education.unimelb.edu.au · Web viewmay be indicative of early dysregulation of circadian timing, resulting in metabolic hormone disruption (e.g. leptin and ghrelin) leading to

Beyond duration: Investigating the association between sleep parameters and the weight status of

children

Running head: Beyond duration: sleep parameters and weight status

Cassandra L. Pattinson1,2*, BPsySci, Simon S. Smith1,3, PhD, Sally L. Staton1,2, PhD, Stewart G. Trost1,

4, PhD, Karen J. Thorpe1,2, PhD

1Institute for Health and Biomedical Innovation, Centre for Children’s Health Research, Queensland

University of Technology, Victoria Park Rd, Kelvin Grove, Queensland, Australia, 4101.

2School of Psychology and Counselling, Queensland University of Technology, South Brisbane,

Queensland, Australia, 4059.

3Recover Injury Research Centre Faculty of Health and Behavioural Sciences, The University of

Queensland, Queensland, Australia

4School of Exercise and Nutrition Sciences, Centre for Children’s Health Research, Queensland

University of Technology, Queensland, Australia.

*Corresponding Author: Cassandra L. Pattinson, Centre for Children’s Health Research (CCHR),

Level 5, 62 Graham Street, South Brisbane, Queensland University of Technology, QLD, 4101,

Australia, Ph: +61 (07) 3069 7288, Fax: +61 (07) 3138 0486 Email: [email protected]

Manuscript word count: 4,981 words

Conflict of interests: The authors have no conflicts of interests to disclose

Author contributorship: Conceived and designed the study: SLS KJT SSS CLP SGT. Performed data

collection: CLP SLS. Supervised data collection: KJT. Analysed the data: CLP SSS KJT SLS. Wrote

the paper: CLP SSS KJT. Contributed to revision/editing of manuscript: SLS SGT

mailto:[email protected]

Beyond duration: sleep parameters and weight status2

Summary

This study examined the associations between sleep parameters and weight status in a large sample

of preschool aged (3 to 6 year old) children. Data from a sample of 1,111 children from the

Effective Early Educational Experiences for children (E4Kids) study were analysed. Cross-

sectional associations were examined and general linear modelling (GLM) conducted, with

adjustment for significant control variables, assessed the impact of night-sleep duration, total

sleep duration, napping frequency, sleep timing (onset, offset and midpoint), and severity of

sleep problems on standardised body mass index (BMI z-score). GLM was conducted for the

total sample and then, separately by gender. For the total sample, there was a significant

association between short sleep duration (≤10 hours) and increased BMI z-score. No other

sleep parameters were associated with BMI z-score in this sample. Analyses by gender

revealed that among girls, there were no associations between any sleep parameter and BMI

z-score. However, among boys short sleep duration and napping frequency were significantly

associated with weight status, even after adjustment for control variables. These results

indicate that the relationship between sleep and body mass is complex. Sleep duration

remains a significant independent predictor of body mass in young children. Furthermore, the

differential effect of napping frequency between genders is a significant parameter for further

investigation.

Key Words: Sleep, Body Mass Index, Preschool Children, Gender, Australia


Beyond duration: Investigating the association between sleep parameters and the weight

status of children

Paediatric obesity is a significant public health concern with negative psychosocial and health

sequelae in childhood and across the life course (Dietz, 1998; Ebbeling et al., 2002). Globally, it is

estimated that 42 million children under the age of 5 are classified as overweight or obese

(Commission on Ending Childhood Obesity, 2014). In Australia, 23% of children aged between 2 and

4 years are classified as overweight or obese, with slight increases in prevalence observed through to

adolescence (Australian Bureau of Statistics, 2013). To date, intervention strategies have been

directed to the immediate problem of caloric intake and energy expenditure (e.g. Get Up & Grow

(AUS), Hip-Hop to Health (USA), MAGIC (UK) campaigns); however, the problem remains

significant. For this reason attention has turned to identifying other modifiable mechanisms

implicated in weight status. Sleep has emerged as a significant candidate for investigation.

Meta-analyses have identified an association between shortened sleep duration and increased risk of

obesity in children (Chen et al., 2008; Ruan et al., 2015). However, not all studies have found an

association between sleep duration and weight status in young children (e.g. Hiscock et al., 2011).

Furthermore, recent research suggests that alternative sleep parameters, including sleep midpoint and

timing of sleep onset or offset, potentially exert a greater influence on weight status than duration

alone (Anderson et al., 2016; Golley et al., 2013; Olds et al., 2011; Scharf and DeBoer, 2015; Thivel

et al., 2015). For example, Olds et al. (2011) report that children and adolescence classified as “late

bed - late rise” had decreased physical activity and increased weight status compared to those

classified as “early bed - early rise” despite these groups having similar sleep durations. Similarly, in

a study of “late” vs “normal” sleepers, later sleep midpoint was associated with higher weight status,

even though sleep duration did not differ (Thivel et al., 2015). In longitudinal analysis of younger

children (aged 4-5 years), both shorter night sleep duration and later sleep onset at age 4 was

associated with increased body mass between 4 and 5 years of age (Scharf and DeBoer, 2015). This


may be indicative of early dysregulation of circadian timing, resulting in metabolic hormone

disruption (e.g. leptin and ghrelin) leading to increased body mass (Hart et al., 2013).

Considering the independent effect of a multiplicity of sleep parameters on the weight status of young

children may provide greater understanding of the mechanisms at play. Therefore, the aim of this

study was to investigate associations between multiple sleep parameters (daytime napping, night time

sleep duration, sleep timing and sleep problems) and weight status in a large sample of Australian

children aged 3 to 6 years (N=1,111). We hypothesized that sleep parameters indicative of poorer

sleep would be associated with increased body mass. Further, due to recent research indicating

significant gender differences associated with shortened sleep duration (Plancoulaine et al., 2015) and

subsequent increased BMI z-score (Tatone-Tokuda et al., 2012), we conducted exploratory analyses

stratified by gender.

Method

Participants

The children were participants in the Effective Early Educational Experiences for children (E4Kids), a

5-year Australian longitudinal study of the developmental impact of early childhood education and

care (ECEC) services. The sample and design of the study have been detailed elsewhere (Tayler et al.,

2016). Briefly, children and families were recruited in 2010 from childcare services across four

locations in two states: Queensland – Brisbane (metropolitan) and Mt Isa (remote), Victoria –

Melbourne (metropolitan) and Shepparton (rural). Stratified random sampling was used to capture the

range of licensed service types (Long Day Care, Kindergarten and Family Day Care) and ensure

representation of both high and low socioeconomic states (SES). Recruitment was focussed on

children aged between 3 and 5 years of age, with any child within identified ECEC rooms invited to

participate. Written informed consent was provided by main caregivers and children gave verbal

consent. Ethical approval was provided by both The University of Melbourne and Queensland

University of Technology Human Research Ethics Committees. A total of 2,488 children were


recruited in 2010 from 140 services. These data are from the second year (2011) of the study, when

height and weight were collected for 1,945 children (78.2%). Parents of 1,288 children completed the

sleep items. Complete data were available for a total of 1,112 children.

Analyses were conducted to investigate if there were any significant differences between parents who

did and did not complete the main caregiver sleep items in 2011. Parents who did not complete the

items were significantly younger (M = 30.56 (.23)) than parents who completed (M = 32.20 (.14)) the

survey (t (1,129.56) = -6.11, p <.001). Furthermore, parents who completed the survey had

significantly higher relative advantage on SES (M = 1031.54 (2.1)) than parents who did not complete

(M = 1000.96 (2.16)) the survey (t (1281) = -10.13, p <.001). No other significant differences were

found between parents who completed or did not complete the study in 2011.

Weight Status

Height and weight were measured by trained fieldworkers to WHO standards (WHO Multicentre

Growth Reference Study Group, 2006) within the child’s ECEC service. Children were dressed in

light clothing and without shoes and were measured using calibrated stadiometers (SECA Leicester

Portable Height Measure) and floor scales (HD-316, Wedderburn Scales; Tanita Corporation, Tokyo,

Japan). Children were measured twice; if measurements differed (weight >0.1kg; height >0.5cm) a

third measurement was taken by the researcher with the mean of these measurements used to calculate

BMI. The WHO’s Anthro (version 3.2.2) and AnthroPlus (for children over 5.1 years) programs were

then used to transform raw anthropometric data into sex- and age-specific z scores. To ensure

comparability, weight status was reported using international cut-points (Cole et al., 2000; Cole and

Lobstein, 2012). Biologically implausible values (BIV) were identified using the WHO guidelines,

with one child identified and subsequently removed.

Sleep Parameters

The main caregiver reported their child’s typical night sleep; (1) “On a typical night, when does the

study child usually go to sleep?” – responses were indicated by a time between “before 5 pm” through

to “midnight or after midnight” presented in half hour increments, (2) “On a typical morning, when


does the study child usually wake up?” - respondents indicated a time between “before 4am” through

to “after 10am” presented in half hour increments. The main caregiver also reported on their child’s

napping frequency and duration; (1) “Does the study child ever nap (sleep during the day)?” –

response category of yes/no, (2) “In a typical week, on how many days does the study child usually

nap?” – using an eight-item Likert scale response item from “only some weeks/less than 1 day per

week – 7 days per week”, (3) “On days when the study child naps, how long do they usually nap for?”

– responses was on a seven-point Likert scale from “0-15mins – more than 2hours”. Finally, the main

caregivers were also asked if their child “ever have problems with their sleep” and how severe they

thought these problems were: Mild / Moderate / Severe. Using the information collected the sleep

parameters were created as described in Table 1.

Control Variables

Child Factors

Child gender and date of birth were reported by the main caregiver. The main caregiver also

completed information about child temperament, using the Short Temperament Scale for Children

(Sanson et al., 1994). Inflexible (aka reactive) temperament was measured as temperament has been

shown to be associated with both increased weight status (Faith and Hittner, 2010) and decreased

sleep duration (Touchette et al., 2009) in young children. The question, “In the last 24 hours, how

often has your child had biscuits, doughnuts, cake, pie or chocolate?” was used to measure frequency

of discretionary food intake with dichotomous responses categories of “not at all” and “at least once”.

The main caregiver also provided information on the frequency of their child’s physical activity and

sedentary behaviour. A question from the home learning environment scale (Totsika and Sylva,

2004) was used as a proxy of physical activity (PA) frequency. This question asks parents to report the

days per week (0 – 7 days) that they participated in outdoor activities with their child like walking,

swimming or cycling. Furthermore, two questions from the social learning environment scale (Sylva

et al., 2004) in which parents reported the number of days per week (0 – 7 days) in which a child


watches TV or movies either with an adult, or alone, were averaged to provide a measure of screen

time frequency.

Family and Environment Factors

The main caregiver provided information about their family’s demographic profile. This information

included the total family net income for the last 12 months. The main caregiver also indicated their

highest level of education currently completed. The main caregiver also reported the total years of

centre-based child care their child had received up until 2011. From the postcode of the family, SES

was determined using the index of relative socioeconomic advantage and disadvantage Socio-

Economic Indices for Areas (SEIFA) from the Australian Bureau of Statistics (ABS) 2006 Census

data (Australian Bureau of Statistics, 2008). The SEIFA includes measures of disadvantage (low

income, lack of post-school qualifications in people aged 15 years or older) and relative advantage

(e.g. tertiary education) of people residing in the area, with higher scores indicating greater advantage

with a corresponding lack of disadvantage. Parental control over child choices e.g. bed timing and

food eaten, was also measured. Six questions were summed (highest score = 30), with higher scores

indicative of greater parental control over activities.

Statistical Analyses

Analyses were conducted to examine the relationship between sleep parameters, child, family, and

environmental factors for 1,111 children. All statistical analyses were completed using the Statistical

Packages for Social Sciences (SPSS) version 23.0 software. The purposeful selection method (Bursac

et al., 2008; Hosmer et al., 2013) was used to determine the sleep parameters and control variables

which had a significant effect on BMI z-score. Correlation analysis identified the independent

variables (both explanatory and control) that were significantly associated with BMI z-score at the

selected p-value cut-off point of < .30. Then, an iterative process of variable selection was conducted.

Explanatory and control variables were entered into a General Linear Model (GLM) with BMI z-score

as the dependent variable. Variables were removed if they did not have a “significant effect” on the

model and were not a confounder. A significant effect was defined as having a p <.10 and not having


a significant influence over any remaining variables within the model of greater than 15% (Bursac et

al., 2008; Hosmer et al., 2013). After the process of deleting and verifying the model, any variable

that had been excluded was then entered back into the model one at a time (Hosmer et al., 2013). Any

independent variables that had a significant effect were retained in the model. This process provided

the most parsimonious model with the significant independent and control variables retained and any

extraneous variables removed. From the final GLM analyses, any significant associations found

between BMI z-score and the sleep parameters were examined using post-hoc pairwise comparisons

using Sidak correction for multiple comparisons. Data were then stratified by gender and the models

analysed separately for boys (n = 576) and girls (n = 535), with the same process identified with each

sleep parameter and control variable examined for inclusion in the final GLM. Planned post-hoc

comparisons with Sidak corrections were utilised to examine any significant differences between

categorical sleep parameters and BMI z-score.

Results

Demographic information for the participating children and families are presented in Table 2.

Children were aged between 2.79 to 6.78 years and 1.9% of children were identified as being of

Aboriginal or Torres Strait Islander Origin. In this sample, 16.2% of the children were classified as

overweight or obese (Cole and Lobstein, 2012). Table 3 provides information about the measured

sleep parameters across the whole sample, and then by gender. Boys woke up slightly earlier (t

(1107.29) = -3.45, p = .001) and had shorter night-sleep durations (t (1109) = -2.94, p = .003) than

girls. Accordingly, boys typically had an earlier sleep midpoint time than girls in this sample (χ2 (1,

1111) = 6.14, p = .013).

The result of the correlational analysis examining the relationship between sleep parameters, control

variables, and BMI z-score are reported in Table S1. The only sleep parameter that was significantly

associated with BMI z-score was night sleep duration (r = -.07, p = .028). According to the

purposeful selection methodology (p < .30), napping frequency (r = .04, p = .154) was also included

in the second, iterative step of analyses. These analyses revealed that night sleep duration was the


only sleep parameter significantly associated with BMI z-score to be included in the final model. The

significant control variables identified for inclusion in the final model were main caregiver education,

parent control, inflexible temperament, and SEIFA.

The overall model was significant F (6,1070) = 5.27, p <.001; partial ɳ2 = .029 (Table 4). An effect

size of .029 indicates a small effect of the model in accounting for change in BMI z-score (Cohen,

1988). Night sleep duration remained a significant independent predictor of BMI z-score, even after

controlling for SEIFA, inflexible temperament, parent control and main caregiver education. Post-hoc

pairwise comparisons using Sidak corrections indicated that there was a .22 mean unit increase in

BMI z-score for short sleepers (M = .77 ± .09) in comparison to normal sleepers (M = .55 ± .04).

Separate analyses of gender found that for girls, there were no significant correlations between any

sleep parameters and BMI z-score (see Table S2 for correlations). For boys (Table S3), sleep duration

and napping frequency were significantly associated with BMI z-score. Control variables identified to

interact with BMI z-score included; SEIFA, inflexible temperament, parent control, main caregiver

education, TV and PA frequency. The iterative process revealed that all variables made a significant

impact on the overall model. For example, although SEIFA and PA frequency were not associated

with BMI z-score, they did influence the other variables within the model, thus all variables were

included in the final model

The overall model was significant F (11,548) = 2.61, p = .003; partial ɳ2 = .050 (Table 4). After

adjusting for the covariates, night sleep duration and napping frequency remained significant

predictors of BMI z-score. Post-hoc pairwise comparisons with Sidak corrections indicated that, male

children classified as short sleepers had a .27 mean unit increase in BMI z-score compared to normal

sleepers. In contrast, although there was a main effect of napping frequency, post-hoc comparisons

revealed that there were no significant differences between the napping frequency groups on BMI z-

score. However, Figure 1 illustrates a U-shaped pattern between the four napping groups and BMI z-

score observed in this sample of boys.


Figure 1. Mean BMI z-scores observed for boys in each of the napping frequency groups after

adjusting for night sleep duration, parent control, temperament and main caregiver education.

Discussion

Poor sleep has been implicated in childhood weight gain, and has been suggested as a

potential modifiable mechanism to address unhealthy weight status. However, to date, large

scale studies examining the contribution of poor sleep to overweight and obesity have

primarily focussed on night-time sleep duration. This study aimed to advance knowledge

through investigation of the association of weight status with a broader range of sleep

parameters in a large sample of preschool aged children.

Consistent with previous research, our study identified a significant association between

shorter night-time sleep duration and increased BMI z-score (Scharf and DeBoer, 2015).

Children who slept fewer than 10 hours per night had an increase of 0.22 BMI z-score units

in comparison to children getting more than 10 hours of sleep per night. This finding lends

support to the recently updated international recommendations that preschool aged children


achieve sleep durations of between 10 and 13 hours (Hirshkowitz et al., 2016). No other sleep

parameters were associated with weight status in this sample, including total sleep duration,

which is closely associated with night-sleep duration. The null finding may reflect an

interplay between night-sleep duration and napping, as evidenced in previous research (see

Thorpe et al., 2015).

Consistent with previous research, gender-specific differences of sleep parameters were

observed in the data (Chen et al., 2008; Tatone-Tokuda et al., 2012). After controlling for

significant confounding variables, night sleep duration and napping had independent effects

on BMI z-score for boys. In contrast, no sleep parameters were associated with BMI z-score

for girls. Some have hypothesised that such gender differences may be due to evolutionary

adaptive factors (Chen et al., 2008). Alternatively, gender-specific biological and/or social-

environmental differences may also play a role. In this study, boys had significantly shorter

night-sleep duration than did girls. Boys also had significantly earlier rise times than did girls.

These sleep patterns may reflect neurocognitive differences in maturation, e.g. gender-

specific differences in sleep architecture (Knutson, 2005). Alternatively, social-

environmental factors, such as differences in parental control and subsequent parenting

strategies between genders, may influence night sleep duration and other sleep parameters

(Plancoulaine et al., 2015; Tatone-Tokuda et al., 2012) with subsequent effects on weight

status.

The effect of napping and night sleep duration on BMI z-score across genders warrants

further investigation. All children in this study were recruited from licensed childcare

services, and previous research has shown that childcare sleep policies and practices can

affect both napping frequency (Staton et al., 2016), and night-time sleep duration, both


concurrently and in the longer-term (Fukuda and Asaoka, 2004; Fukuda and Sakashita, 2002;

Staton et al., 2015). It may be that boys are particularly susceptible to these practices.

Even after accounting for significant control variables, the amount of variance explained in

our models, though statistically significant, was low. In the full sample, the model only

explained 3% of variance in total BMI z-score. This may indicate that sleep interacts with

weight status in indirect ways, and that more complex modelling of such interactions

informed by developmental physiology may be needed. Alternatively, this result may reflect

a genuinely modest contribution of sleep to overall weight status. the determinants of weight

status are complex, multifaceted and dynamic. Sleep parameters are one of a multiplicity of

factors contributing to weights status in our modern obesogenic environment. Further

research examining the associations between sleep duration, napping, and weight status in

young children is needed.

Strengths and Limitations

This study has a number of strengths. Firstly, it comprised a large population of children aged

between 3 and 6 years from a study which used stratified-random sampling to capture a wide

range of social and economic experiences of families using ECEC services in Australia.

Furthermore, BMI was directly measured by trained researchers using standardised

procedures, reducing the error that is associated with parent-report. One of the major

limitations of the study, however, is that sleep parameters were based on parent report.

Although parent-reported sleep duration is a commonly employed method in large-scale

studies and has been shown to be correlated with actigraphic recorded sleep duration (Iwasaki

et al., 2010), some research has indicated that parents can overestimate sleep duration by

more than one hour per night (Dayyat et al., 2011). Therefore, replication with objective


measurement (such as actigraphy or polysomnography) may be necessary to improve

understanding of the relationships between sleep parameters and weight status.

Conclusion

In conclusion, shortened sleep duration was associated with increased BMI z-score in this

Australian preschool sample. The results indicated that children sleeping less than 10 hours

per night had higher BMI z, which lends support to the current international recommended

sleep guidelines for children in this age group. In boys, there was a significant independent

effect of both night-sleep duration and napping frequency on weight. It is recognised that

sleep is important in early childhood, and the potential for sleep to elicit better health

outcomes remains.


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Table 1. Definition of the sleep parameters assessed in this study.

Sleep Parameter Description Variable Type

Sleep onset Typical time that sleep commences. Continuous

Sleep offset Typical time that sleep ends. Continuous

Night-sleep

duration

Calculated using the duration of time between typical sleep

onset and offset. Night-time sleep duration was then

categorised into long (>10 hours) and short sleepers (≤10

hours) consistent with previous research and also with the

National Sleep Foundations recommended 10 hours of sleep

for children in preschool age range (Hirshkowitz et al., 2016).

Categorical

(2 levels)

Total-sleep

duration

Calculated by summing napping duration per week with night

time sleep duration. Nap duration per week (Miller et al.,

2014) is calculated by multiplying nap duration by days

napped per week which is then divided by 7 days.

Continuous

Sleep midpoint Calculated as a proxy for circadian timing (Martin and

Eastman, 2002) using the midpoint of the time between sleep

onset and offset, then transformed using a median split into

late sleep midpoint (>1:15am) and early sleep midpoint

(≤1:15am), this is consistent with previous research (Thivel

et al., 2015).

Categorical

(2 levels)

Napping

frequency

As a significant number (848; 76.7%) of children who had

ceased napping, the distribution of napping duration per week

was extremely skewed. Therefore, children were classified

into four napping frequency groups: Non-nappers; Incidental

Nappers (<1 per week); Transitioning nappers (1 – 3 days per

week); Consistent Nappers (4 – 7 days per week).

Categorical

(4 levels)

Sleep problems The responses on the 2 sleep problem questions were

dichotomised into: “no problem/mild’ versus

“moderate/severe”.

Categorical

(2 levels)


Table 2. Demographic Information of the 2011 E4Kids Sample included in the final analysis.

Characteristic Descriptive Sample Size (n)

Child age in years, Mean (SD) 4.86 (0.63) 1111

Child gender (% Boys) 51.8 576

Child ever breastfed? (% Yes) 91.9 1024

Child is Aboriginal and/or Torres Strait Islander origin (%) 1.9 1100

Child diagnosed as having intellectual disability or

development delay6.2 1110

Child BMI z-score, Mean (SD) 0.55 (.96) 1111

Child weight status (IOTF):

Non-overweight 83.8 931

Overweight 12.7 141

Obese 3.5 39

Family Characteristics

Gender of Main Caregiver completing the survey in 2011 (%

Female)92.7 1106

Main Caregiver born in Australia (%) 80.4 1102

SEIFA (relative advantage and disadvantage), Mean (SD) 1036.15 (73.58) 1095

Main Caregiver Education 1100

High school or did not complete high school (%) 17.5 193

Technical certificate or diploma (%) 26.4 290

University bachelors or postgraduate degree (%) 56.1 617


Table 3. Information about the measured sleep parameters.

Whole

Sample Boys Girls Diffa

Sleep Parameter M (SD) M (SD) M (SD) t

Sleep Onset 19:47 (00:37) 19:47 (00:36) 19:48 (00:37) -0.54

Sleep Offset 6:48 (00:37) 6:44 (00:39) 6:52 (00:35) -3.45**

Night sleep duration (Hrs) 11.00 (.62) 10.96 (.63) 11.07 (.60) -2.94*

Total sleep duration (Hrs) 11.13 (.63) 11.10 (.64) 11.17 (.62) -1.94

% % χ2

Sleep Midpoint ≤1:15am 59.9 63.4 56.1 6.14*

Sleep problems

(moderate/severe) 3.9 3.6

4.1 0.16

Napping Frequency 1.02

Non-napper 65.2 64.1 66.3

Incidental 11.7 11.5 11.9

Transitioning 17.3 18.1 16.4

Consistent 5.9 6.3 5.5

aDiff refers to the difference between boys and girls on each of the sleep parameters using

independent-samples t-tests (t) and chi-square (χ2) analyses were appropriate.

*p <.05**p = .001


Table 4. General Linear Model of Night Sleep Durations effect on BMI when controlling for

significant confounding variables

Variable β 95% CI ɳ2p p

Whole Sample (N = 1077)

Night Sleep Duration .222 .034 - .409 .005 .020

Main caregiver education .004 .094

School or did not finish schoola .047 -.116 - .209 .000 .572

Tertiary Study (i.e. Diploma)a .155 .015 - .296 .004 .030

Parent control .035 .014 - .056 .010 .001

Inflexible -.088 -.149 - .028 .008 .004

SEIFA -.001 -.001 - .000 .002 .122

Boys (N = 560)

Night Sleep Duration .268 .007 - .529 .007 .044

Napping Frequency .015 .045

Non napperb .011 -.344 - .366 .000 .950

Incidental napperb .297 -.119 - .712 .004 .162

Transitioning napperb .259 -.125 - .643 .003 .186

Main caregiver education .006 .187

School or did not finish school a .146 -.089 - .382 .003 .223

Tertiary Study (i.e. Diploma) a .173 -.028 - .374 .005 .091

Parent Control .036 .005 - .067 .009 .023

Inflexible -.061 -.147 - .026 .003 .169

SEIFA -.001 -.002 - .001 .001 .379

Screen time frequency -.041 -.094 - .012 .004 .126

PA frequency -.016 -.058 - .027 .001 .471

a The referent group for main caregiver education is parents with bachelors/postgraduate degree.

bThe referent group for napping are consistent nappers


Acknowledgments: The sampling derives from an Australian longitudinal study of ECEC

effectiveness, Effective Early Educational Experiences for Children (E4Kids). E4Kids is a project of

the Melbourne Graduate School of Education at The University of Melbourne and is conducted in

partnership with the Queensland University of Technology. E4Kids is funded by the Australian

Research Council Linkage Projects Scheme (LP0990200), the Victorian Government Department of

Education and Early Childhood Development, and the Queensland Government Department of

Education and Training. E4Kids is conducted in academic collaboration with the University of

Toronto Scarborough, the Institute of Education at the University of London and the Royal Children’s

Hospital in Melbourne. The E4Kids team would like to sincerely thank the ECEC services, directors,

teachers/staff, children and their families for their participation in this study.


Supplementary Material

Table S1. Correlations between BMI z-score, sleep parameters and control variables (N = 1,111)Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 BMI z-score

2 Sleep midpoint .009

3 Sleep onset .019 .702**

4 Sleep offset -.010 .666** .503**

5 Sleep duration -.066* .001 -.296** .351**

6 Total sleep duration -.011 .000 -.389** .493** .547**

7 Napping frequency .043 .029 .137** -.086** -.195** .157**

8 Sleep problems .030 .045 .131** .007 -.085** -.122** -.017

Child Factors

9 Inflexible temperament -.084** -.084** -.130** -.024 .053 .083** -.051 -.113**

10 Discretionary food intake -.034 -.007 .022 -.038 -.051 -.075* -.006 -.025 -.025

11 Screen Time Frequency .001 .109** .132** -.002 -.112** -.103** .085** .025 -.083** .121**

12 PA Frequency -.012 -.071* -.067* -.006 .022 .063* .010 -.018 .106** .008 .036

Family and Environment Factors13 Years centre based care .033 .000 .069* -.042 -.067* -.057 .105** -.022 -.012 -.022 .032 -.022

14 SEIFA -.065* -.122** -.116** -.085** .053 .021 -.016 -.058 -.025 .041 -.055 .060* .132**

15 Main caregiver education -.058 -.054 -.055 -.040 .049 .020 .037 -.055 .031 .079** -.036 .086** .146** .321**

16 Family net income -.042 -.086** -.089** -.131** -.008 -.035 .039 -.050 .014 .002 -.055 .034 .150** .429** .305**

17 Parent Control .087** -.096** -.149** -.039 .099** .101** -.017 .004 .069* .006 -.119** -.049 -.022 .020 .016 .028

* Correlation is significant at the p < 0.05 level ** Correlation is significant at the p < 0.01 level

Table S2. Correlations between BMI z-score, sleep parameters and control variables for girls (N = 535)

Beyond duration: sleep parameters and weight status 24

Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 BMI z-score



4 Sleep offset .041 .665** .507**

5 Sleep duration -.023 -.105* -.381** .225**

6 Total sleep duration .018 -.039 -.436** .460** .547**

7 Napping frequency -.011 .033 .100* -.039 -.088* .203**


8 Sleep problems .040 .082 .237** .093* -.109* -.156** .006

Child Factors

9 Inflexible temperament -.080 -.125** -.199** -.062 .073 .114** -.050 -.138**

10 Discretionary food intake -.090* .008 .016 .002 -.014 -.036 -.043 -.029 .023

11 Frequency of TV watching .044 .128** .134** -.002 -.098* -.130** .054 .054 -.083 .104*

12 Frequency of physical activity .029 .011 -.036 .078 .091* .103* -.019 -.054 .122** -.012 .037


13 Years centre based care .040 -.034 .077 -.033 -.017 -.062 .097* -.007 .042 -.012 .062 .002

14 SEIFA -.087* -.114** -.145** -.089* .089* .050 -.021 -.077 -.088* .103* -.071 .025 .117**

15 Main caregiver education -.032 -.063 -.060 -.065 .058 .022 .056 -.051 -.022 .066 -.046 .047 .175** .339**

16 Family net income -.099* -.056 -.086 -.119** .011 -.010 .056 -.050 -.009 .016 -.017 -.062 .154** .404** .289**

17 Parent Control .072 -.063 -.088* .019 .124** .090* -.024 .009 .089* -.030 -.140** -.050 .018 -.027 .016 .008



Table S3. Correlations between BMI z-score, sleep parameters and control variables for boys (N = 576)Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 BMI z-score



4 Sleep offset -.035 .665** .504**

5 Sleep duration -.088* .077 -.233** .432**

6 Total sleep duration -.026 .028 -.348** .516** .548**

7 Napping frequency .083* .030 .172** -.119** -.274** .120**

8 Sleep problems .024 .006 .023 -.072 -.068 -.092* -.038

Child Factors

9 Inflexible temperament -.076 -.058 -.072 -.008 .031 .050 -.049 -.092*

10 Discretionary food intake .016 -.023 .028 -.073 -.082* -.112** .028 -.022 -.067

11 Frequency of TV watching -.047 .102* .134** .013 -.117** -.071 .110** -.003 -.074 .139**

12 Frequency of physical activity -.053 -.145** -.095* -.069 -.029 .031 .035 .018 .097* .027 .030


13 Years centre based care .021 .037 .062 -.044 -.100* -.048 .110** -.036 -.055 -.030 -.002 -.048

14 SEIFA -.056 -.123** -.087* -.072 .033 .002 -.015 -.038 .038 -.014 -.050 .088* .143**

15 Main caregiver education -.082 -.046 -.050 -.020 .043 .019 .019 -.060 .079 .092* -.028 .123** .120** .304**

16 Family net income .005 -.114** -.091* -.141** -.022 -.056 .023 -.048 .038 -.011 -.093* .126** .147** .454** .322**

17 Parent Control .087* -.116** -.208** -.071 .093* .124** -.015 .002 .065 .042 -.113** -.054 -.068 .054 .016 .044


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education.unimelb.edu.au · Web viewmay be indicative of early dysregulation of circadian timing, resulting in metabolic hormone disruption (e.g. leptin and ghrelin) leading to