WHAT’S KNOWN ON THIS SUBJECT: Presleep activities (eg, television watching) have been implicated in the declining sleep duration of young people. However, previous research reported on selected presleep activities, raising the possibility that important activities in this period are not accounted for.
WHAT THIS STUDY ADDS: This is thefirst study in youth to construct the presleep period by using a use-of-time approach. Twin trajectories of higher screen time and lower nonscreen sedentary time/self-care were evident in late sleepers, with the opposite pattern occurring in early sleepers.
abstract
OBJECTIVE:Presleep activities have been implicated in the declining sleep duration of young people. A use-of-time approach may be used to describe the presleep period. The study aims were to describe the activities undertaken 90 minutes before sleep onset and to examine the association between activities and time of sleep onset in New Zealand young people.
METHODS: Participants (N = 2017; 5–18 years) self-reported their time use as part of a national survey. All activities reported in the 90 minutes before sleep were extracted. The top 20 activities were grouped into 3 behavioral sets: screen sedentary time, nonscreen sedentary time, and self-care. An adjusted regression model was used to estimate presleep time spent in each behavioral set for 4 distinct categories of sleep onset (very early, early, late, or very late), and the differences between sleep onset categories were tested.
RESULTS:In the entire sample, television watching was the most com-monly reported activity, and screen sedentary time accounted for∼30 minutes of the 90-minute presleep period. Participants with a later sleep onset had significantly greater engagement in screen time than those with an earlier sleep onset. Conversely, those with an earlier sleep onset spent significantly greater time in nonscreen sedentary activities and self-care.
CONCLUSIONS:Screen sedentary time dominated the presleep period in this sample and was associated with a later sleep onset. The devel-opment of interventions to reduce screen-based behaviors in the presleep period may promote earlier sleep onset and ultimately improved sleep duration in young people. Pediatrics 2013;131:276– 282
AUTHORS:Louise S. Foley, PhD,aRalph Maddison, PhD,a Yannan Jiang, PhD,aSamantha Marsh, MSc,aTimothy Olds, PhD,band Kate Ridley, PhDc
aNational Institute for Health Innovation, University of Auckland,
Auckland, New Zealand;bHealth and Use of Time (HUT) Group,
Sansom Institute for Health Research, University of South Australia, Adelaide, Australia; andcSchool of Education, Flinders
University, Australia
KEY WORDS
sleep, child, adolescent, television
ABBREVIATIONS
CAPI—computer-assisted personal interview CATI—computer-assisted telephone interview CI—95% confidence interval
MARCA—Multimedia Activity Recall for Children and Adults Drs Maddison, Jiang, Olds, and Ridley were involved in the conception, design, and analysis of the original national survey; Dr Foley extracted the data and drafted the article; Dr Jiang conducted the analysis; and all authors were involved in the design of the presleep analysis reported, contributed to the interpretation of data, were responsible for revising it critically for important intellectual content, and gavefinal approval of the version to be published.
www.pediatrics.org/cgi/doi/10.1542/peds.2012-1651 doi:10.1542/peds.2012-1651
Accepted for publication Oct 1, 2012
Address correspondence to Louise S. Foley, PhD, c/o Ralph Maddison, National Institute for Health Innovation, University of Auckland, Private Bag 92019, Auckland Mail Centre, Auckland 1142, New Zealand. E-mail: L.foley@hotmail.com
PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275). Copyright © 2013 by the American Academy of Pediatrics
FINANCIAL DISCLOSURE:Dr Foley was supported by a Tertiary Education Commission Bright Futures Doctoral Scholarship and is currently supported by a Heart Foundation of New Zealand Postdoctoral Fellowship; Dr Maddison was supported by a Heart Foundation of New Zealand Fellowship and is currently supported by a Health Research Council of New Zealand Sir Charles Hercus Health Research Fellowship; Dr Marsh was supported by a University of Auckland Doctoral scholarship; the other authors have indicated they have nofinancial
relationships relevant to this article to disclose.
During the past 100 years, a rapid de-cline in the sleep duration of young people of.1 hour per night is evident.1
Inadequate sleep has been associated with a range of behavioral and health disturbances in young people, including poor concentration and academic per-formance,2 lack of coordination,3
in-creased aggression,4,5 hyperactivity,5
metabolic dysfunction,6 and obesity.7
Reduced total sleep time is thought to be due to later bedtimes rather than to early waking,8and therefore, presleep
activities may be implicated through disrupting or displacing sleep. These presleep activities include the use of electronic screen–based media9 (eg,
television, computers, and video games) and other nonscreen activities, such as homework.10
The association between screen time and sleep onset or duration has been exam-ined in a number of studies in young people. In Saudi 6- to 13-year-olds (N= 1012), late evening television viewing or computer game play was associated with reduced total sleep time (20.63 hour, 95% confidence interval [CI]27.9 to
20.47,P,.001).11Similarly, television
watching (b=2.041, P, .05), com-puter games (b= 2.064, P, .001), and Internet use (b=2.119,P,.001) were all associated with reduced total sleep time during weekdays in Flemish adolescents (N= 2546).12Data from a
sample of Australian adolescents (N= 2200) indicated 79% were engaged in screen-based activities (television, video games, and computer) in the 120 minutes before sleep onset, with screen time accounting for more than half of this presleep period. Total daily screen time of adolescents with very late or late sleep onset was signifi -cantly greater than for those with very early or early sleep onset (247 vs 208 min/d;P,.0001).13In addition to these
traditional screen activities, presleep use of mobile phones (eg, text mes-saging) may also disrupt sleep. In the
same Flemish cohort described earlier, the odds ratio of being “very tired” 1 year later increased to 3.3 (95% CI 1.9– 5.7) and 5.1 (95% CI 2.5–10.4) in par-ticipants who used a mobile phone once per week or more than once per week after the lights are turned out, respectively, compared with those who did not do this.14
Presleep electronic media use is hy-pothesized to affect sleep patterns in 3 ways: time displacement, depression of melatonin, and cognitive arousal. Time displacement of sleep has been shown to increase when a media device, such as a television, is present in the bed-room.15In addition, the blue light
emit-ted by screens attenuates melatonin concentrations in children,16 which
disrupts the circadian rhythm and delays sleep onset.17Finally, “thriller”
or action-oriented electronic games may stimulate wakefulness through heightened cognitive processes, such as fear or excitement,9,18,19 which may
be reflected in somatic outcomes such as elevated heart rate and perspira-tion.20These 3 factors have potentially
additive and adverse effects on the viewer’s sleep duration.
Other nonscreen activities have also been associated with inadequate sleep in young people, most likely primarily through time displacement. For ex-ample, in a study of 5- to 19-year-old Americans (N = 2454), academic and religious activities were significant predictors of lower sleep duration.21
One other study examined the associ-ation between extracurricular activi-ties, extracurricular employment, and sleep in American adolescents aged 12 to 19 years (N= 3094). A gradient effect on sleep duration was found: those who reported low levels of both extra-curricular activity and work reported the highest sleep duration (459 min/ night); those who reported high levels of extracurricular activities or high levels of work had a shorter sleep
duration (448 and 429 min/night, re-spectively); and those who reported high levels of both extracurricular ac-tivities and work had the shortest sleep duration (408 min/night).22
Because of their association with sleep onset and duration, presleep activities are a logical intervention target to im-prove sleep in young people. However, there is a paucity of information re-garding what young people do in the presleep period, how this influences sleep onset, and what is the ultimate impact on sleep duration. Previous re-search has reported on a priori se-lected presleep activities (primarily screen time), raising the possibility that important activities in this period are not accounted for. A“use-of-time” ap-proach may be used to gain a holistic understanding of what young people are doing in the time preceding sleep. This approach involves the complete construction of all activities performed within a defined and bounded time period, accounting for all of the time in that period. In this context, this relates to the time period immediately before sleep onset. The use-of-time approach may be used to identify individual ac-tivities (eg, television or reading) or broader behavioral sets of interest (eg, non–screen-based sedentary behav-ior) occurring in the time period of interest. Therefore, the aims of this study were to examine the period of time 90 minutes before sleep onset to (1) provide a descriptive account of the most popular activities and (2) explore the relationship between presleep behaviors and sleep onset in a nation-ally representative sample of New Zea-land young people aged 5 to 18 years.
METHODS
A nationally representative cross-sectional survey of 2503 New Zealand children and young people aged 5 to 24 years was conducted between Sep-tember 2008 and May 2009. The survey
Helsinki and was covered by Statistics New Zealand Tier 1 ethical approval. Written consent was obtained from all participants or their parent, depending on the age of the participant. The survey design and methodology have been reported in detail elsewhere.23
Pre-sleep data for participants aged 5 to 18 years are reported here.
Design and Participants
A complex survey design involving stratified multistage sampling was used. The primary sampling unit was a mesh block, which is a defined geo-graphic area, varying in size from part of a city block to large areas of rural land. Within each mesh block, eligible households were identified and asked to participate in the survey. One child or young person was randomly chosen from each eligible household. The re-sponse rate was 55% and a total of 2503 households were surveyed. The race/ ethnicity of the sample was 18.8% Maori (indigenous population), 9.6% Pacific, 12.9% Asian, and 71.4% New Zealand European. This is representa-tive of the ethnic composition of the general New Zealand population.24
Procedure
Data were collected during a face-to-face home visit (computer-assisted personal interview [CAPI]) and a sub-sequent telephone interview (computer-assisted telephone interview [CATI]) conducted 7 to 14 days after the CAPI. The CAPI collected self-report data on sociodemographic characteristics and 1 to 2 days of use-of-time data (which in-cluded the assessment of activities un-dertaken in the presleep period). The subsequent CATI collected an additional 2 days of self-reported use-of-time data.
Home visits were conducted mainly on weekend days to maximize the chance of participants being present at home
data on the weekend, the return CATI was made on a weekday.
Measures
Self-reported time use, including sleep, physical activity, and sedentary behav-ior, was measured by using the Multi-media Activity Recall for Children and Adults (MARCA).25The MARCA is a
com-puterized use-of-time tool. All daily activities (including sleep) are retro-spectively recalled in sequential time segments of 5 minutes or more for 24 hours of the previous day (midnight to midnight). Participants choose from a list of ∼250 activities. Each activity is linked to an energy cost taken from existing child26and adult27compendia.
Metabolic equivalents28 are used to
describe the intensity of activities. The MARCA has been shown to have ade-quate psychometric properties.25,29For
the current survey, up to 4 days of recall were completed. Participants recalled the 2 previous days of activity at each of the 2 data collection periods. Because of the limited cognitive ability of young children to recall time accu-rately,30parents of participants aged 5
to 9 years provided a proxy recall of their child’s activities when they were directly supervising the child, including the presleep period.
Data Treatment
For each eligible participant, 1 MARCA profile was randomly selected for ana-lysis to avoid problems associated with intraindividual clustering. For each profile, the self-reported time of evening sleep onset was identified. Profiles with a reported sleep onset earlier than 6:00 PM were regarded as invalid and ex-cluded. All activities reported in the 90 minutes before sleep onset were extracted for each profile, accounting for the entire presleep period. The top 20 most popular activities (by frequency
screen sedentary time (television, com-puter, and video games), nonscreen sedentary time (eg, reading, eating, and talking), and self-care (eg, showering, brushing teeth, and getting ready for bed). Presleep time spent in each be-havioral set was calculated for each profile. If a profile reported none of the activities comprising a particular be-havioral set, the time in that set was considered to be zero.
According to time of sleep onset,
pro-files were classified into 4 mutually exclusive categories (very early, early, late, and very late) by using quartiles of the residuals obtained from adjusted regression analysis. For each profile, sleep onset was estimated in the model adjusting for participant age and gen-der, as well as day type (school day or not school day). The residuals therefore represented sleep onset for each par-ticipant relative to other young people of the same age and gender on the same type of day.
Statistical Analysis
All statistical analyses were performed by using SAS version 9.2 (SAS Institute, Inc, Cary, NC). Descriptive information on time spent in each behavioral set (screen sedentary time, nonscreen sedentary time, and self-care), for each of the 4 sleep onset categories (very early, early, late, and very late), was presented by age group (5–12 and 13– 18 years) and gender (male and female). Regression analysis was con-ducted to estimate the time in each behavioral set for different categories of sleep onset, adjusting for age, gen-der, and day type. Differences between sleep onset categories were tested statistically, with theaset at .05.
RESULTS
current analysis. The sample consisted of 951 (47.1%) girls, and the mean age was 11.6 years (SD 3.7 years).
Average time of sleep onset is presented in Table 1 for each age group and gen-der. In general, younger participants had an earlier sleep onset than older participants. There were no clear dif-ferences between male and female participants. The top 20 most popular activities (by frequency of reporting) in the 90 minutes before sleep onset are presented in Table 2. These activities
were grouped into 3 behavioral sets. The activities performed in the pre-sleep period were predominantly of low intensity, in the range of 1 to 2 metabolic equivalents. The 3 most com-monly reported activities were watching television, dressing/undressing, and brushing teeth. Overall, these top 20 activities accounted for∼80% of time in the 90-minute presleep period.
Descriptive data on time (minutes) spent in each of the 3 behavioral sets for each of the 4 sleep onset categories
(very early, early, late, and very late) are presented in Table 3. Screen sedentary time accounted for the most time in the 90 minutes before sleep onset, ∼30 minutes or one-third of this period. Consistent with overall daily patterns of screen use in New Zealand31 and
Australia,32 older participants and
male participants engaged in more screen time in the presleep period than did younger participants and female participants, respectively. The opposite trajectory was found for nonscreen sedentary time and self-care; younger participants and female participants spent more time in these activities in the presleep period than did older participants and male participants, respectively. The descriptive data also indicated that screen time tended to be higher in those with a later sleep onset.
The model-adjusted means and differ-ences in time spent in each of the be-havioral sets between sleep onset categories are presented in Table 4. An early sleep onset was associated with significantly less time in screen-based sedentary activities compared with a later sleep onset. The difference be-tween earlier and later sleep onset categories ranged between 4 and 13 minutes (eg, those in the late group spent 13 minutes more of the presleep period in screen time than did those in the very early group). Conversely, an early sleep onset was associated with significantly more time spent in non-screen sedentary behavior, though the difference between early and late cat-egories was less pronounced (5–8 minutes). Finally, for self-care, there were significant differences between early and late groups (higher in the early group), but these differences were even smaller (2–5 minutes).
DISCUSSION
The aims of this study were to describe the activities of 5- to 18-year-old New Zealand young people during the 90 TABLE 1 Average Time of Sleep Onset in All Participants (N= 2017)
Gender Age Group, y Sleep Onset Category n Time of Sleep Onset Female participants 5–12 Very early 146 19:41
Early 182 20:27
Late 152 21:00
Very late 123 22:03
13–18 Very early 89 20:47
Early 65 21:37
Late 85 22:11
Very late 109 23:06 Male participants 5–12 Very early 164 19:38
Early 182 20:29
Late 161 21:01
Very late 158 22:02
13–18 Very early 95 20:30
Early 83 21:31
Late 111 22:14
Very late 112 23:17
TABLE 2 Top 20 Presleep Activities 90 Minutes Before Sleep Onset (N= 2017)
Activity Metabolic Equivalentsa Group Frequency
n %
Watching television–sitting 1.0 SST 964 47.8
Dressing/undressing 2.0 SC 844 41.8
Brushing teeth 2.0 SC 838 41.5
Eating–sitting 1.5 NSST 602 29.8
Getting ready for bed 2.0 SC 580 28.8
Sitting on toilet 1.0 SC 525 26.0
Washing hands/face 2.0 SC 421 20.9
Showering/towelling off 2.0 SC 390 19.3
Reading–lying down 1.0 NSST 381 18.9
Lying awake 1.0 NSST 367 18.2
Drinking–sitting 1.5 NSST 284 14.1
Watching television–lying quietly 1.0 SST 257 12.7
Riding in a car/truck 1.0 NSST 219 10.9
Sitting–talking 1.5 NSST 193 9.6
Reading–sitting 1.3 NSST 185 9.2
Studying/homework 1.8 NSST 146 7.2
Computer work (eg, typing/Internet) 1.8 SST 115 5.7
Sitting in bath 1.5 SC 113 5.6
Computer/console games 1.5 SST 102 5.1
Listening to music/radio–lying 1.0 NSST 94 4.7
NSST, nonscreen sedentary time; SC, self-care; SST, screen sedentary time.
aFrom Ridley et al.26
minutes before sleep onset and to in-vestigate the association between these activities and time of sleep onset. This is thefirst study to date to use a use-of-time approach to construct the entire presleep period, rather than reporting on selected individual activities.
In the entire sample, screen sedentary time (in particular, television watching) dominated the presleep period, by both frequency of reporting and duration. Nearly half the sample reported sitting and watching television, and screen time accounted for∼30 minutes of the
people33; therefore, participants in this
study accumulated one quarter of this recommendation in the 90 minutes before sleep alone. In addition to these
findings, those with a later sleep onset reported up to 13 more minutes of screen time in the presleep period than did those with an earlier sleep onset. The largest mean time differences be-tween those of early and late sleep onset were for screen time, which suggests that this set of activities may be an appropriate target for inter-ventions to promote earlier sleep onset and subsequently improve sleep dura-tion in young people.
Causality, and the direction of causality, cannot be inferred from a cross-sectional design; therefore, it is necessary to consider a number of explanations when assessing the relationship be-tween screen time and sleep onset. One explanation is that screen time may cause later sleep onset via somatic arousal, attenuation of melatonin, or sleep displacement, as proposed by other researchers. Alternatively, young people with later sleep onset may have a greater amount of discretionary time during which they can engage in screen time. A young person who arrives home from school at 4:00PMand goes to sleep at 9:00 PM has 1 additional hour of discretionary time than a young person who arrives home at the same time but goes to sleep at 8:00 PM. If essential tasks, such as homework and dinner, take up a proportion of after-school time, those with a greater overall amount of after-school time, due to later sleep onset, may use this extra time for recreational screen-based activities. The sequencing of events may also be important. Those with later sleep onset may complete their self-care activities earlier before engaging in screen-time later in the evening, with the opposite 5–12 y 13–18 y 5–12 y 13–18 y
n Mean SD n Mean SD n Mean SD n Mean SD
Screen sedentary time
Very early 146 17.23 23.54 89 32.7 31.85 164 22.32 27.87 95 36.11 32.6 Early 182 23.98 27.39 65 32.23 31.31 182 31.29 28.86 83 45 33.57 Late 152 34.8 33.15 85 38.76 32.09 161 35.65 32.21 111 47.7 31.97 Very late 123 34.27 34.58 109 35.78 32.74 158 36.3 33.22 112 44.69 35.01 All 603 27.17 30.48 348 35.06 32.05 665 31.32 30.97 401 43.55 33.48 Nonscreen sedentary time
Very early 146 35.27 22.37 89 25.67 26.4 164 30.58 22.84 95 22.32 22.61 Early 182 32.25 24.46 65 28.46 26.52 182 27.55 23.57 83 19.88 26.18 Late 152 22.37 25.88 85 19.65 24.53 161 25.59 25.05 111 17.61 23.69 Very late 123 22.44 25.71 109 24.4 29.86 158 22.41 25.24 112 19.51 26.73 All 603 28.49 25.19 348 24.32 27.18 665 26.6 24.29 401 19.73 24.82 Self-care
Very early 146 19.35 15.79 89 13.2 12.14 164 18.6 15.06 95 11.89 13.01 Early 182 16.87 13.02 65 15.62 15.65 182 15.66 11.75 83 12.53 12.28 Late 152 13.91 11.62 85 13.06 10.52 161 14.75 12.77 111 11.22 10.49 Very late 123 11.83 9.61 109 11.33 11.28 158 11.96 11.2 112 8.97 9.45 All 603 15.7 13.08 348 13.03 12.29 665 15.29 12.95 401 11.02 11.29
TABLE 4 Differencea
in Time (in minutes) Spent in Screen, Nonscreen, and Self-care Activities 90 Minutes Before Sleep Onset Between 4 Sleep Onset Categories
Difference Between Mean Values
95% CI P
Lower Upper Screen sedentary time
Very early–Early 27.21 211.03 23.38 .0002 Very early–Late 213.34 217.16 29.53 ,.0001 Very early–Very late 211.47 215.32 27.63 ,.0001
Early–Late 26.14 29.93 22.34 .0015
Early–Very late 24.27 28.11 20.43 .0294
Late–Very late 1.87 21.94 5.68 .3362
Nonscreen sedentary time
Very early–Early 2.04 21.06 5.13 .1978
Very early–Late 7.65 4.56 10.74 ,.0001
Very early–Very late 6.97 3.86 10.09 ,.0001
Early–Late 5.61 2.54 8.69 .0004
Early–Very late 4.94 1.82 8.05 .0019
Late–Very late 20.68 23.77 2.41 .6672
Self-care
Very early–Early 1.46 20.07 2.99 .0608
Very early–Late 3.18 1.66 4.71 ,.0001
Very early–Very late 5.26 3.72 6.80 ,.0001
Early–Late 1.72 0.20 3.24 .0264
Early–Very late 3.80 2.26 5.33 ,.0001
Late–Very late 2.08 0.55 3.60 .0076
pattern occurring in those with earlier sleep onset. In this way, screen time may not be keeping late onset sleepers awake but is reflective of differential time-patterning resulting in higher engagement in screen time in the 90 minutes immediately before sleep.
Use-of-time research operates within a bounded time period; therefore, it necessarily follows that increased en-gagement in one behavioral set results in reduced time spent in other behav-iors. In this study, twin trajectories of higher screen time and lower non-screen sedentary time and self-care were evident in those with a later sleep onset, with the opposite pattern occurring in those with an earlier sleep onset. Although statistically significant, the differences between early and late sleepers for nonscreen sedentary time (5–8 minutes) and self-care (2–5 minutes) were small and unlikely to be of clinical significance.
The results of this study are consistent with reports involving Saudi,11
Flem-ish,12 and Australian13 young people
that indicated that screen-based ac-tivities are associated with lower sleep duration or later sleep onset. However, the use of mobile phones in the pre-sleep period was not commonly reported by New Zealand young peo-ple, in contrast to previous research in Flemish adolescents.14 Previous
research10,21,22 examining the effect
of nonscreen activities on sleep (eg, extracurricular activities or employ-ment) is not directly comparable to this study. Earlier studies assessed all nonschool time, whereas the current study assessed the 90 minutes before sleep onset only, when it is unlikely that these activities would occur. Less than 10% of participants reported engaging in study or homework in the 90 minutes before sleep onset. In the current study, we considered all presleep activities, most of which were categorized as self-care. Very few active pursuits were reported in the presleep period, but they may also influence sleep behavior.
The strengths of this study include the use of a large, nationally representative sample and the holistic approach to-ward the construction of the entire presleep period. However, several lim-itations should be acknowledged. First, there was a 45% nonparticipation rate, and nonparticipants may have differed from participants in important ways. However, the sample was representa-tive of the New Zealand population by ethnicity, age, and geography. As dis-cussed, the cross-sectional nature of the study does not allow for inferences of causality to be made. In addition, self-report tools such as the MARCA are associated with error due to memory or social desirability bias, although the
MARCA has been shown to have sound psychometric properties,25,29 and in
the current study the proportion of valid data were high (99.6%). Using data from a subset of participants (5– 18 years) was considered the most appropriate approach to compare young people at a similar stage of development and life circumstances (preadolescent or adolescent school students). The current study examined time of sleep onset but not total sleep duration, which may be an avenue for future research. Onefinal limitation is it was unknown where the activity oc-curred, although it may be assumed it was primarily in the home. For exam-ple, having a television in the child’s bedroom has been linked with sleep disturbances in previous research.9
CONCLUSIONS
Screen time accounted for one third of the 90 minutes before sleep onset in New Zealand young people aged 5 to 18 years. Higher engagement was evident in participants with a later sleep onset, suggesting that reducing screen sed-entary time may be an appropriate in-tervention for promoting earlier sleep onset in young people.
ACKNOWLEDGMENTS
The authors wish to thank all partici-pants in the national survey.
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DOI: 10.1542/peds.2012-1651 originally published online January 14, 2013;
2013;131;276
Pediatrics
Kate Ridley
Louise S. Foley, Ralph Maddison, Yannan Jiang, Samantha Marsh, Timothy Olds and
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DOI: 10.1542/peds.2012-1651 originally published online January 14, 2013;
2013;131;276
Pediatrics
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