Document Detail

A population study of 5 to 15 year olds: full time maternal employment not associated with high BMI. The importance of screen-based activity, reading for pleasure and sleep duration in children's BMI.
Jump to Full Text
MedLine Citation:
PMID:  21505779     Owner:  NLM     Status:  MEDLINE    
To describe the relationship between maternal full time employment and health-related and demographic variables associated with children aged 5-15 years, and the factors associated with child overweight/obesity. Data from a chronic disease and risk factor surveillance system were limited to children aged 5-15 years whose mothers responded on their behalf (n = 641). Univariate/multivariate analyses described the differences between mothers who did and did not work full time. The same data were analysed comparing children who are overweight/obese against those with a normal BMI. The children of mothers who worked full time are more likely to be older, live in a household with a higher household income, be an only child or have one sibling or other child in the household, have a sole mother family structure and not spend any time reading for pleasure. No relationship was found between maternal employment and BMI. Compared with children of normal weight, those who were overweight/obese were more likely to spend no time studying, spend more than 2 h per day in screen-based activity and sleep less than 10 h per night. Child BMI status was not related to maternal employment. Although this analysis included eight diet related variables none proved to be significant in the final models.This study has shown that mothers' working status is not related to children's BMI. The relationship between overweight/obesity of children and high levels of screen-based activity, low levels of studying, and short sleep duration suggests a need for better knowledge and understanding of sedentary behaviours of children.
Anne W Taylor; Helen Winefield; Lisa Kettler; Rachel Roberts; Tiffany K Gill
Related Documents :
21462729 - Maternal immigrant status and high birth weight: implications for childhood obesity.
24047239 - Validity of self-reported lunch recalls in swedish school children aged 6--8 years.
23390819 - Minor physical anomalies in children with hearing impairment and normal controls.
23121139 - Childhood predictors of adolescent competence and self-worth in rural youth.
831369 - School-based immunization clinics. a method for raising community immunization levels.
3723239 - High measles mortality in infancy related to intensity of exposure.
11519899 - Hypercalciuria in ex-preterm children, aged 7-8 years.
17525189 - Oculomotor control in children who were born very prematurely.
20411039 - Emerging parental gender indifference? sex composition of children and the third birth.
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Maternal and child health journal     Volume:  16     ISSN:  1573-6628     ISO Abbreviation:  Matern Child Health J     Publication Date:  2012 Apr 
Date Detail:
Created Date:  2012-03-15     Completed Date:  2012-07-23     Revised Date:  2013-06-30    
Medline Journal Info:
Nlm Unique ID:  9715672     Medline TA:  Matern Child Health J     Country:  United States    
Other Details:
Languages:  eng     Pagination:  587-99     Citation Subset:  IM    
Population Research and Outcome Studies, School of Medicine, University of Adelaide, 122 Frome St Adelaide, Adelaide 5000, South Australia.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Body Mass Index
Child, Preschool
Cross-Sectional Studies
Leisure Activities
Multivariate Analysis
Overweight / epidemiology*
Population Surveillance
Sedentary Lifestyle*
Socioeconomic Factors
Television / utilization
Women, Working*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Full Text
Journal Information
Journal ID (nlm-ta): Matern Child Health J
ISSN: 1092-7875
ISSN: 1573-6628
Publisher: Springer US, Boston
Article Information
Download PDF
© The Author(s) 2011
Electronic publication date: Day: 20 Month: 4 Year: 2011
pmc-release publication date: Day: 20 Month: 4 Year: 2011
Print publication date: Month: 4 Year: 2012
Volume: 16 Issue: 3
First Page: 587 Last Page: 599
ID: 3304066
PubMed Id: 21505779
Publisher Id: 792
DOI: 10.1007/s10995-011-0792-y

A Population Study of 5 to 15 Year Olds: Full Time Maternal Employment not Associated with High BMI. The Importance of Screen-Based Activity, Reading for Pleasure and Sleep Duration in Children’s BMI
Anne W. Taylor1 Address: +61-8-83131211 +61-8-83131228
Helen Winefield23
Lisa Kettler3
Rachel Roberts3
Tiffany K. Gill1
1Population Research and Outcome Studies, School of Medicine, University of Adelaide, 122 Frome St Adelaide, Adelaide, 5000 South Australia
2School of Psychology, The University of Adelaide, Adelaide, 5000 South Australia
3Discipline of Psychiatry, The University of Adelaide, Adelaide, 5000 South Australia


Rising prevalence rates of obesity in school-aged children is of major concern for health policy professionals and public health promoters and has resulted in the implementation of major research projects and intervention programs. Possible causal pathways and mechanisms for the rising prevalence include societal changes such as increased technology usage, television viewing and other screen based activities [1, 2], heightened security concerns which often limit outdoor physical activity [3, 4], increased processed food consumption and other changes in dietary habits [57], and changes in the built environment [8, 9]. Included in the major societal changes that have occurred in recent decades is the increase in mothers undertaking paid work when the children are young. The conflict between work demands and those of home life tends to affect mothers more than fathers and the damage to well-being caused by work-family interference is the subject of much research recently [10]. This change in employment patterns and resultant family home life is cited as a reason, often the main reason, for the increase in child obesity rates [1113]. Other studies have found either no relationship, or an inconsistent relationship, between mothers’ working status and child obesity or nutritional status [14, 15]. Similar mixed results have been reported in pre-school aged children [1618]. It has also been shown that the relationship between child obesity prevalence rates and maternal work practices varies for different cultures and societies [1921].

While research has consistently shown that the rise in obesity rates has coincided over time with the corresponding increase in paid work undertaken by mothers [11], other studies have shown that the actual time mothers spend with their children has remained stable over this same period [22]. What has tended to change in the family home life, as a result of increased maternal workforce participation, are changes in responsibility for domestic chores, a decrease by mothers in volunteer activities and decreases in family sizes [22]. Bianchi [22] has argued that for all the research that has been undertaken trying to find negative relationships between maternal work force participation and children’s wellbeing, consistent results are lacking and often the breakdown of the marriage/relationship has more effect than work participation. Previous studies assessing the relationship between maternal work patterns and child wellbeing have mainly focussed on academic success, cognitive development, and emotional problems of the children and it is only in recent years that health effects, such as obesity, have become a focus in research [23, 24].

Longitudinal studies have shown a relationship between maternal employment and hours worked and overweight in their children [11, 13, 18]. Anderson et al. [11] found that higher socio-economic status mothers, whose work demands are often more intense, are more likely to have overweight children. In addition, longitudinal studies reported that hours worked per week is an important predictor of childhood obesity [11, 18]. While it is acknowledged that these studies provide important evidence of associations, cross-sectional studies such as that presented here often add additional insights. Research into the relationship between maternal work patterns and childhood obesity is cited as being relatively limited [8, 11, 18]. Studies often assess individual issues such as nutritional aspects and physical activity patterns without incorporating the wide range of socio-economic, family and other related behavioural indicators. These include important child-related issues such as screen-based activity and sleeping patterns which have also become important in the debate regarding childhood obesity. The aim of this study is to assess these relationships, using a wide range of relevant indicators, on data collected on randomly selected children, their mothers and their household.


Data on the children and their mothers were collected using the South Australian monitoring and surveillance system (SAMSS), a telephone monitoring system designed to systematically monitor chronic disease, risk factors and other health-related issues on a regular and ongoing basis [25]. A representative cross-sectional sample of approximately 600 people (all ages) is randomly selected each month from all households in South Australia with a telephone connected and the number listed in the electronic white pages. A letter of introduction is sent to the selected household and the person who was last to have a birthday within a 12 month period is chosen for interview. Interviews are conducted by a trained interviewer via a computer assisted telephone interview (CATI) system. Surrogate interviews are undertaken for persons in the household under the age of 16 by the most appropriate person to answer on their behalf. Up to ten call backs are made in an attempt to interview the selected person; there are no replacements for non-respondents.

Although SAMSS has been in operation since 2002, the sample for the current analysis consisted of n = 641 children aged 5 to 15 years for whom a surrogate interview was completed by their mother between March 2008 and December 2009, with March 2008 being the first month that a question assessing soft drink consumption was asked. Respondents for whom body mass index (BMI) information was unavailable were excluded. The monthly response rate for the survey ranged from 60.1 to 69.3% with an average of 63.8%.

Details on the mother included: highest education level achieved, employment status, and number of hours worked per week for those who indicated employment of any kind. Classification as either full-time or part-time employment was determined according to a cut-off of 35 h per week.

Child specific questions included: gender; age; overall health status assessed using a single item (SF-1) from the SF-36 [26]; child weight status assessed using BMI, calculated using self-reported height and weight information using the classification of Cole et al. [27]; current asthma status (doctor diagnosed and symptoms currently present); and mental health problems (defined as ‘quite a lot’ to ‘very much’ trouble with emotions, concentration, behaviour or getting on with people).

Child dietary habit questions included daily consumption of: recommended serves of fruit and vegetables [28]; processed meat (meat products such as sausages, frankfurters, devon (fritz), salami, meat pies, bacon or ham); fast food (meals or snacks such as burgers, pizza, chicken or chips from places like McDonalds, Hungry Jacks, Pizza Hut or Red Rooster); potatoes (french fries, fried potatoes or potato crisps); juice (fruit or vegetable juice not including fruit juice drinks and fruit drinks (e.g. Fruitbox)); water; and soft/sport drink (includes drinks such as coke, lemonade, flavoured mineral water, Powerade or Gatorade). Assessment of physical activity included asking about the time spent per day doing organised sport; reading for pleasure; studying or doing homework; sleeping; and participating in screen-based activities such as watching television (TV), videos or playing video or computer games. Questions related to cultural background were also asked, including the child’s country of birth and whether the child was from an Aboriginal or Torres Strait Islander background. However, these questions were not included in the statistical analysis due to small numbers of respondents from culturally and linguistically diverse backgrounds.

Household specific data collected that related to both the mother and the child included: annual household income, socio-economic status (SES) (measured by classifying postcode using the Australian socio-economic index for areas (SEIFA) 2001 index of relative socio-economic disadvantage (IRSD) Quintiles) [29], area of residence, family structure, financial situation, and whether the home was owned or being rented.

Data were re-weighted by age, sex, area and probability of selection in the household to estimated resident population data so that the results were representative of the South Australian population aged 5 to 15 years. Data were analysed using SPSS for Windows Version 17.0 [30].

Two analyses were undertaken. Firstly, associations between full-time working mothers compared to part-time or economically inactive (not employed mothers including home duties), and a range of socio-demographic and health-related variables were determined using univariate analyses. Chi-square tests were undertaken to compare differences. A multivariate logistic regression model was subsequently developed, including all variables with a P-value < 0.25 at the univariate level [31], in order to ascertain independently associated factors. The second set of analyses followed the same procedure but assessed child overweight and obese status with the range of socio-demographic and health-related variables including mother’s work status. An alpha level of 0.05 was employed for all statistical tests.


The mean age of the children was 10.15 years (SD = 3.16). Overall, 49.4% were male. The mean number of hours worked per week for mothers reporting employment was 26.11 (SD = 13.02). BMI for children ranged between 6.5 and 54.6 (M = 18.34, SD = 4.36), with 24.2% (n = 155) consequently classed as being overweight or obese.

Table 1 details child health status variables grouped by mothers work classification with significant differences by consumption of fruit, daily organised sport activities, and number of hours spent reading for pleasure and sleeping. Table 2 highlights the univariate analysis assessing the range of variables comparing mothers who work full time with a combined category of mothers who work part-time or who do not work. Table 3 details the multivariate model (model X2 = 12.77, P = 0.12) with children with full-time employed mothers more likely to be older, live in a household with a higher household income, live in the country, be an only child or one of two children in the household, have a sole mother family structure and not spend any time reading for pleasure.

The second univariate and multivariate analyses determining the variables associated with children classified as overweight or obese, are highlighted in Tables 4 and 5. In the final multivariate model (model X2 = 38.17, P < .001), compared with children of normal weight, those who were overweight or obese were more likely to spend no time studying, spend more than 2 h per day in screen-based activity and sleep less than 10 h per night.


This study has shown that children whose mother’s are working full time, as compared with children whose mothers work part time or not at all, are not more likely to be overweight or obese. In terms of behaviours, these children are less likely to be reading for pleasure. When the same data were analysed to assess the best joint predictors of a child who is overweight or obese compared to normal BMI children, full time maternal work status was again not one of the variables in the final model. The overweight or obese child was more likely to spend at least 2 h a day on screen based activities and undertake no studying per day outside of school hours and sleep less than 10 h per night.

The prevalence of overweight/obesity in children found in this study of 24.2% is consistent with other Australian studies. Booth et al. [32] reported rates of 25.7% for younger boys (7 years), 26.1% for older boys (15 years) and corresponding rates of 24.8 and 19.8% for girls in a 2004 study. Waters et al. [33] reported 31% of ethnic children aged 4–13 years overweight/obese in a Melbourne setting and earlier 1995 figures Magarey et al. [34] reported overweight/obesity figures for 7 to 15 year old Australian children of 20–21%. Cretikos et al. [35] reported 29.6% of nearly 13,000 children, who visited a doctor in Australian general practices and who had their height and weight measured, were overweight or obese.

We acknowledge several weaknesses in this cross-sectional study. The self-report nature of the data collection could result in socially desirable responses or problems with recall. While an English study reported that parents overestimated their children’s physical activity considerably [36], there is little evidence of socially desirable responses in this study with many of the findings not necessarily in the direction of acceptable social norms. Notwithstanding, self-reported height and weight has been shown to be an issue due to a problem with recall [37] and there is no reason to suspect that this was any different in this study. A further weakness is the exclusion of interviews where the child’s height and weight were not known by the mother. No details are available to indicate the BMI of these children.

Additional bias could also be expected based on that fact that while the mother may be classified as having a certain work status at the time of the survey, no details on the time in that status were obtained and the mother in our analyses may have been working full time for a short duration only. Other important indicators that could affect BMI status of the child such as breastfeeding and birth weight were also not available. The response rate of nearly 64% is acceptable but nevertheless it could be that busy working mothers might be non-responders and hence add to the possible bias of results. Notwithstanding, the strength of this study includes the random nature of the sample and the large number and variety of the associated variables.

The findings in the initial multivariate analysis that the children of full time working mothers are more likely to be older, that the more children a mother has, the less likely she is to work full time, and that the household has a higher household income are not surprising. Interestingly, included in the model related to maternal employment was the variable that assessed the amount of reading undertaken for pleasure, with the children of mothers who worked full time significantly more likely to report no reading after school hours. Leatherdale and Wong [38] reported that 48.1% of high school students spent less than 1 h per week reading although it has been shown that the amount of time patents spent listening to their young children (8–9 year olds) was related to reading accuracy and comprehension [39]. Perhaps, in the busy lives of full time working mothers, this is one area that is being overlooked and could be a potentially important area of intervention for schools, childcare facilities and after and before school care services.

Although this analysis included eight diet related variables none proved to be significant in the final model assessing the best joint predictors associated with full time maternal employment. The lack of a relationship between broad based diet quality and maternal employment has also been reported by Johnston et al. [16] although their study group was younger children (aged 2 to 5 years).

In the second analysis undertaken to determine the best joint predictor of overweight/obese children, no demographic variables were included in the final model. The SES specific variable included in the analyses (SEIFA) was also not significant in this final multivariate model. The three behaviour related variables included in the final model were more than 2 h of screen-based activities per day, no time spent studying out of school hours, and sleeping less than 10 h per night. Although previous research has shown a relationship between increased screen-based activities and an unhealthy BMI in children [4042] the relationship between increased screen-based activity and inactivity is less convincing [2]. TV viewing has been shown to be the favourite leisure time activity for adolescent boys on both weekends and weekdays [43] and other studies have highlighted the playing of computer games and other technology based activity being lower for girls [2, 44]Studies have shown positive results in studies and intervention aimed at reducing TV viewing in children [4547]. Russ et al. [48] reported that each additional hour of TV viewing was associated with greater odds of overweight/obesity although others have reported that it is more likely the advertising on TV rather than the sedentary behaviours that is associated with obesity [49].

Leatherdale and Wong [38] have previously highlighted the relationship between unhealthy weight and less time spent on homework in their Canadian study of high school students. They also reported the relationship between high levels of screen-based activity, low levels of studying and overweight/obesity and suggest a need for better knowledge and understanding of sedentary behaviours of this priority population.

The relationship between short sleep duration and overweight/obesity has been consistently shown in epidemiological cross-sectional studies [50, 51] highlighting the fact that children who have short sleep duration are at increased risk of being overweight/obese. This may be related to metabolic disturbance [50] with a corresponding increase in appetite and caloric intake [51]. It has also been suggested that short sleep duration, especially in adults, may be a marker of inappropriate lifestyle characteristics again highlighting the need for early intervention.

The duality of being a mother of a school aged child or children and being a paid full time employee rests heavily for many mothers [10]. In this analysis, whether analysed from a maternal work status point of view or when assessing school aged children who are overweight or obese, the relationship between weight issues and maternal work status did not prove significant in the final multivariate models. The fact that the children of fulltime working mothers were less likely to be reading for pleasure might be a concern for both parents and education policy makers. What was interesting was the level of screen-based activity and sleeping patterns associated with overweight/obese children. This leaves open the opportunity for targeted interventions that should perhaps look beyond the family as the focus. This study has added to the debate regarding full time working mothers and has found that there is no relationship between maternal full time work and child BMI. Notwithstanding some important areas of concern (reading for pleasure, sleeping patterns and screen based activity) have been shown to be important indicators and interventions should be considered before these ‘un-healthy’ relationships set the scene for adult behaviours and manifests into increased health care costs.

Open Access

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

1.. Gortmaker SL,Must A,Sobol AM,Peterson K,Colditz GA,Dietz WH. Television viewing as a cause of increasing obesity among children in the United States, 1986–1990Archives of Pediatrics and Adolescent MedicineYear: 1996150435636210.1001/archpedi.1996.021702900220038634729
2.. Briddle SJ,Gorely T,Stensel DJ. Health-enhancing physical activity and sedentary behaviour in children and adolescentsJournal of Sports ScienceYear: 200422867970110.1080/02640410410001712412
3.. Miles R,Panton L. The influence of the perceived quality of community environment on low-income women’s efforts to walk moreJournal of Community HealthYear: 200631537939210.1007/s10900-006-9021-917094646
4.. Sallis JF,Bauamn A,Pratt M. Environmental and policy interventions to promote physical activityAmerican Journal of Preventive MedicineYear: 199815437939710.1016/S0749-3797(98)00076-29838979
5.. Nielson SJ,Siega-Riz AM,Popkin BM. Trends in energy intake in US between 1977 and 1996: similar shifts seen across age groupsObesity ResearchYear: 200210537037810.1038/oby.2002.5112006636
6.. Prentice AM,Jebb SA. Fast foods, energy density and obesity: A possible mechanistic linkObesity ResearchYear: 20034418719410.1046/j.1467-789X.2003.00117.x
7.. Bowman SA,Gortmaker SL,Ebbeling CB,Pereira MA. Effects of fast-food consumption on energy intake and diet quality among children in a national household surveyPediatricsYear: 200411311211810.1542/peds.113.1.11214702458
8.. Anderson PM,Butcher KE. Childhood obesity: trends and potential causesFuture of ChildrenYear: 2006161194510.1353/foc.2006.000116532657
9.. Powell LM,Auld C,Chaloupka FJ,O’Malley PM,Johnston LD. Associations between access to food stores and adolescent body mass indexAmerican Journal of Preventive MedicineYear: 2007334SS301S30710.1016/j.amepre.2007.07.00717884578
10.. Ford MT,Heinen BA,Langkamer KL. Work and family satisfaction and conflict: a meta-analysis of cross-domain relationsJournal of Applied PsychologyYear: 2007921578010.1037/0021-9010.92.1.5717227151
11.. Anderson PM,Butcher KF,Levine PB. Maternal employment and overweight childrenJournal of Health Economics.Year: 20032247750410.1016/S0167-6296(03)00022-512683963
12.. Hinke Kessler Scholder S. Maternal employment and overweight children: does timing matter?Health EconomicsYear: 200817888990610.1002/hec.135718566969
13.. Phipps SA,Lethbridge L,Burton P. Long-run consequence of parental paid work hours for child overweight status in CanadaSocial Science and MedicineYear: 200662497798610.1016/j.socscimed.2005.06.05416099085
14.. Lamerz A,Kuepper-Nybelen J,Wehle C,et al. Social class, parental education, and obesity prevalence in a study of 6-year-old children in GermanyInternational Journal of ObesityYear: 20052937338010.1038/sj.ijo.080291415768043
15.. Sweeting H,West P. Dietray habits and childrne’s family livesJournal of Human Nutritional Dietet.Year: 200518939710.1111/j.1365-277X.2005.00592.x
16.. Johnston RK,Smiciklas-wright H,Crouter AC,Willits FK. Maternal employment and the quality of young children’s diets: empirical evidence based on the 1987–1988 Nationwide food consumption surveyPediatricsYear: 19929022452491641290
17.. Mindlin M,Jenkins R,Law C. Maternal employment and indicators of child health: a systematic review of pre-school children in OECD countriesJournal of Epidemiology and Community HealthYear: 20096334035010.1136/jech.2008.07707319193668
18.. Hawkins SS,Cole TJ,Law C. The Millennium Cohort Study Child Health Group. Maternal employment and early childhood overweight: findings from the UK Millennium Cohort StudyInternational Journal of Obesity.Year: 200832303810.1038/sj.ijo.080368217637703
19.. Gaina A,Sekine M,Chandola T,Marmot M,Kagamimori S. Mother employment status and nutritional patterns in Japanese junior high schoolchildrenInternational Journal of Obesity.Year: 20093375375710.1038/ijo.2009.10319528966
20.. Baker E,Balistreri KS,Hook J. Maternal employment and overweight among Hispanic children of immigrants and children of nativesJournal of Immigration and Minority HealthYear: 200911315816710.1007/s10903-007-9096-0
21.. Waters E,Ashbolt R,Gibbs L,et al. Double disadvantage: the influence of ethnicity over socioeconomic position on childhood overweight and obesity: Findings from an inner urban population of primary school childrenInternational Journal of Pediatrics ObesityYear: 20083419620410.1080/17477160802141846
22.. Bianchi SM. Maternal employment and time with children: Dramatic change or surprising continuityDemographyYear: 200037440141410.1353/dem.2000.000111086567
23.. Waldfogel J,Han WJ,Brooks-Gunn J. The effects of early maternal employment on child cognitive developmentDemographyYear: 200239236939210.1353/dem.2002.002112048957
24.. Gordon RA,Kaestner R,Korenman S. The effects of maternal employment on child injuries and infectious diseaseDemographyYear: 200744230733310.1353/dem.2007.001117583307
25.. Population Research and Outcome Studies Unit: The South Australian Monitoring and Surveillance System (SAMSS) (2002). Brief report 2002–20. Adelaide: South Australian Department of Health.
26.. Kerr EA,Smith DM,Kaplan SH,Hayward RA. The association between three different measures of health status and satisfaction among patients with diabetesMedical Care Research and Review.Year: 20036015817710.1177/107755870306000200212800682
27.. Cole T,Bellizzi M,Flegal K,Dietz W. Establishing a standard definition for child overweight and obesity worldwide: International surveyBritish Medical JournalYear: 200032072441610.1136/bmj.320.7244.124010617503
28.. Australian guide to healthy eatingYear: 1998CanberraCommonwealth of Australia
29.. Australian Bureau of Statistics. (2004). Census of population and housing: Socioeconomic Indexes for Areas (SEIFA). Technical paper. Australia 2001. Canberra: ABS.
30.. Statistical Package for the Social Sciences Inc. (2006). SPSS 15.0 for Windows. Chicago: SPSS Inc.
31.. Hosmer D,Lemeshow S. Applied logistic regressionYear: 1989New YorkWiley
32.. Booth Ml,Dobbins T,Okely AD,Denney-Wilson E,Hardy LL. Trends in the prevalence of overweight and obesity among young Australians, 1985, 1997, 2004Obesity.Year: 20071551089109510.1038/oby.2007.58617495184
33.. Waters E,Ashbolt R,Gibbs L,et al. Double disadvantage: the influence of ethnicity over socioeconomic position on childhood overweight and obesity: Findings from an inner suburb population of primary school childrenInternational Journal of Pediatric ObesityYear: 20083419620410.1080/1747716080214184618608640
34.. Magarey AM,Daniels LA,Boulton TJC. Prevalence of overweight and obesity in Australian children and adolescents: Reassessment of 1985 and 1995 data against new standard international definitionsMedical Journal of AustraliaYear: 200117456156511453327
35.. Certikos MA,Valenti L,Britt HC,Baur LA. General practice management of overweight and obesity in children and adolescents in AustraliaMedical CareYear: 200846111163116910.1097/MLR.0b013e318179259a18953227
36.. Basterfield L,Adamson AJ,Parkinson KN,et al. Surveillance of physical activity in the UK is flawed: Validation of the health survey for England physical activity questionnaireArchives of Disease in ChildhoodYear: 200893121054105810.1136/adc.2007.13590518782845
37.. Taylor AW,Dal Grande E,Gill TK,et al. How valid are self-reported height and weight? A comparisons between CATI self-report and clinic measurements using a large representative cohort studyAustralian New Zealand Journal of Public HealthYear: 20063023824610.1111/j.1467-842X.2006.tb00864.x
38.. Leatherdale ST,Wong SL. Modifiable characteristics associated with sedentary behaviours among youthInternational Journal of Pediatric ObesityYear: 2008329310110.1080/1747716070183087918465435
39.. Wilks RT,Clarke VA. Training versus non-training of mothers as home reading tutorsPerceptual and Motor SkillsYear: 198867113514210.2466/pms.1988.67.1.1353211664
40.. Wiecha JL,Peterson KE,Ludwig DS,Kim J,Sobel A,Gortmaker SL. When children eat what they watch: impact of television viewing on dietary intake in youthArchives of Pediatrics and Adolescent MedicineYear: 2006160443644210.1001/archpedi.160.4.43616585491
41.. Lowry R,Wechsler H,Galuska DA,Fulton JE,Kann L. Television viewing and its associations with overweight, sedentary lifestyle, and insufficient consumption of fruits and vegetables among US high school students: Differences by race, ethnicity and genderJournal of School HealthYear: 2002721041342110.1111/j.1746-1561.2002.tb03551.x12617028
42.. Scully M,Dixon H,White V,Beckmann K. Dietary, physical activity and sedentary behaviour among Australian secondary students in 2005Health Promotion InternationalYear: 200722323624510.1093/heapro/dam02117693446
43.. Gorely T,Marshall SJ,Biddle SJH. Couch kids: Correlates of television viewing among youthInternational Journal of Behavioural MedicineYear: 200411315216310.1207/s15327558ijbm1103_4
44.. Atkin AJ,Gorely T,Biddle SJ,Marshall SJ,Cameron N. Critical hours: physical activity and sedentary behaviour of adolescents after schoolPediatric Exercise ScienceYear: 200820444645619168921
45.. Escobar-Chaves SL,Markham CM,Addy RC,Greisinger A,Murray NG,Brehm B. The Fun Families Study: Interventions to reduce children’s TV viewingObesityYear: 201018Suppl 1S99S10110.1038/oby.2009.43820107469
46.. Todd MK,Reis-Bergan MJ,Sidman CL,et al. Effect of a family-based intervention on electronic media use and body composition among boys aged 8–11 years: A pilot studyJournal of Child Health CareYear: 200812434435810.1177/136749350809740419052191
47.. Epstein LH,Roemmich JN,Robinson JL,et al. A randomised trial of the effects of reducing television viewing and computer use on body mass index in young childrenArchives of Pediatrics and Adolescent MedicineYear: 2008162323924510.1001/archpediatrics.2007.4518316661
48.. Russ SA,Larson K,Franke TM,Halfon N. Associations between media use and health in US childrenAcademic PediatricsYear: 20099530030610.1016/j.acap.2009.04.00619592321
49.. Zinnermann FJ,Bell JF. Associations of television content type and obesity in childrenAmerican Journal of Public HealthYear: 2010100233434010.2105/AJPH.2008.15511920019313
50.. Taheri S. The link between short sleep duration and obesity: We should recommend more sleep to prevent obesityArchives of Disease in ChildhoodYear: 2006911188188410.1136/adc.2005.09301317056861
51.. Cappuccio FP,Taggeart FM,Kandala NB,et al. Meta-analysis of short sleep duration and obesity in children and adultsSleepYear: 200831561962618517032

[TableWrap ID: Tab1] Table 1 

Child (aged 5 to 15 years) health factors by maternal work status, South Australia

Full time employed Part time employed Economically Inactive
n % n % n %
Overweight or obese
 No 96 74.3 265 76.4 124 75.5
 Yes 33 25.7 82 23.6 40 24.5
Overall health status
 Excellent/Very good/Good 128 98.2 334 97.7 155 94.1
 Fair/Poor 2 1.8 8 2.3 10 5.9
Current asthma
 No 114 88.0 321 92.7 145 88.1
 Yes 16 12.0 25 7.3 20 11.9
Mental health problems
 No 112 86.1 316 91.2 141 85.8
 Yes 18 13.9 30 8.8 23 14.2
Consuming recommended daily intake of vegetables
 Yes 47 35.8 132 38.2 69 42.0
 No 83 64.2 214 61.8 95 58.0
Consuming recommended daily intake of fruit
 Yes 74 56.7↓ 232 66.9 121 73.7↑
 No 56 43.3↑ 115 33.1 43 26.3↓
French fries, fried potato or crisps consumptiona
 One a week or less (inc. never) 66 50.4 200 57.6 93 56.4
 More than once a week 64 49.6 146 42.2 71 43.3
Processed meat consumptiona
 One a week or less (inc. never) 55 42.0 165 49.5 76 46.0
 More than once a week 75 58.0 181 52.4 89 54.0
Fast food consumptiona
 Less than once a week (inc. never) 73 56.3 221 63.7 110 67.0
 Once a week or more 57 43.7 125 36.1 54 33.0
Water consumed per daya
 Less than 8 glasses 114 87.4 313 90.5 152 92.1
 8 glasses or more 16 12.6 30 8.7 12 7.3
Juice consumed per daya
 None 66 51.2 176 50.7 92 55.7
 Some 63 48.6 168 48.6 73 44.3
Soft drink consumed per daya
 None 98 75.5 287 82.9 128 77.6
 Some 31 24.2 54 15.5 36 21.7
Daily organised sporta
 None 25 19.2 70 20.1 50 30.3↑
 Less than 0.5 h 55 42.1 151 43.6 79 47.9
 More than 0.5 h 50 38.3 121 34.9 33 19.7↓
Daily reading for pleasurea
 None 34 26.1↑ 59 17.1 22 13.6
 Less than 0.5 h 60 45.8 176 50.8 72 43.9
 More than 0.5 h 37 28.1 106 30.5 70 42.5↑
Daily time spent studyinga
 None 13 10.0 37 10.8 22 13.4
 Less than 0.5 h 68 52.0 190 54.8 86 51.9
 More than 0.5 h 49 38.0 115 33.1 57 34.4
Daily screen based activitya
 None to 0.5 h 14 10.9 43 12.4 16 9.5
 0.5 h to 1 h 40 31.0 115 33.1 51 30.9
 1 h to 2 h 44 34.2 124 35.7 58 35.2
 More than 2 h 31 23.9 65 18.7 39 23.9
Daily time spent sleepinga
 Up to 8 h 44 34.1↑ 73 21.0 31 18.8
 8 to 9 h 32 24.6 72 20.7 35 21.2
 9 to 10 h 28 21.8↓ 105 30.3 53 32.5
 More than 10 h 25 19.6 92 26.5 45 27.5

a‘Don’t know’ option not reported

↓↑Statistically significantly higher or lower (P < 0.05) compared to other maternal employment categories combined

[TableWrap ID: Tab2] Table 2 

Univariate odds ratios of socio-demographic and health factors associated with children aged 5 to 15 whose mothers work full-time as compared to part-time or economically inactive

n % OR 95% CI P value
 Female 61/325 18.8 1.00
 Male 69/316 21.8 1.21 (0.82–1.78) 0.337
 5 to 7 year olds 14/166 8.7 1.00  
 8 to 11 year olds 53/234 22.4 3.04 (1.63–5.66) <0.001
 12 to 15 year olds 63/240 26.2 3.74 (2.03–6.89) <0.001
Household income
 Up to $40,000 6/92 6.6 1.00  
 $40,001 to $60,000 20/114 17.2 2.94 (1.13–7.66) 0.027
 $60,001 to $80,000 21/122 17.4 3.00 (1.16–7.74) 0.023
 $80,001 or more 77/267 28.9 5.78 (2.43–13.75) <0.001
 Not stated 6/47 12.4 2.01 (0.61–6.68) 0.253
 Low 15/98 14.9 1.00   0.524
 Lowest 31/132 23.4 1.74 (0.88–3.46) 0.114
 Middle 23/125 18.3 1.28 (0.62–2.62) 0.504
 High 29/129 22.5 1.66 (0.83–3.31) 0.154
 Highest 32/157 20.7 1.49 (0.76–2.94) 0.247
 Metropolitan 75/424 17.8 1.00    
 Country 54/217 25.1 1.55 (1.04–2.30) 0.030
Children in the household
 3 or more 22/193 11.2 1.00  
 2 children 59/302 19.5 1.93 (1.13–3.28) 0.016
 1 child 49/145 34.1 4.11 (2.34–7.23) <0.001
Maternal highest educational achievement
 No schooling to secondary 49/296 16.5 1.00  
 Trade/certificate/diploma 41/188 22.0 1.42 (0.90–2.26) 0.134
 Degree or higher 40/157 25.4 1.72 (1.07–2.76) 0.025
Family structure
 Child or children living with biological parents 97/523 18.5 1.00   
 Sole mother 20/84 23.7 1.36 (0.79–2.36) 0.268
 Other (step/blended/shared) 13/33 38.6 2.76 (1.33–5.74) 0.007
Financial status
 Can save a bit/a lot 85/393 21.7 1.00  
 Just enough to last until next pay/spend whatever left over 34/206 16.7 0.73 (0.47–1.12) 0.150
 Spend more than earn 8/33 25.1 1.21 (0.53–2.75) 0.647
Dwelling status
 Rented 11/62 18.0
 Owned or being purchased 118/574 20.5 1.18 (0.60–2.32) 0.638
 Other 1/5 16.5 0.90 (0.08–10.67) 0.932
Current asthma
 No 114/580 19.7 1.00    
 Yes 16/61 25.8 1.42 (0.77–2.61) 0.261
Mental health problems
 No 112/569 19.7 1.00    
 Yes 18/72 25.1 1.37 (0.77–2.43) 0.279
Overweight or obese
 No 96/486 19.9 1.00    
 Yes 33/155 21.5 1.10 (0.71–1.72) 0.663
Consuming recommended daily intake of vegetables
 Yes 47/248 18.7 1.00    
 No 83/393 21.2 1.17 (0.78–1.74) 0.446
Consuming recommended daily intake of fruit
 Yes 74/427 17.3 1.00    
 No 56/214 26.2 1.70 (1.15–2.53) 0.008
French fries, fried potato or crisps consumption
 Once a week or less (inc. never) 66/358 18.3 1.00  
 More than once a week 64/282 22.8 1.32 (0.9–1.94) 0.157
Processed meat consumption
 Once a week or less (inc. never) 55/295 18.5 1.00  
 More than once a week 75/346 21.8 1.23 (0.83–1.81) 0.301
Fast food consumption
 Less than once a week (inc. never) 73/404 18.1 1.00  
 Once a week or more 57/236 24.0 1.43 (0.97–2.12) 0.073
Water consumed per day
 Less than 8 glasses 114/579 19.6 1.00  
 8 glasses or more 16/59 27.9 1.59 (0.87–2.91) 0.135
Juice consumed per day
 None 66/334 19.9 1.00
 Some 63/304 20.7 1.05 (0.72–1.55) 0.798
Soft/sport drink consumed per day
 None 98/513 19.1 1.00  
 Some 31/121 26.0 1.49 (0.94–2.36) 0.093
Daily organised sport
 None 25/144 17.3 1.00  
 Less than 0.5 h 55/285 19.2 1.14 (0.68–1.92) 0.624
 More than 0.5 h 50/203 24.5 1.55 (0.91–2.66) 0.108
Daily reading for pleasure
 More than 0.5 h 37/212 17.2 1.00    
 Less than 0.5 h 60/308 19.4 1.16 (0.73–1.82) 0.535
 None 34/115 29.3 2.00 (1.17–3.41) 0.012
Daily time spent studying
 None 13/72 17.9 1.00  
 Less than 0.5 h 68/343 19.7 1.12 (0.58–2.17) 0.727
 More than 0.5 h 49/221 22.4 1.32 (0.67–2.61) 0.421
Daily screen based activity
 None to 0.5 h 14/73 19.5 1.00  
 0.5 h to 1 h 40/206 19.6 1.01 (0.51–1.98) 0.981
 1 h to 2 h 44/226 19.6 1.01 (0.52–1.97) 0.975
 More than 2 h 31/135 23.0 1.24 (0.61–2.50) 0.556
Daily time spent sleeping
 More than 10 h 25/163 15.6 1.00  
 8 to 10 h 60/326 18.5 1.23 (0.74–2.04) 0.430
 Up to 8 h 44/148 29.9 2.30 (1.33–3.99) 0.003

Data source: SAMSS March 2008–December 2009

Bold indicates statistically significant at P < 0.05

[TableWrap ID: Tab3] Table 3 

Multivariate odds ratios of socio-demographics and health factors independently associated with children aged 5 to 15 whose mothers work full-time as compared to part-time or economically inactive

OR 95% CI P value
 5 to 7 year olds 1.00     
 8 to 11 year olds 3.46 1.76–6.81 <0.001
 12 to 15 year olds 2.93 1.50–5.71 0.002
Household income
 Up to $40,000 1.00    
 $40,001 to $60,000 8.02 2.58–24.94 0.001
 $60,001 to $80,000 12.78 3.94–41.45 0.001
 $80,001 or more 27.25 8.68–85.53 <0.001
 Not stated 3.51 0.94–13.13 0.062
 Metropolitan 1.00     
 Country 1.93 1.23–3.03 0.004
Children in the household
 3 or more 1.00     
 2 children 2.13 1.19–3.83 0.011
 1 child 5.25 2.68–10.29 <0.001
Family structure
 Child or children living with biological parents 1.00
 Sole mother 4.64 2.02–10.57 <0.001
 Other (step/blended/shared) 2.85 1.23–6.57 0.14
Daily reading for pleasure
 More than 0.5 h 1.00
 Less than 0.5 h 1.15 0.69–1.91 0.598
 None 2.21 1.20–4.08 0.011

Data source: SAMSS March 2008–December 2009

Bold indicates statistically significant at P < 0.05

[TableWrap ID: Tab4] Table 4 

Univariate odds ratios of socio-demographic and health factors associated with overweight and obese children as compared with normal weight children, aged 5 to 15

n % OR 95% CI P value
 Female 80/325 24.5 1.00  
 Male 76/316 24.0 0.97 (0.68–1.40) 0.881
 12 to 15 year olds 54/240 22.4 1.00    
 8 to 11 year olds 59/234 25.0 1.15 (0.75–1.76) 0.513
 5 to 7 year olds 43/166 25.9 1.21 (0.76–1.91) 0.423
Maternal employment status
 Unemployed/economically inactive 40/165 24.5 1.00    
 Part-time employed 82/346 23.6 0.95 (0.62–1.46) 0.816
 Full-time employed 33/130 25.7 1.07 (0.63–1.81) 0.813
Household income
 $80,001 or more 58/267 21.8 1.00    
 $60,001 to $80,000 23/122 18.5 0.82 (0.47–1.40) 0.461
 $40,001 to $60,000 32/114 28.4 1.42 (0.86–2.35) 0.168
 Up to $40,000 29/92 31.9 1.68 (0.99–2.85) 0.053
 Not stated 13/47 28.4 1.42 (0.71–2.87) 0.324
 Highest 28/157 17.9 1.00    
 High 33/129 25.7 1.59 (0.90–2.80) 0.111
 Middle 38/125 30.3 1.99 (1.14–3.49) 0.015
 Low 29/132 21.7 1.27 (0.71–2.28) 0.417
 Lowest 27/98 28.0 1.78 (0.98–3.25) 0.060
 Metropolitan 100/424 23.5 1.00  
 Country 56/217 25.7 1.13 (0.77–1.64) 0.539
Children in the household
 3 or more 43/193 22.2 1.00
 2 children 74/302 24.4 1.13 (0.73–1.73) 0.581
 1 child 39/145 26.8 1.28 (0.78–2.12) 0.329
Maternal highest educational achievement
 Degree or higher 28/157 17.7 1.00    
 Trade/certificate/diploma 49/188 26.3 1.66 (0.99–2.81) 0.057
 No schooling to secondary 78/296 26.5 1.68 (1.03–2.73) 0.036
Family structure
 Child or children living with biological parents 117/523 22.4 1.00
 Sole mother 30/84 35.0 1.87 (1.14–3.06) 0.013
 Other (step/blended/shared) 9/33 26.3 1.24 (0.56–2.75) 0.603
Financial status
 Can save a bit/a lot 88/393 22.3 1.00
 Just enough to last until next pay/spend whatever left over 56/206 27.3 1.30 (0.89–1.92) 0.179
 Spend more than earn 11/33 32.2 1.65 (0.77–3.56) 0.198
Dwelling status
 Owned or being purchased 137/574 23.8 1.00
 Rented 18/62 29.1 1.31 (0.74–2.35) 0.357
 Other 1/5 14.0 0.52 (0.04–6.75) 0.619
Current asthma
 No 139/580 24.0 1.00  
 Yes 16/61 26.8 1.16 (0.64–2.11) 0.632
Mental health problems
 No 134/569 23.6 1.00  
 Yes 21/72 29.4 1.35 (0.78–2.32) 0.280
Recommended consumption of vegetable serves
 Yes 57/248 22.9 1.00  
 No 99/393 25.1 1.13 (0.78–1.64) 0.518
Recommended consumption of fruit serves per day
 Yes 103/427 24.3 1.00  
 No 52/214 24.2 1.00 (0.68–1.46) 0.993
French fries, fried potato or crisps consumption
 Once a week or less (inc. never) 88/358 24.5 1.00
 More than once a week 68/282 24.0 0.97 (0.68–1.40) 0.891
Processed meat consumption
 Once a week or less (inc. never) 69/295 23.3 1.00
 More than once a week 87/346 25.1 1.10 (0.77–1.59) 0.591
Fast food consumption
 Less than once a week (inc. never) 87/404 21.6 1.00
 Once a week or more 68/236 28.8 1.47 (1.02–2.12) 0.041
Water consumed per day
 Less than 8 glasses 136/579 23.6 1.00
 8 glasses or more 19/59 32.6 1.57 (0.88–2.80) 0.126
Juice consumed per day
 Some 64/304 21.0 1.00  
 None 91/334 27.4 1.42 (0.98–2.04) 0.063
Soft/sport drink consumed per day
 None 117/513 22.8 1.00
 Some 35/121 29.0 1.38 (0.89–2.15) 0.155
Daily organised sport
 More than 0.5 h 45/203 22.0 1.00    
 Less than 0.5 h 67/285 23.5 1.09 (0.71–1.67) 0.707
 None 40/144 27.9 1.37 (0.84–2.25) 0.207
Daily reading for pleasure
 More than 0.5 h 52/212 24.6 1.00    
 Less than 0.5 h 68/308 22.1 0.87 (0.58–1.31) 0.506
 None 33/115 28.5 1.22 (0.73–2.03) 0.451
Daily time spent studying
 More than 0.5 h 48/221 21.8 1.00    
 Less than 0.5 h 79/343 23.1 1.08 (0.72–1.62) 0.719
 None 27/72 37.4 2.15 (1.21–3.81) 0.009
Daily screen based activity
 None to 0.5 h 13/73 18.1 1.00
 0.5 h to 1 h 35/206 17.0 0.93 (0.46–1.87) 0.841
 1 h to 2 h 57/226 25.0 1.51 (0.77–2.95) 0.226
 More than 2 h 51/135 37.5 2.72 (1.36–5.43) 0.005
Daily time spent sleeping
 More than 10 h 26/163 15.9 1.00  
 8 to 10 h 89/326 27.4 2.00 1.23–3.25 0.005
 Up to 8 h 39/148 26.3 1.89 1.08–3.29 0.026

Data source: SAMSS March 2008–December 2009

Bold indicates statistically significant at P < 0.05

[TableWrap ID: Tab5] Table 5 

Multivariate odds ratios of socio-demographic and health factors independently associated with overweight and obese children as compared to normal weight children, aged 5 to 15

OR 95% CI P value
Daily time spent studying
 More than 0.5 h 1.00
 Less than 0.5 h 1.27 0.83–1.94 0.269
 None 2.68 1.46–4.94 0.001
Daily screen based activity
 None to 0.5 h 1.00
 0.5 h to 1 h 0.95 0.47–1.94 0.896
 1 h to 2 h 1.55 0.79–3.07 0.203
 More than 2 h 2.65 1.31–5.37 0.007
Daily time spent sleeping
 More than 10 h 1.00
 8 to 10 h 2.25 1.35–3.75 0.002
 Up to 8 h 2.00 1.10–3.62 0.022

Data source: SAMSS March 2008–December 2009

Bold indicates statistically significant at P < 0.05

Article Categories:
  • Article

Keywords: Keywords Children, Maternal employment, Obesity, Self-report, Sleep, Screen-based activity.

Previous Document:  Pacifier Use and Sids: Evidence for a Consistently Reduced Risk.
Next Document:  Risk and Protective Factors for Pregnancy Outcomes for Urban Aboriginal and Non-Aboriginal Mothers a...