the highschool bins are taken from the 2006 Sleep in America Poll (n 1,602) [9].



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Predictors of sleep quality

Several lines of evidence point to stress as a major contrib-utor to poor sleep quality in college students. First, 20.1% of students reported stress interfering with sleep at least oncea week. Women were signicantly more likely to report stress-related sleep troubles than men; t (927) 5.49, p < .001.Second, when asked to provide a written answer to PSQI ques-tion 5j, How often have you had trouble sleeping because of other reason(s); please describe reasons, the most commonanswers were related to stress. Answers such stress about

school, racing thoughts, or worry about the future, ac-counted for 35% of the responses, followed by excess noise(33%), cosleeping (7%), and talking with friends (6%). Third,when asked If your sleep is at all compromised, to what onefactor do you most strongly attribute this? in forced-choicequestion, the majority of students responded that academic(39%) or emotional (25%) stress most interfered with their

tional 3%, and morningness/eveningnfor another 2% of the variance ( Tab

caffeine per day, exercise frequency, avision and video game exposure weretors of the PSQI score.

Discussion

Overall, the results demonstrate thacient sleep documented in high schoearly and midadolescence to college

time is similar between high schoolbut bedtimes and risetimes are s90 minutes on both week and weeketo delay bedtimes and extend risetalso continues into young adulthoosleep and irregular schedules, colleg

l l li h d

Table 1Prevalence of sleep disturbances as measured by the PSQI

Pittsburgh Sleep Quality Index Bedtime Sleep latency Risetime Mean, SD 12:21 a.m. 74 min 23.8 min, 19.2 min 8:05 a.m.,76 min How often have you had trouble sleeping

because .Not during the past month Less than once a week Once or twice a week

Cannot get to sleep within 30 minutes 26.2% 41.4% 21.5% Wake up in middle of night or early morning 44.2% 34.2% 16.0% Wake up to use the bathroom 56.1% 32.7% 7.4% Cough or snore loudly 87.5% 9.6% 2.1% Cannot breathe comfortably 80.6% 13.3% 4.7% Feel too cold 71.0% 23.4% 4.5%

Feel too hot 33.5% 42.8% 19.3% Have bad dreams 70.2% 21.7% 6.5% Have pain 73.1% 19.2% 5.4% Other reasons 49.4% 17.3% 22.0% How often have you .Taken medicine to aid in sleep? 82.1% 11.0% 4.0% Had trouble staying awake during social

activities?75.7% 20.8% 3.3%

Had a problem getting the enthusiasm to get things done?

19.8% 30.1% 33.0%

Rate overall sleep. Very good Fairly good Fairly bad 11.0% 55.0% 30.0% Global PSQI Optimal(15) Borderline(67)

34.1% 27.7%

PSQI Pittsburgh Sleep Quality Index.



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and weekend bedtimes and risetimes) in this report weresimilar to ones at a Chinese university and a small liberalarts college in New England [22,29] . Also, with respect todifferences between student and nonstudent sleep patterns,Oginska and Pokorski [17] documented similar relationships

oping depressive mood disorder [3survey data, there is the potential for

Sleep and stress

Table 2Sample characteristics by class and gender

N Mean SD NESS week Morningness/eveningnessClass 1 262 7.3 3.6 Class 1 27

2 270 7.1 3.9 2 27Post hoc 3 244 6.7 3.7 3 251 > 4 4 205 6.2 3.7 4 21

*Gender M 359 6.2 3.6 Gender M 378F 625 7.2 3.8 F 64

ESS weekend Caffeinated drinks/weekday

Class 1 262 6.7 3.5 *Class 1 302 270 6.8 3.7 2 313 244 6.6 3.8 3 274 205 6.4 3.5 1 < 3,4 2 < 4 4 23

*Gender M 359 6.0 3.6 Gender M 420F 625 7.0 3.6 F 70

Total PSQI Caffeinated drinks/weekend dayClass 1 254 6.7 3.0 Class 1 30

2 254 6.9 3.1 2 313 236 7.3 3.3 3 27

4 202 7.1 3.1 1 < 3 4 23Gender M 349 6.7 3.1 Gender M 420

F 600 7.2 3.2 F 70SUDS week Alcoholic drinks/weekday*Class 1 248 55.1 25.1 *Class 1 30

2 244 62.3 22.3 2 313 234 63.0 21.7 3 27

1 < 2,3,4 4 196 62.2 24.5 1,2 < 3,4 4 23*Gender M 345 53.6 26.0 *Gender M 420

F 580 64.7 21.1 F 70

SUDS weekend Alcoholic drinks/weekend day*Class 1 248 34.9 23.0 Class 1 302 243 40.2 23.1 2 313 234 40.9 22.7 3 27

1 < 3 4 195 39.1 24.1 4 23*Gender M 345 31.8 23.4 *Gender M 420

F 578 43.0 22.3 F 70

Difference from total N reects omissions in survey reporting. Asterisks indicate signicant differences between class or gender. Bodifferences ( a .01) between classes are provided in the left side of the columns.

SUDS Subjective Units of Distress Scale; PSQI Pittsburgh Sleep Quality Index; ESS Epworth Sleepiness Scale.



7/29the neuroendocrine system. Developmental changes in theHPA axis during adolescence result in increased perisleep

Of particular concern is the tendento self medicate sleep wakefulness

Table 3Differences in behavior by PSQI group

Sleep schedule (df) F p

h2

Post hoc Optimal Total sleep time (h) 2,952 102.8 < .001 .01 O > B> P 7.61 Bedtime, weekday 2,948 9.5 < .001 .02 O,B < P 12:07 a.m. Risetime, weekday 2,948 2.8 0.062 < .01 8:06 a.m. Bedtime, weekend 2,946 5.5 0.004 .01 O < P 1:34 a.m. Risetime, weekend 2,947 3.0 0.060 < .01 10:03 a.m. Bedtime Delay (h) 2,946 0.8 0.448 < .01 1.46 Weekend Oversleep (h) 2,947 4.0 0.019 < .01 1.95 Morningness/Eveningness 2,949 33.1 < .001 .07 O > B> P 53

(df),N c 2 p Optimal

Stayed up to 3 a.m. > 13 /week 6,948 46.4 < .001 25% All-nighter > 13 /month 6,947 30.8 < .001 12%

Stress and mood (df) F p

h2 Post hoc Optimal

Anger 2,897 66.8 < .001 .13 O < B< P 7.48 Confusion 2,897 32.2 < .001 .07 O < B< P 8.6 Depression 2,897 71.2 < .001 .14 O < B< P 7.01 Fatigue 2,897 146.2 < .001 .25 O < B< P 9.44 Tension 2,897 81.1 < .001 .16 O < B< P 8.29

Vigor 2,897 28.4 < .001 .06 O > B> P 14.29 Weekday distress (SUDS) 2,916 72.4 < .001 .14 O < B< P 49.9 Weekend distress (SUDS) 2,916 37.7 < .001 .08 O < B< P 30.8

Psychoactive drug use (df) F p

h2 Post hoc Optimal

Caffeinated drinks/day 2,952 0.53 0.59 < .01 1.0 Alcoholic drinks/day 2,952 3.42 0.03 < .01 1.07

(df),N c 2 p Optimal

Use OTC/Rx meds to wake > 13 /month 6,871 23.3 0.003 12%

Use OTC/Rx meds to sleep > 13 /month 6,949 118 < .001 4% Use alcohol to get to sleep 2,681 14.0 < .001 5%

Sleepiness and performance (df) F p h 2 Post hoc Optimal

Epworth SS weekday 2,917 42.2 < .001 .04 O < B< P 5.32 Epworth SS weekend 2,915 16.7 < .001 .10 O < B< P 5.58

(df),N c 2 p Optimal Fall asleep in class 13 /week 6,951 39.2 < .001 9% Skip class > 2/mo, due to illness 12,948 39 < .001 4% Skip class > 2/months, other reasons 10,950 29.3 < .001 16%

O optimal (PSQI > 6), B borderline (PSQI 67), P poor (PSQI > 7). a .01.PSQI [ Pittsburgh Sleep Quality Index; OTC over the counter.

H.G. Lund et a l. / Journal of Adolescent Health 46 (2010) 124132130


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Recommendations

These results highlight a growing need for professionals tofocus on the quality as well as the quantity of sleep whenpromoting mental and physical health in adolescents andyoung adults. College students who are consistently gettingpoor-quality sleep are at risk for problems far more seriousthan simply struggling to function in daily activities. Aschronic insomnia is a risk factor for major mood [39] andsubstance abuse disorders [40], physicians, college health-care professionals, and residence life workers should bemore proactive in screening for sleep difculties and in artic-ulating the importance of sufcient, restorative sleep in

college students well-being.

Disclosure Statement

The authors have indicated no nancial conicts of

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Review Article

Electronic media use and sleep in school-aged children and adolescents: A

Neralie Cain, Michael Gradisar *

School of Psychology, Flinders University, Adelaide, SA, Australia

a r t i c l e i n f o

Article history:Received 3 December 2009Received in revised form 15 February 2010Accepted 19 February 2010Available online xxxx

Keywords:ChildAdolescentSleepMediaTechnologyTelevisionComputer

a b s t r a c t

Electronic media have often been considered to have a negative impact on the sleep lescents, but there are no comprehensive reviews of research in this area. The presenpapers that have investigated the relationship between sleep and electronic media idren and adolescents, including television viewing, use of computers, electronic gami

net, mobile telephones, and music. Many variables have been investigated across thedelayed bedtime and shorter total sleep time have been found to be most consistentuse. A model of the mechanisms by which media use may affect sleep is presentevehicle for future research.

2010 Els

During recent decades, research investigating the sleep patternsand habits of children and adolescents has become increasinglymore prevalent. In these age groups, sleep is considered particu-larly important for learning and memory, as well as having impli-cations for emotional regulation and behaviour [1,2] . Morespecically, insufcient sleep and poor sleep quality have beenassociated with impairments in declarative, procedural and work-ing memory performance, as well as poor concentration, whichlogically translates into poor academic performance [13] .

It is now well known that there is an inverse relationship be-tween sleep duration and age. For example, Iglowstein and col-leagues reported that children move from getting a mean of 11 hsleep at age 6, to 9.6 h at age 11, to 8.1 h at age 16 [4] , and thesevalues are consistent with those reported by other authors [57] .Interestingly, the total sleep duration of children and adolescentsalso seems to be decreasing over time, with young people today

ages or pubertal stages [8] . This suggesttors (such as decreased parental monitoring) ators (such as increased use of electrconsiderable inuence on the amount of sleepcents [8] . Nevertheless, despite popular presmon practice for clinicians to emphasise thelectronic media on sleep, there have been nviews of empirical research in this area.are to (1) describe the evolution of technol

be impacting child and adolescent sleep, (2) reto date, and (3) provide future directions for bical practice.

1. Literature search and inclusion criteria

Sleep Medicine xxx (2010) xxxxxx

Contents lists available at ScienceDirect

Sleep Medicinej ou r na l hom epage : ww w.e l s ev i e r. com/ loca t e / s l e ep
http://dx.doi.org/10.1016/j.sleep.2010.02.006http://www.sciencedirect.com/science/journal/13899457http://www.elsevier.com/locate/sleephttp://www.elsevier.com/locate/sleephttp://www.sciencedirect.com/science/journal/13899457http://dx.doi.org/10.1016/j.sleep.2010.02.006


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that primarily focused on age groups outside the range speciedabove were excluded from this review.

Using these criteria, 36 research articles were identied. Of these, 20 related to television viewing, 15 related to the use of computers, electronic gaming, and/or the internet, 7 related to mo-bile telephones, and 2 related to music (although some papersexamined the inuence of more than one media type).

2. Bedroom media presence and sleep

According to the National Sleep Foundations 2006 Sleep in America Poll , almost all American adolescents (97%) had at leastone electronic media device in their bedroom [13] . These items in-cluded such things as music players (90%), televisions (57%), videogame consoles (43%), mobile (42%) or xed-line telephones (34%),computers (28%), and internet access (21%). Older adolescentshad more media devices in their bedrooms than younger adoles-cents, with a median of two devices for 6th8th graders and fourdevices for 9th12th graders [13] . In line with this, studies withprimary school children have revealed lower prevalence rates of 1843% for televisions in bedrooms [1418] , 1820% for computersor video games [14,17] , and 14% for mobile phones [17] . Otherstudies have found gender differences, with adolescent boys beingmore likely than adolescent girls to have a television or computergame console in their bedroom [19] .

The National Sleep Foundation found that adolescents who hadfour or more media devices in their bedrooms got signicantly lesssleep on both school nights and non-school nights than adoles-cents who had three media devices or less [13] . These adolescentswere also more likely to have fallen asleep at school or while doinghomework at least a few times per week, felt too tired or sleepyduring the day, and more likely to think that they have a sleepproblem. Adolescents with more media devices in their bedroomalso drank more caffeinated beverages during the day and weremore likely to be evening types (i.e., having a preference forgoing to bed late and getting up late) than adolescents with fewermedia devices in their bedroom. Considering that the percentage of children who watch television or play video games before bed issignicantly higher among children who have a television or videogame console in their bedroom [17] , it is likely that bedroom med-ia presence has an indirect effect on sleep (see Fig. 1). Consistentwith this hypothesis, adolescents (aged 1218 years) who reportedgetting more than 8 h sleep per night have been found to engage inless technology-related activities after 9 pm compared with ado-

lescents who reported getting 68 h sleep and thgetting 35 h sleep [20] .

Individual media items in a child or adolescalso been associated with sleep disturbance. Thevision in a child or adolescents bedroom haslayed bedtimes [17,19] , less time in bed time [14,15,18] , increased bedtime resistanoverall level of sleep disturbance [16,18] . signicant relationship between total sleep time vision in the bedroom [20] . The presence of gaming console in a child or adolescents bedrated with delayed bedtime [17,19] , less titotal sleep time [14,17] , and increased prevbedtime resistance, delayed sleep onset, sleep anas, and sleep-disordered breathing [14] . Mrooms pose an additional problem as they alerincoming calls or text messages. In a recent studlescents who reported being woken up at least omessages were signicantly more tired than adolken by text messages [21] . Importantly, simp xed-line telephone in the bedroom has beebedtime on weekend days and a discrepancy of tween weekday and weekend bedtimes [1being correlational (the implications of which wfurther detail later), these results suggest that tbe kept out of bedrooms in order to promote children and adolescents. The following sectiosearch relating to children and adolescents uitems (in or outside the bedroom), beginning searched: television.

3. Television viewing and sleep

Television broadcasting began in the late 1the early 1940s in America, followed later by cpan in 1953 and Australia in 1956 [22]World War, television experienced a populari1950s and 1960s as televisions became more afthen, key stations only broadcasted for seve[22,23] . Television viewing became more exiwith the introduction of video cassette recordercable television and the large clear images assobroadcasting [23] . Even today, large wide-

Socio-Economic Status?

2 N. Cain, M. Gradisar/ Sleep Medicine xxx (2010) xxxxxx


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are becoming increasingly more affordable and more prevalent,with many homes now having multiple sets [18] .

Although early research found no signicant correlation be-tween television viewing and evening sleep duration for school-aged children [24] , a number of subsequent studies have found arelationship between television viewing and a variety of sleep vari-ables (see Table 1 ). For example, one study found that the numberof hours of television watched per day predicted increased bedtimeresistance in children aged 410 years, after controlling for demo-graphic variables of age, gender, and socio-economic status [18] .Comparing children who watched less than 2 h of television eachweekday, children who watched more than 2 h of television eachweekday had increased sleep onset latency, increased sleep anxi-ety, increased night waking, and increased total sleep disturbance[18] . In a longitudinal study, the number of hours of televisionviewing at age 14 was associated with increased sleep problemsat follow-up (age 1622) [25] . In addition, adolescents who choseto reduce their television viewing to less than 1 h per day were lesslikely to experience sleep problems at follow-up. Other studieshave found television use to be associated with less time in bedon weekdays [19] , delayed bedtime on both weekdays and week-end days [14,19,2628] , later wake-up time or out-of-bed time[14,19,26] , increased sleep onset latency [14,29,30] , shorter sleepduration [14,26,27,31] , and sleep disorders involving bedtimeresistance, sleep anxiety, and/or parasomnias [14,18,30,31] . Thisappears to be a worldwide phenomenon, with research now avail-able from Australia, Europe, Asia, and North America.

In addition to active television viewing, one study also exam-ined the relationship between passive television viewing and sleepamong 5- to 6-year-old children [31] . The authors found that theamount of time the television was switched on during the childswaking hours was signicantly correlated with decreased totalsleep duration. Furthermore, passive television exposure was asso-ciated with disorders of initiating and maintaining sleep and over-all sleep problem severity.

In an Indian survey of parental opinions about the effect of tele-vision on childrens sleep it was reported that 18% of parents be-lieved their child (aged 310 years) had a decreased or disturbedsleep pattern as a result of television viewing [32] . In contrast, anAmerican study found that only 6.5% of parents believed that tele-

vision had a negative effect on their childs slare important, considering that much of the this paper relies on parent-report data (see discussion of this issue).

In terms of television viewing in the evhas been reported that as many as 82% of watch television after 9 pm and 34% watchWatching television in the evenings has beennicantly shorter total sleep time on both wee[33,34] and with a generally higher frequecompared with children not watching televiSimilarly, children and adolescents who watch television in the evening have signiand wake-up times on weekdays and/or wTelevision viewing at bedtime has also beecantly correlated with sleepwake transitionof excessive somnolence, and overall sleepchildren aged 56 years [31] . A discrepatween weekday and weekend bedtimes has with watching television before bedtimefrom adolescents are also consistent with the

The results presented in Table 1 reveaables have been investigated in relation to tesleep among school-aged children. As yet, hconsensus regarding which aspects of sleep mvision viewing. The most consistent results tcreased total sleep time, prolonged sleepdelayed bedtime. More research is clearly whether or not other aspects of sleep are alsoviewing.

It is important to note that all of the aboare correlational and that empirical evidencIn a small experimental study, 11 boys (ageexposed to 1 h of a subjectively exciting mo3 h prior to bedtime [36] . Their sleep wboth subjective and objective measures (i.e., somnography, respectively). Compared to a they did not watch television or play videopants had signicantly lower sleep efcieof time in bed that the individual was ac

Table 1

Relationship between television viewing and sleep variables.

Authors Age (years) TIB TST SOL WASO GTB WUT DS/T BR SA Parasomnias SWT DE

Weissbluth et al. [24] 516 XTynjala et al. [28] 1116 UGupta et al. [32] 510 Owens et al. [18] 410 X U U X U U X Van den Bulck [35] 1718 U USaarenpaa-Heikkila et al. [27] 917 U U U

N. Cain, M. Gradisar / Sleep Medicine xxx (2010) xxxxxx


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experimental night. But there was no effect on any other sleepvariables, including sleep onset latency and time spent awake aftersleep onset.

Many children and adolescents watch television in the eveningsfor the purpose of aiding sleep onset [18,37] . For example, onestudy found that 76.5% of their sample (495 children aged 410 years) had television viewing as part of their usual bedtime rou-tines [18] . As seen in Table 2 , use of television has been associatedwith reduced total sleep time, longer sleep onset latency, delayedbedtime, increased bedtime resistance, increased sleep anxiety,and an increased level of overall sleep disturbance. It has not beensignicantly associated with parasomnias or time spent awakeafter sleep onset.

When considering daytime consequences, one study found nosignicant correlations between any of their television viewingvariables and daytime sleepiness in children aged 410 years[18] . But others found television use to be associated with in-creased daytime sleepiness or tiredness in adolescents aged 1217 years [19,27] . The use of television as a sleep aid was also asso-ciated with increased tiredness in this age group [37] . As the Amer-ican Academy of Sleep Medicine stressed the importance of daytime dysfunction in the context of sleep disturbance [38] , fur-ther research is clearly needed on the impact of television viewingon the daytime consequences of poor sleep.

Overall, it appears that television viewing among children andadolescents should be limited, especially in the evenings. Twohours per day has been suggested as a recommended maximumfor school-aged children [39] . Furthermore, consistent with com-mon sleep hygiene recommendations, it appears that televisionsshould be kept out of bedrooms in order to create an environmentconducive to sleep.

4. Use of computer or electronic games and sleep

Computer andelectronic games were introduced to the public inthe early 1970s; however, their popularity surged in the late 1980sand 1990s with the rapid progression of gaming technology andseveral major video gaming companies competing for prominence[23] . In the 2000s, while the use of computer game software de-clined considerably (as more games were produced solely for gam-ing consoles), internet gaming surged in popularity as moreindividuals gained access to this technology [23] . For example, theAustralian Bureau of Statistics reported that the number of house-holds with access to a home computer doubled from 1998 to2008, and the number of households with internet access increasedvefold over the same time period [40] . Canadian data also showedan increase in household internet access from 2001 to 2005 [41] .

Frequent use of computer or electronic games has been associ-ated with later bedtimes on weekend days [14,19,26] , longer sleeponset latency [29,42] , later out-of-bed times on weekend days [19] ,

li t f b d ti kd [19] h t t t l l ti

any sleep disorders (e.g., bedtime resistance, ssomnias) [14] and, in contrast to another studnicant relationship with wake-up times on weekdays nor with weekday bedtimes or daytime sle

When considering the use of computers or ethe evening or at night it has been reported thaadolescents access the internet and 24% play com9 pm [20] . Playing video games or using a combeen associated with later bedtimes [17duration [17,33,37] , later wake-up time on wcreased daytime tiredness [37] , and poore[45] . A discrepancy of more than 1 h between wend bedtimes and a discrepancy of more than 2day and weekend wake-up times have also beeplaying video games before bed [17] .

In terms of internet use, three studies have oresults despite measuring internet use in differeBulck measured the amount of time that adolethe internet at any time of day [19] , Oka andchildren according to whether or not they used tbed two or more times per week [17] , wused a self-report questionnaire to assess symaddiction [46] . Taken together, this research reuse is related to delayed bedtimes [17,19] ,up times or out-of-bed times [17,19] , sh[17,46] , shorter time in bed on weekdaystiredness [19] , and higher levels of subjectivTable 3 ). A discrepancy of more than 2 h beweekend wake-up times was also associated winet before bed [17] .

In a small experimental study (described eawere exposed to 1 h of video-game play approxito bedtime [36] . Compared to a control evensignicantly longer sleep onset latencies on the eand experienced signicant changes in their with less time spent in slow-wave sleep. But thence in overall sleep efciency or time spent awset. A second experimental study involved 22aged 1215 years who participated in three extions: playing a violent video game, playing agame, neither playing video games nor watchingsion programs [47] . Games were played for 2 before bedtime, and a comprehensive questiodiary was also completed on each occasion. Thcant differences between the two game-playing sleep item. Compared to the control night, howwent to bed signicantly later on both gaminplaying the non-violent game, participants reportnicantly easier to fall asleep, and their out-of-being morning was signicantly earlier. Weavconducted a similar experimental study involvin



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Overall, it appears that time playing computer or electronicgames should be limited both during the day and in the eveningfor school-aged children and adolescents. Consistent with recom-mendations regarding television viewing, a cut-off of 2 h of game-playing per day has been suggested as being appropriate[29] , but results from a recent experimental study suggest that it

may be important to distinguish between violent and non-violentgames, as it appears that playing non-violent games in the eveningmay produce positive effects on sleep [47] . Research regardinginternet use has suggested that excessive use at any time of daymay be related to negative consequences for sleep, although norecommendations have yet been made regarding frequency orduration of use.

5. Mobile telephone use and sleep

Since rst being tested in the late 1970s, mobile telephone tech-nology has developed rapidly [23] . Telephones are now not onlyused for making and receiving calls, but also for text-messaging(introduced in the early 1990s) [23] , accessing the internet (intro-duced in the early 2000s) [23] , playing games, listening to music,and storing other personal information (e.g., calendar). Due tothe rapid development of technology in this area, it should benoted that studies performed on mobile phone use may not haveincluded all of the above.

Mobile phone use among Finnish adolescents has been associ-

ated with shorter sleep duration [44] . Similarly, Japanese adoles-cents aged 1315 who use their mobile telephone every daywere found to be more evening-typed and have later wake-uptimes and shorter total sleep duration [49] . They also reportedbeing less satised with their current sleep duration than studentswho did not use a mobile telephone every day [49] . Furthermore,adolescents who reported using their telephone for more than

on weekend mornings and having more frcompared to students who used their telephotions [49] . Another Japanese study, howeveicant difference between adolescents withlatency (i.e., less than 10 min) and long slemore than 10 min) for mobile telephone own

phone use [42] . A more recent study of Taifound no relationship between problematic (based on symptoms of addiction or dependtime or subjective insomnia [46] . Swborderline signicant relationship betweeand mobile phone use, but found no signitween self-reported sleep disturbance and mo

When considering use of mobile telephoning the night, a recent study found that 34%cents reported text messaging and 44% rep

telephone after 9 pm [20] . Furthermore, ucents reported being woken up by incoming tonce per month [21] . This appears to beincreasing age, as older adolescents (mean agken up by text messages signicantly moradolescents (mean age 13 years) [21]lights out (which includes both incominand text messages) was also found to be relatness measured concurrently [21] and 1 lar, adolescents who used mobile telephone

and 3 am were most likely to be very tired adolescents who never used mobile telephon[51] .

While it is clear that children and adolesphones in the evening and at night, the impasleep and daytime functioning is less clear. Asults in this area appear to be less consistent

Table 3

Relationship between sleep variables and use of computer, internet, or electronic games.

Authors Age (years) TIB TST SOL GTB WUT BR SA Parasomnias Daytime sleepiness/tiredne

Van den Bulck [19] 1217 U U U UGaina et al. [42] 1215 UBaHammam et al. [33] 613 UEggermont and Van den Bulck [37] 1217 U U UFuligni and Hardway [43] 1415 UGaina et al. [29] 1213 ULi et al. [14] 511 U U X X X X XMesquita and Reimao [45] 1518 Punamaki et al. [44] 1218 UAdam et al. [26] 511 U U UOka et al. [17] 612 U U UYen et al. [46] 1218 U

Note: U = examined in study (signicant relationship between variables); X = examined in study (non-signicant); TIB = less time in bed; TST = shorterSOL = longer sleep onset latency; GTB = delayed bedtime; WUT = later weekend wake-up time; BR = bedtime resistance; SA = sleep anxiety.



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activities a mobile phone provides and its portability this could be-come the most frequently used media device (day and night) andthus should be an essential item assessed in future studies.

6. Music and sleep

Surprisingly, the effect of music on the sleep of children andadolescents has rarely been studied, despite the fact that electronictransmission of music has followed a similar developmental time-line to that of television. FM radio transmission began in the 1960s,with audio cassettes and compact discs developed in the 1980s,followed later by portable MP3 players in the late 1990s [23] .

A recent study found that 42% of American adolescents reportedlistening to music on an MP3 player after 9 pm [20] , and the Na-tional Sleep Foundation reported that 90% of adolescents have amusic player in their bedroom [13] . But only two studies have ex-plored the effectiveness of music as a sleep aid among school-agedchildren and adolescents. In a cross-sectional correlational studyuse of music as a sleep aid was related to later bedtime on week-days, less sleep on weekdays, and increased tiredness among ado-lescents aged 1217 years [37] . But it was not signicantly relatedto bedtime or total sleep time on weekend days [37] . In an exper-imental study with participants in the 5th grade, participants wererandomly allocated to either receive sedative classical music atnaptime and bedtime for three weeks or not to receive music dur-ing this time [52] . The researcher found that children in the exper-imental group had better subjective global sleep quality over timethan those in the control group [52] . They also experiencedimprovements in subjective sleep efciency and subjective sleepduration, and there was a signicant decrease over time in the per-centage of children who were considered poor sleepers [52] . Theintervention received by the experimental group, however, also in-volved a component of relaxation training, as the children in thisgroup were instructed to monitor their breathing and relax theirmuscles as they listened to the music. Therefore, it cannot be con-cluded that the effects on sleep were purely a result of listening tomusic. Considering the widespread use of music players in theevening, more research is needed to ascertain the effects of differ-ent types of music on sleep.

7. Study limitations

The research studies reviewed here are plagued by a number of limitations. The rst limitation is that only three experimentalstudies were found [36,47,52] . Instead, most report on cross-sec-tional correlational studies, which means that causal direction isdifcult to ascertain. It is possible that children and adolescentswho use technology in the evening do so because they do not needas much sleep as their peers or because they already have a de-

Of the 36 studies reviewed, 21 used self-rparent-report data, 5 used both parent and self2 studies used objective data obtained from (in addition to self-report measures). Of the self-data, 17 collected data using general questions (often a certain sleep problem occurs or estimatiwithout reference to other sleep parameters), pants to estimate sleep parameters (e.g., eswake-up times, sleep onset latencies), and onlytual sleep parameters for a given night (e.g., sledent from these observations there has been verybetween studies in the method and/or content of obtained. Furthermore, parent-reports of sleepnot be an accurate way of measuring the sleep hdren and adolescents [55,56] . In future stmeasured using self-report estimates of sleep paing go-to-bed time, sleep onset latency, time sleep onset, wake-up time, and out-of-bed timseparately for weekdays and weekend days) othese parameters taken from sleep diaries. Subjeas these have been validated against objective sleactigraphy) in adolescent populations [57]tance of daytime consequences of sleep problemtime functioning (e.g., sleepiness) at different tialso be included.

Consideration must be given to whether or ported in these studies are meaningful, even if thsignicant. In some cases, the difference betwetime or wake-up time was only 1015 minference was statistically signicant, we must cbedtime delay of 10 min produces a cliniment in total sleep quality or daytime consequenit may be clinically important to consider the ithe discrepancy between weekday and weekefor adolescents, as a discrepancy of more than 2day and weekend wake-up times has been consignicant by other researchers and cliniciansnoted that this has been considered by one growho reported that a wake-up time discrepancassociated with playing video games or using tbed more than two nights per week [17] .

Different age ranges have been used in thhere. As many developmental changes occur wand sleep habits during the school years, especiaof puberty, it may be more informative to use sFor example, one group of researchers acknolikely that the effects of both active TV viewexposure are largely age-dependent (p. 158)differences in the type and extent of electronicchildren of different ages [44] and it is likeof media use also changes with age Finally it h



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8. Possible mechanisms

A number of mechanisms have been proposed by which mediause might impact sleep quality or quantity. First, media use maydirectly displace sleep or other activities related to good sleep hy-giene (such as physical activity). Second, media use in the eveningsmay cause children to become physiologically aroused, making itmore difcult for them to relax prior to bedtime. A number of stud-ies have found increases in physiological arousal associated withplaying computer games [47,5962] , and with the advent of morephysical computer gaming technology (such as the Nintendo Wii)this issue becomes increasingly more relevant. Third, eveningexposure to bright light from television or computer screens maysuppress melatonin and consequently delay the circadian rhythm[63] . Finally, electromagnetic radiation from mobile telephoneshas been found to change sleep architecture [64,65] and delay mel-atonin production [66] .

It is beyond the scope of this paper to comprehensively reviewthe support for and against the model presented in Fig. 1. Further-more, it is acknowledged that much of the research in this area hasbeen correlational in nature and, consequently, the causal direc-tions presented in the proposed model are largely speculative. Inaddition to the results relating to the potential mechanisms de-scribed above, however, studies providing preliminary supportfor other aspects of the model have already been mentioned in thisreview. For example, the percentage of children who watch televi-sion or play video games before bed has been found to be higheramong children who have a television or video game console intheir bedroom [17] and the relationship between age and weekendtotal sleep time has been found to be mediated by time spent usinga computer [26] . It would be useful for future studies to compre-hensively test this model, using research designs that move beyondthe correlational analyses which are prevalent in this area.

9. Conclusions

Despite the aforementioned limitations, it appears that the useof electronic media by children and adolescents does have a nega-tive impact on their sleep, although the precise effects and mech-anisms remain unclear. Across different media types, the mostconsistent results have been obtained regarding delayed bedtimeand shorter total sleep time associated with excessive media use.In future research, it would be good to develop and test a modelof the mechanisms by which media use affects sleep (such as thatpresented in Fig. 1). This would provide further evidence to informclinicians, parents, young people, and the popular press and willlikely become even more relevant as technology continues to de-velop in the future.

Looking towards the future, the research community faces thechallenge of keeping up with advances in technology For example

be discouraged [31] . Parents should also having electronic media devices in the bedrofect their childrens sleep.

10. Research priorities

It is recommended that future research sthe following areas:

1. Using experimental methods to better between electronic media use and sleep.

2. Including the latest technologies (e.gdevices) in future studies.

3. Assessing the statistical and clinical sindings that relate to measures of sleep qand sleep timing.

4. Attempting to test the mechanisms invoships between electronic media use and s

Financial support

N/A.

Conicts of interest

Nil.

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