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To provide accurate population normative data documenting cross-sectional, age-specific sleep patterns in Australian children aged 0-9 years. The first three waves of the nationally representative Longitudinal Study of Australian Children, comprising two cohorts recruited in 2004 at ages 0-1 years (n=5107) and 4-5 years (n=4983), and assessed biennially. Children with analysable sleep data for at least one wave. At every wave, parents prospectively completed 24-h time-use diaries for a randomly selected week or weekend day. 'Sleeping, napping' was one of the 26 precoded activities recorded in 15-min time intervals. From 0 to 9 years of age, 24-h sleep duration fell from a mean peak of 14 (SD 2.2) h at 4-6 months to 10 (SD 1.9) h at 9 years, mainly due to progressively later mean sleep onset time from 20:00 (SD 75 min) to 21:00 (SD 60 min) and declining length of day sleep from 3.0 (SD 1.7) h to 0.03 (SD 0.2) h. Number and duration of night wakings also fell. By primary school, wake and sleep onset times were markedly later on weekend days. The most striking feature of the centile charts is the huge variation at all ages in sleep duration, sleep onset time and, especially, wake time in this normal population. Parents and professionals can use these new centile charts to judge normalcy of children's sleep. In future research, these population parameters will now be used to empirically determine optimal child sleep patterns for child and parent outcomes like mental and physical health.
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Childrens sleep patterns from 0 to 9 years:
Australian population longitudinal study
Anna M H Price,
1,2
Judith E Brown,
3
Michael Bittman,
3
Melissa Wake,
1,2,4
Jon Quach,
1,2
Harriet Hiscock
1,2,4
1
Murdoch Childrens Research
Institute, Parkville, Victoria,
Australia
2
Centre for Community Child
Health, The Royal Childrens
Hospital, Parkville, Victoria,
Australia
3
School of Behavioural,
Cognitive and Social Sciences,
University of New England,
Armidale, North South Wales,
Australia
4
Department of Paediatrics,
The University of Melbourne,
Parkville, Victoria, Australia
Correspondence to
Dr Anna Price, Centre for
Community Child Health,
The Royal Childrens Hospital,
Flemington Road, Parkville,
VIC 3052, Australia;
anna.price@mcri.edu.au
Received 27 March 2013
Revised 16 October 2013
Accepted 29 October 2013
http://dx.doi.org/10.1136/
archdischild-2013-304083
To cite: Price AMH,
Brown JE, Bittman M, et al.
Arch Dis Child Published
Online First: [please include
Day Month Year]
doi:10.1136/archdischild-
2013-304150
ABSTRACT
Objective To provide accurate population normative
data documenting cross-sectional, age-specic sleep
patterns in Australian children aged 09 years.
Design and setting The rst three waves of the
nationally representative Longitudinal Study of Australian
Children, comprising two cohorts recruited in 2004 at
ages 01 years (n=5107) and 45 years (n=4983), and
assessed biennially.
Participants Children with analysable sleep data for at
least one wave.
Measures At every wave, parents prospectively
completed 24-h time-use diaries for a randomly selected
week or weekend day. Sleeping, nappingwas one of
the 26 precoded activities recorded in 15-min time
intervals.
Results From 0 to 9 years of age, 24-h sleep duration
fell from a mean peak of 14 (SD 2.2) h at 46 months
to 10 (SD 1.9) h at 9 years, mainly due to progressively
later mean sleep onset time from 20:00 (SD 75 min) to
21:00 (SD 60 min) and declining length of day sleep
from 3.0 (SD 1.7) h to 0.03 (SD 0.2) h. Number and
duration of night wakings also fell. By primary school,
wake and sleep onset times were markedly later on
weekend days. The most striking feature of the centile
charts is the huge variation at all ages in sleep duration,
sleep onset time and, especially, wake time in this
normal population.
Conclusions Parents and professionals can use these
new centile charts to judge normalcy of childrens sleep.
In future research, these population parameters will now
be used to empirically determine optimal child sleep
patterns for child and parent outcomes like mental and
physical health.
INTRODUCTION
Insufcient or poor-quality sleep in childhood is
associated with serious negative consequences
including poorer emotional, behavioural and cogni-
tive functioning, increased injury and obesity, and
poorer parental mental and general health.
14
The
cost of childhood sleep problems is considerable.
For Australian families, the average cost associated
with seeking professional healthcare to manage
infant sleep problems in the second 6 months of
life totals $A380 per family (adjusted for ination
to 2012).
5
Unpublished population data indicate
that sleep problems in children aged 07 years
(estimated population 1.14 million) are associated
with a $A15.3 million cost to government in add-
itional health services every year.
6
Matriccianis recent systematic review veried
the common perception that sleep duration in
childhood (518 years) is decreasing.
7
Data from
218 studies (n=690 747 from 20 countries)
showed that the median decrease in childrens sleep
duration was 0.75 min per year since 1905. This
could be contributing to the rise in morbidities
such as childhood obesity and attention decit dis-
order recorded over recent decades.
8
It is equally possible that too much sleep is detri-
mental to health. In a recent critical review, some
adult studies suggested that short (<7 h) and long
(8 h) nightly sleep duration could be associated
with obesity (ie, a non-linear association).
9
Although comparable studies with children suggest
only a negative linear relationship, more evidence
could reveal complex, non-linear relationships.
9
Finally, and independently of duration, sleep timing
and fragmentation may be important to childrens
health. Olds et al
10
, studying time diary data in
2200 Australians aged 916 years, compared two
groups with the same total sleep duration. Those
What is already known
Research interest in infant and child sleep has
rapidly increased because of their relevance to
modernproblems such as obesity and
attention decit disorder.
Starting from infancy, there are steady
age-related declines in duration, number/length
of night wakes and length of daytime sleeps.
However, current reference values are largely
based on inaccurate parental summary or
stylisedrecall of sleep parameters, rather than
accurately recorded population-level sleep data.
What this study adds
Time-use diaries provide population normative
centile curves for sleep duration and, for the
rst time, sleep onset times and wake times
throughout infancy and childhood.
There is a striking range and steady decline in
sleep duration, number of sleep episodes,
number/length of night wakes and length of
day sleeps.
These population parameters can be used to
determine optimal sleep patterns for childrens
behavioural, emotional and cognitive
outcomes, and parent outcomes like mental
health.
Price AMH, et al.Arch Dis Child 2013;0:17. doi:10.1136/archdischild-2013-304150 1
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who rose and went to bed early were more physically active,
while those who rose and went to bed late reported more screen
time and had higher body mass index z scores.
10
Studies are yet
to examine the effect of sleep fragmentation on childrens
health; as Jenni
11
notes, such measures are missing from existing
normative research.
Gallands
12
meta-analysis of sleep patterns in children aged
012 years summarises what is known about child sleep globally.
However, much of the reviewed literature relies on parents
summary recall
12 13
that are known to be inherently inaccur-
ate.
14
Williams et al
15 16
addressed this by developing US popu-
lation norms for 24-h and daytime sleep duration based on
time-use diaries in 018-year-olds. Such diaries approximate
detailed written descriptions of daily activities. They are known
to reliably and validly record daily activities
17
and produce
more precise and accurate recollection of time spent in particu-
lar activities than summary or stylisedrecall.
1820
Despite the known importance of adequate sleep for health,
recommendations for optimal childhood sleep parameters are
traditionally based on opinion rather than empirical evidence.
8
It
is thus unknown whether these recommendations identify optimal
sleep patterns for child and parent outcomes. The rst necessary
step is to extract an accurate, contemporary description of the
range of population-based sleep parameters from infancy through
childhood. This will allow future analyses to examine cross-
sectional associations between sleep and a range of morbidities
and generate evidence-based sleep recommendations. Such
population-level data will make it possible to delineate whether
there are longitudinal sleep trajectories that are associated with
good and poor outcomes. We draw parallels with the advances in
epidemiology that became possible when internationally agreed
paediatric body mass index cut-points were rst developed a
decade ago.
21 22
The nationally representative Longitudinal Study of
Australian Children (LSAC)
23
allows us to extend Williams
research and address this challenge for the rst time. We there-
fore aimed to document the cross-sectional, age-specic sleep
patterns of Australian children aged 4 months to 9 years, to
produce population-based centile charts for clinical use.
METHODS
Design and setting
Data are from the rst three waves of the nationally representa-
tive LSAC. A complete description of the design and sample is
published elsewhere.
24
In brief, a two-stage cluster sampling
design was used to create two cohorts, birth (B Babycohort:
aged 01 years in 2004) and preschool (K Kindergarten
cohort: aged 45 years in 2004), who are assessed every 2 years.
Both cohorts were enrolled during the same period from the
same geographical postcodes, but were sampled independently.
In the rst stage, postcodes (except the most remote) were
sampled after stratifying by the state of residence and urban
versus rural status to ensure proportional geographical and
socioeconomic representation. All children in the relevant age
ranges registered on the Australian Medicare Database (98% of
Australian children) were randomly selected within each post-
code to participate in the trial.
Of the 7980 families invited to join the B cohort and 8446
families invited to join the K cohort, 5107 (64%) infants and
4983 (59%) children, respectively, participated in wave 1. The
nal LSAC sample was proportionally representative of
Australian children based on urban versus rural geographical
location and by state, except that mothers who had completed
high school were over-represented and families with low
incomes under-represented.
24
At wave 3 in 2008, 4386/5107
(86%) B cohort children (aged 45 years) and 4332/4983 (87%)
K cohort children (aged 89 years) were retained. Retention was
marginally lower for children with less highly educated parents
and from non-English-speaking background.
25
Procedures
At all waves, trained researchers administered a face-to-face
caregiver interview and conducted direct child assessments in
the childs home. Researchers then left two time-use diaries (see
Measures below) with primary caregivers to complete on one
randomly selected week and one weekend day, using a desig-
nated day approach where respondents were told which days to
do a diary. Parents returned the diaries by post.
Measures
For the rst three waves, LSAC included a lighttime-use diary (see
gure 1 for a sample diary completed at 45 years).
26 27
Parents
(usually the mother) completed the 24-h diaries, beginning at 04:00
and ending at 04:00 the following day. Beginning at 04:00 is a con-
vention which, while arbitrary, is widely used because at that time
virtually the entire population is undertaking their overnight repose.
Unlike (say) midnight or 05:00, it thus gives a very clear separation
between1days activities and the next for all except night workers.
A diary could be completed prospectively through the day or all
at once at the end of the day. As any of the 26 precoded activities
occurred, mothers indicated its duration in 15-min time intervals.
Sleep was represented by the category sleep, napping.An
episodewas dened by a change in activity or context of any
amount of time. Light time-use diaries are derived from and are
equally valid to traditional full-length time-use diaries, which
require detailed written descriptions of daily activities.
17
For the purposes of this paper, we dened sleep parameters
as follows:
Sleep onset time: start of rst sleep episode occurring after
19:00. If the child was asleep at 19:00, sleep onset time was
dened as the beginning of that sleep episode providing that
episode was at least 90 min in duration.
Wake time: distinguished from an interruption of sleep in the
morning period (from 04:00) if the child was awake for at
least 75 min before the next sleep episode.
Number of night wakes: sum of night wakes from 04:00
until wake time and from 19:00 to 04:00. The average dur-
ation of night wakes was calculated over the same periods.
Length of daytime sleep: any sleep occurring between wake
and sleep onset times.
24-h sleep duration: sum of morning sleep from 4:00 until
wake time, daytime sleep and night-time sleep from sleep
onset time until 4:00 the following morning.
Sleep parameters varied by age more in infancy than child-
hood, so we classied wave 1 of the B cohort in three monthly
age groups, that is, 46 months, 79 months, 1012 months
and 1315 months. At all other waves, we used six monthly age
groups. Given the age range of children at each wave, not all
the consecutive monthly age groups from 0 to 9 years are repre-
sented by these data (see table 2).
Statistical analyses
We applied conservative data cleaning strategies and removed
poor-quality diaries, dened as having more than 150 min of
missing data (excluding time at child care), <10 episodes and/or
>5 episodes where more than ve activities took place simultan-
eously. Traditionally, with time-use data, a cut-off of 90 min is
used. However, this loses a large proportion of the sample. We
2 Price AMH, et al.Arch Dis Child 2013;0:17. doi:10.1136/archdischild-2013-304150
Original article
group.bmj.com on December 17, 2013 - Published by adc.bmj.comDownloaded from
chose 150 min as this is what our statistician (JEB) has used in a
previous analysis of time-use data. The analytic sample com-
prised families who returned at least one good-quality diary
across the three waves. We calculated means and SDs for the
sleep parameters, taking account of the complex survey design,
using Stata SE V.8.2 for Windows (Stata, College Station, Texas,
USA). We present the data without controlling for repeated mea-
sures because we want to present unadulterated data where pos-
sible, and colleaguesprevious analysis of LSAC data suggests
little reason to expect that controlling for repeated measures
would substantially alter the patterns.
28
Existing papers reporting normative sleep data use a range of
techniques to analyse the data, including least mean square,
linear regression models and Kernel plots.
16 29
We chose to plot
Loess curves (smoothing factor 0.75) for sleep duration, and
sleep onset and wake times from Gaussian centiles calculated
for each age group. Centiles were calculated using the formula:
Centile ¼
xþzs
Where x is the sample mean, z is the standardised z score for
each centile (eg, the z score for the 2nd and 98th centile was
2.05) and s is the sample SD. We plotted mean sleep onset and
wake times by week and weekend days. A weekendwas dened
using Friday and Saturday night sleep onset times and Saturday
and Sunday wake times. Loess curves and column graphs for
sleep onset and wake times were plotted using R(V.2.13). To
determine whether to present data by gender, we conducted a
simple sensitivity analysis to examine whether there were differ-
ences in sleep duration between males and females. As there
were few differences between genders across the age categories,
we report results for the full sample.
RESULTS
Respondent characteristics
The B cohort provided 6976 useable diaries (n=3837) for wave
1, 5924 diaries (n=3309) for wave 2 and 5139 diaries
(n=2856) for wave 3. The K cohort provided 6207 diaries
(n=3563) for wave 1, 5719 diaries (n=3247) for wave 2 and
4976 diaries (n=2802) for wave 3. Table 1 shows that, com-
pared with children without analysable diary data, children with
these data were similar in gender and age, but more socio-
economically advantaged.
Figure 1 Example of Longitudinal Study of Australian Children (LSAC) weekday light time-use diary from wave 3 B cohort.
Price AMH, et al.Arch Dis Child 2013;0:17. doi:10.1136/archdischild-2013-304150 3
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Sleep patterns of Australian children
Table 2 and gures 24 show a striking range in sleep patterns.
The number and length of night wakes, number of sleep epi-
sodes and length of daytime and total sleep duration were great-
est at age 46 months, showing a steep decline in the rst
3 years before a atter, continuing decline to 9 years. The vari-
ation in sleep parameters followed a similar pattern, except for
an increase in total sleep duration from 69 years and a brief
increase in length of night wakes at 6.5 years.
The range in sleep duration (gure 2) was large throughout,
though the width and timing of the span differed somewhat by age.
At the age of 46 months, more than 8 h of sleep per day separated
the 2nd and 98th centiles (spanning 1018 h), falling to a difference
of just over 5 h at age 5 (spanning 914 h) before rising again to a
difference of around 8 h at age 9 (spanning 614 h).
This reduction in total sleep duration was driven mainly by
two factors: progressively later sleep onset times (gure 3),
coupled with a reduction then cessation of daytime sleep. In
contrast, mean wake time (gure 4) stayed relatively stable over
time, although its variability increased markedly in the older
children, to a range of around 9 h (3:4515:45) at 9 years.
Some of this widening in centiles represents the differences in
wake times between week and weekend days, both of which are
included in these centile charts. Figure 5 shows that from com-
mencement of school, children progressively woke up and had a
later sleep onset on weekends. For these older children, the
weekendweekday difference was greatest for wake rather than
sleep onset times, ranging from less than 15 min at age 3, climb-
ing to 50 min at age 7 and >60 min at age 9 years. Similarly,
the difference in mean sleep onset times between week and
weekend days was less than 15 min at age 3, increasing to 20,
30 and 35 min for ages 5, 7 and 9 years, respectively.
Sleep patterns were similar for the overlapping ages in B and
K cohorts, apart from night wakes and daytime sleep, which
Table 1 Comparing baseline demographic characteristics of families with versus without analysable sleep diary data in the two cohorts
Characteristics
B Cohort K Cohort
Whole cohort
n=48315107
Analysable sleep diary data
Whole cohort
n=46594983
Analysable sleep diary data
Yes
n=25072625
No
n=23242482 p Value
Yes
n=33863563
No
n=12731420 p Value
Child
Male, % 51.2 50.1 51.9 0.4 50.9 51.5 49.5 0.2
Age in months, mean (SD) 9.2 (2.6) 9.1 (2.6) 9.4 (2.5) <0.0001 57.4 (2.6) 57.3 (2.5) 57.5 (2.8) <0.0001
Born in Australia/New Zealand, % 81.3 82.9 79.7 0.004 77.7 86.1 71.8 <0.0001
Primary caregiver
Age in years, mean (SD) 31.0 (5.5) 31.2 (5.3) 30.8 (5.7) <0.0001 34.7 (5.5) 35.0 (5.2) 34.0 (6.1) <0.0001
Born in Australia/New Zealand, % 81.4 82.9 79.9 0.004 77.7 80.1 71.8 <0.0001
English mainly spoken at home, % 85.6 87.7 83.4 <0.0001 84.4 87.9 75.6 <0.0001
Education status, % <0.0001 <0.0001
Did not complete high school 31.7 28.5 35.2 39.6 35.2 50.8
Completed high school 35.4 36.5 34.3 32.2 33.2 30.0
Completed university degree 32.8 35.1 30.5 28.1 31.7 19.2
Equivalised yearly household
income ($A), mean (SD)
31 745 (17 053) 32 558 (16 523) 30 868 (17 569) <0.0001 31 857 (16 612) 33 478 (16 346) 27 543 (16 550) <0.0001
Married/de facto, % 90.5 92.5 88.5 <0.0001 85.9 88.7 78.8 <0.0001
Table 2 Child sleep patterns (mean (SD)) by age groupings, in the two cohorts
Wave
Age
years (months) N Sleep (h)
No of sleep
episodes
No of
night wakes
Night
wakes (min)
Day
sleep (h)
Day
sleep* (h)
Wake
time(am)
Sleep onset
time(pm)
1 0.5 (46) 554 14.0 (2.2) 6.1 (1.9) 1.1 (1.2) 26.9 (33.8) 3.0 (1.7) 3.0 (1.7) 7:30 (1:30) 8:00 (1:15)
1 0.75 (79) 1573 13.6 (2.1) 5.4 (1.7) 1.0 (1.2) 20.3 (30.5) 2.7 (1.5) 2.8 (1.4) 7:15 (1:30) 8:00 (1:15)
1 1.0 (1012) 1306 13.4 (2.0) 4.7 (1.5) 0.7 (1.0) 14.3 (22.5) 2.5 (1.4) 2.6 (1.3) 7:00 (1:15) 8:00 (1:15)
1 1.25 (1315) 388 13.4 (1.9) 4.2 (1.4) 0.5 (0.8) 11.7 (21.7) 2.4 (1.3) 2.5 (1.3) 7:00 (1:15) 8:00 (1:15)
2 2.5 (2833) 1275 11.9 (1.6) 2.8 (0.6) 0.2 (0.6) 4.4 (14.1) 1.0 (1.1) 1.2 (1.1) 7:15 (1:15) 8:15 (1:00)
2 3.0 (3439) 1929 11.7 (1.6) 2.6 (0.7) 0.2 (0.5) 3.8 (16.1) 0.8 (1.0) 1.0 (1.1) 7:15 (1:15) 8:15 (1:00)
3 (B) 4.5 (5257) 1251 11.1 (1.4) 2.2 (0.5) 0.1 (0.3) 1.8 (12.8) 0.2 (0.5) 0.2 (0.6) 7:15 (1:00) 8:15 (1:00)
1 (K) 4.5 (5257) 1905 11.2 (1.5) 2.4 (0.7) 0.1 (0.4) 2.8 (12.9) 0.3 (0.7) 0.4 (0.8) 7:15 (1:30) 8:30 (1:00)
3 (B) 5.0 (5863) 1549 11.1 (1.3) 2.2 (0.5) 0.1 (0.3) 1.4 (10.1) 0.1 (0.5) 0.2 (0.6) 7:15 (1:45) 8:15 (1:00)
1 (K) 5.0 (5863) 1635 11.0 (1.3) 2.3 (0.7) 0.1 (0.3) 2.1 (13.1) 0.2 (0.6) 0.3 (0.7) 7:15 (1:30) 8:30 (1:00)
2 6.5 (7681) 1292 10.5 (1.8) 2.0 (0.4) 0.1 (0.3) 2.3 (21.2) 0.04 (0.3) 0.1 (0.4) 7:30 (2:45) 8:45 (1:00)
2 7.0 (8287) 1864 10.4 (1.8) 2.0 (0.4) 0.1 (0.3) 2.1 (16.3) 0.04 (0.3) 0.1 (0.4) 7:30 (2:45) 8:45 (0:45)
3 8.5 (100105) 1278 10.3 (2.0) 2.0 (0.4) 0.04 (0.2) 1.4 (12.4) 0.03 (0.3) 0.04 (0.3) 7:45 (2:45) 9:00 (1:00)
3 9.0 (106111) 1467 10.0 (1.9) 2.0 (0.4) 0.04 (0.2) 1.7 (15.5) 0.03 (0.2) 0.04 (0.3) 7:45 (3:00) 9:00 (1:00)
*Sleep time for those not attending day care on the diary day between wake and sleep onset times.
SD for wake and sleep onset times is hours:minutes.
4 Price AMH, et al.Arch Dis Child 2013;0:17. doi:10.1136/archdischild-2013-304150
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were slightly longer for the K cohort, and sleep onset time,
which was 15 min later.
DISCUSSION
Principal ndings
This is the largest and most detailed prospective population-
based study to document child sleep using accurate time-diary
data, and this is the rst to present sleep onset and wake time
centiles based on this method. Duration of most sleep para-
meters decreased with age, although the variation in sleep dur-
ation and number of night wakes increased around school age.
Weekend and weekday sleep onset and wake times became less
synchronous in school-aged children.
Study strengths
These time-use data provide ner-grained, more accurate mea-
sures of child sleep than parent estimates/summaries,
10
on
which much of the existing literature is based. The prospective
nature of the sleep measure limits recall bias while capturing
uctuations in sleep patterns. The population-based sampling
could allow these ndings to generalise to families in many
advantaged countries with school schedules and cultural prac-
tices comparable to Australia.
Study limitations
We collected subjective parent report rather than an objective
sleep measure like actigraphy. As such, our ndings may under-
estimate the number of sleep episodes and night wakes and the
length of night wakes in children aged 09 years.
14
Although
not perfect, sleep diaries approximate actigraphy better than
summary data for parent-reported child sleep and, while actigra-
phy is considered more objective, it is not a gold standardand
is difcult to collect for large-scale population-based research.
We are not sure of the reason for the differences between
cohorts but, as they are small, we do not expect them to be
meaningful. A further limitation is that our ndings may not
fully generalise to the most vulnerable families for whom, given
their greater exposure to chaotic living circumstances, have
more sleep problems.
30
Finally, undoubtedly individual children
vary from day to day, and some children vary more than others.
However, because most children contributed only one with a
maximum of two diaries, we did not explore this further. With
our large sample sizes, we do not expect that this would alter
our cross-sectional population norms.
Interpretation in the light of other studies
Sleep duration for the current sample matched the largest exist-
ing Australian and New Zealand survey of sleep patterns in
03-year-olds
12 31
and was similar to the largest English cohort
studied from 6 months to 11 years.
29
However, it was consist-
ently (approximately 1 h) longer than Williamsnormative US
data and Gallands meta-analysis.
The number of night wakes for the current sample remained
low from infancy to 9 years. This suggests that it is the length of
night wakes (and accompanying disruption), rather than the
number that contributes to the high proportion of sleep
Figure 3 Centiles for sleep onset
times (pm) in the two cohorts.
Figure 2 Centiles for total sleep
duration per 24 h by age in the two
cohorts.
Price AMH, et al.Arch Dis Child 2013;0:17. doi:10.1136/archdischild-2013-304150 5
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problems reported by parents in the rst year of life.
232
Most
children learn to self-settle by 12 months,
33
which may explain
why the length of night wakes dropped considerably after
infancy.
Wake times for Australian children are similar to English and
Swiss children from 0 to 5 years, with a greater pattern of differ-
ences emerging from 5 years of age.
13 29
These differences may
be related to difference in school start hours. Interestingly, week
and weekend day differences for school-aged children were
greater for wake rather than sleep onset times. Reasons for this
are unclear, but it could be that parents are more aware of when
their children wake up than when they go to sleep or when chil-
dren catch up on sleep during weekends.
Unanswered questions and future research
The problematisationof sleep, according to Matricciani et al
8
,
is the tendency for childrens sleep to be considered inadequate,
despite a lack of evidence. Our data provide a useful empirical
starting point from which our future research will determine
which sleep patterns impact most on child and parent outcomes,
whether such effects are linear or non-linear and whether clear
thresholds emerge for sleep duration, sleep onset time and/or
wake time beyond which certain outcomes are less optimal.
Ideally, when collecting population-level sleep data, an objective
measure like actigraphy would be collected from a
representative subsample and extrapolated to the full sample to
estimate normative sleep patterns as accurately as possible.
Implications
There is a wide range in normalchild sleep from 0 to 9 years.
Practitioners can use these centile charts to better counsel fam-
ilies about the normalcy or otherwise of their childs sleep. We
hope these data will lead directly to research identifying adap-
tive child sleep patterns, so practitioners could accurately target
sleep interventions to families most at risk of the adverse effects
of non-optimal child sleep.
Acknowledgements This paper uses condentialised unit record les from the
Longitudinal Study of Australian Children (LSAC) survey. The LSAC project was
initiated and is funded by the Commonwealth Department of Families, Housing,
Community Services and Indigenous Affairs (FaHCSIA) and is managed by the
Australian Institute of Family Studies. The ndings and views reported in this paper,
however, are those of the authors and should not be attributed to either FaHCSIA or
the Australian Institute of Family Studies.
Contributors MB, MW and HH conceived the original analyses. AMHP, JEB, MB,
MW, JQ and HH wrote the manuscript. JEB had full access to all the data in the
study and takes responsibility for the integrity of the data and the accuracy of the
data analysis.
Funding AMHP and JQ were supported by NHMRC Population Health Capacity
Building Grant #436914; MW by NHMRC Population Health Career Development
Award #546405; and HH by NHMRC Career Development Award 607351. The
Murdoch Childrens Research Institute (MCRI) administered the grants and provided
infrastructural support to its staff but played no role in the conduct or analysis of the
trial. MCRI research is supported by the Victorian Governments Operational
Infrastructure Support Program.
Competing interests None.
Ethics approval The study was approved by the Australian Institute of Family
Studies Ethics Committee, and parents provided written informed consent.
Provenance and peer review Not commissioned; externally peer reviewed.
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Original article
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doi: 10.1136/archdischild-2013-304150
published online December 16, 2013Arch Dis Child
Anna M H Price, Judith E Brown, Michael Bittman, et al.
Australian population longitudinal study
years: Children's sleep patterns from 0 to 9
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... Studies examining the developmental course of sleep duration at a population level indicate an overall decline in sleep duration from childhood to pre-adolescence (Price et al., 2014) and adolescence (Iglowstein, Jenni, Molinari, & Largo, 2003). Regarding sleep duration across childhood, daytime naps are a sleep aspect that differs between infants and pre-schoolers versus children and adolescents (Galland, Taylor, Elder, & Herbison, 2012). ...
... Previous research on night-time sleep duration in childhood has reported three (Tham et al., 2021;Zheng et al., 2021), four (Touchette et al., 2009) or five (Plancoulaine et al., 2018) sleep trajectories over time. Similar to the trajectories identified in previous research (Price et al., 2014;Zheng et al., 2021), the trajectories emerging from our study showed that night-time sleep duration decreased as participants' age increased. However, the discrepancies in the number of trajectories detected likely reflect differences between studies in terms of methodologies, time points and/ or sample sizes. ...
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Background Here, we (a) examined the trajectories of night‐time sleep duration, bedtime and midpoint of night‐time sleep (MPS) from infancy to adolescence, and (b) explored perinatal risk factors for persistent poor sleep health. Methods This study used data from 12,962 participants in the Avon Longitudinal Study of Parents and Children (ALSPAC). Parent or self‐reported night‐time sleep duration, bedtime and wake‐up time were collected from questionnaires at 6, 18 and 30 months, and at 3.5, 4–5, 5–6, 6–7, 9, 11 and 15–16 years. Child's sex, birth weight, gestational age, health and temperament, together with mother's family adversity index (FAI), age at birth, prenatal socioeconomic status and postnatal anxiety and depression, were included as risk factors for persistent poor sleep health. Latent class growth analyses were applied first to detect trajectories of night‐time sleep duration, bedtime and MPS, and we then applied logistic regressions for the longitudinal associations between risk factors and persistent poor sleep health domains. Results We obtained four trajectories for each of the three sleep domains. In particular, we identified a trajectory characterized by persistent shorter sleep, a trajectory of persistent later bedtime and a trajectory of persistent later MPS. Two risk factors were associated with the three poor sleep health domains: higher FAI with increased risk of persistent shorter sleep (OR = 1.20, 95% CI = 1.11–1.30, p < .001), persistent later bedtime (OR = 1.28, 95% CI = 1.19–1.39, p < .001) and persistent later MPS (OR = 1.30, 95% CI = 1.22–1.38, p < .001); and higher maternal socioeconomic status with reduced risk of persistent shorter sleep (OR = 0.99, 95% CI = 0.98–1.00, p = .048), persistent later bedtime (OR = 0.98, 95% CI = 0.97–0.99, p < .001) and persistent later MPS (OR = 0.99, 95% CI = 0.98–0.99, p < .001). Conclusions We detected trajectories of persistent poor sleep health (i.e. shorter sleep duration, later bedtime and later MPS) from infancy to adolescence, and specific perinatal risk factors linked to persistent poor sleep health domains.
... For example, in a longitudinal study of sleep patterns in TD children conducted by Quach and colleagues (37), sleep problems were more common at age 4 to 5 (4.3% severe, 8.7% moderate, 20.6% mild) and less common at age 6 to 7 (1.9% severe, 3.8% moderate, 7.0% mild). On the other hand, studies have found that sleep disturbances for some children do not always resolve with age (38)(39)(40) and variations in sleep duration may increase as children reach school-age years (38). Thus, examining within-person changes in sleep over timein addition to examining group-level dynamicsis important to accurately define how sleep unfolds within families. ...
... For example, in a longitudinal study of sleep patterns in TD children conducted by Quach and colleagues (37), sleep problems were more common at age 4 to 5 (4.3% severe, 8.7% moderate, 20.6% mild) and less common at age 6 to 7 (1.9% severe, 3.8% moderate, 7.0% mild). On the other hand, studies have found that sleep disturbances for some children do not always resolve with age (38)(39)(40) and variations in sleep duration may increase as children reach school-age years (38). Thus, examining within-person changes in sleep over timein addition to examining group-level dynamicsis important to accurately define how sleep unfolds within families. ...
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Introduction Children with neurogenetic syndromes commonly experience significant and pervasive sleep disturbances, however, associations with caregiver mental health remains unclear. Previous studies have linked sleep disturbances with increased caregiver depression in typically developing populations, and heightened caregiver stress among neurogenetic populations. The present study expands on findings by exploring the longitudinal association between child sleep duration and caregiver mental health (depression, anxiety, stress) throughout development (infancy to school-aged children) in dyads with and without a child affected by a neurogenetic syndrome. Methods Participants were drawn from the Purdue Early Phenotype Study, including 193 caregivers (Age: M = 34.40 years, SD = 4.53) of children with neurogenetic syndromes (Age: M = 40.91 months, SD =20.72) and typically developing children (n = 55; Age: M = 36.71 months, SD = 20.68). Children in the neurogenetic group were diagnosed with Angelman (n = 49), Prader Willi (n = 30), Williams (n = 51), and Fragile X (n = 8) syndromes. Caregivers completed assessments every six months up to child age three, and annual assessments thereafter. Child sleep duration was measured using the Brief Infant Sleep Questionnaire, and caregiver internalizing symptoms were assessed using the Depression, Anxiety, Stress Scale. Multilevel models were conducted to examine caregiver depression, anxiety, and stress in relation to child sleep duration at both between- and within-person levels, with child age as a moderator. Results Results indicated a between-person effect of child sleep duration on caregiver depression (i.e., differences between families) and a within-person effect on caregiver stress (i.e., change over time) in the full, combined sample. These effects were not maintained when examined separately in neurogenetic and typically developing groups, except for a between-person effect on caregiver stress in the typically developing cohort. Moderating effects of child age were significant for depression and stress only in the typically developing cohort. Discussion In summary, persistent child sleep disruptions were linked to exacerbated caregiver depression across the sample, while acute child sleep disruptions exacerbate caregiver stress within dyads over time. These findings emphasize the importance of addressing child sleep to enhance caregiver wellbeing and has potential relevance for a wide range of neurogenetic syndromes.
... Health-promoting behaviours include regular physical activity, limited screen time, healthy eating, and adequate sleep (1). However, population-level surveys indicate that most children under ve years of age are not meeting the public health recommendations for these behaviours (3)(4)(5)(6)(7)(8). Health behaviours established in the early years can track into adolescence and adulthood, in uencing health across the life course (2,9,10). ...
... While the program was viewed positively by most participants in this study, some parents felt that they were already addressing the topics presented in the Healthy Conversations @ Playgroup program at home. However, population-representative health survey data indicates that majority of households are still not meeting the targets for these health behaviours (3)(4)(5)(6)(7)(8), presenting an incongruence between what parents say they do at home, and what actually occurs. Additionally, parents valued hearing other's experiences, but few acknowledged their role in helping others through sharing their own experiences. ...
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... During early infancy, there is a natural decline in the biological drive for napping. 1,2 Typically, infants sleep multiple times throughout a 24-hours (h) period. However, as they progress into their second year of life, they transit into a biphasic sleep pattern, characterized by a single daytime nap. ...
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p> Background: Sleep disturbance is a problem that often arises in the population of school-age children where every year around 20% to 50% of sleep disorders and about 17% experience serious sleep disorders caused by various factors. Wudhu therapy and sleepy hygienic therapy is one of the therapies that can be used to overcome sleep disorders. The purpose of this research is to find out the influence of wudhu therapy and sleepy hygiene therapy on sleep disorders in school-age children . Method : The research method used is Quasi Experiment Design with Pre Test and Post Test Two Group Design designs. in the application with design interpretation - posttest. The sampling technique was taken by purposive sampling method with a sample of 20 respondents. Data analysis used in this study is the analysis of univariated and bivariated with using T test. Results : Average sleep disturbance of respondents before (pretest) wudhu therapy was 55.50 and after (posttest) wudhu therapy was given 30.10 with a P-value of 0.001 and obtained an average sleep disturbance before respondents (Pretest) performed sleepy hygiene therapy was 56.80 and after (posttest) given sleepy hygiene therapy was 42.60 with a P-value of 0.002. The results showed ablution therapy is more effective than sleepy hygiene therapy . Conclusion : There is an influence of wudhu therapy and sleepy hygiene therapy on sleep disorders in school-age children. Suggestions in this study if a child has a sleep disorder can perform wudhu therapy and sleepy therapy hygiene so sleep becomes comfortable . </p
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Data collected by survey methodology are sensitive to measurement errors. Factors of memory, understanding, and willingness to respond truthfully, distort the quality of results. In this paper, time diaries were used as a quality check for results obtained by direct interviews and questionnaires. Data is based on surveys carried out by Statistics Finland. Comparison showed that measurement error varied considerably between population groups, influencing dependencies and interpretations of the results. Activities clearly distinctive from other activities, such as gainful employment outside the home, produced the most accurate data in direct survey questions. Everyday activities that don't clearly stand out from other uses of time, such as home based employment, are difficult to recall and produce a lot of biasing measurement errors.
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