Epidemiology and Population Health

Greener neighbourhoods, slimmer children? Evidence from 4423 participants aged 6 to 13 years in the Longitudinal Study of Australian children



There is a growing belief that green space (for example, parks) help prevent obesity. There is evidence of an inverse association between green space and childhood body mass index (BMI); however, the majority of these studies are cross-sectional. Longitudinal studies that track change in BMI across childhood in relation to levels of green space proximity would improve the quality of evidence available for decision making.


Objectively measured BMI was obtained every 2 years between 2006 and 2012 for 4423 participants initially aged 6–7 years in the Longitudinal Study of Australian Children (LSAC). The LSAC is a nationally representative study on a range of health and socio-demographic measures. Using Australian Bureau of Statistics mesh block data, which classify small scale land areas based on the main usage, each participant was assigned an objective measure of green space availability within their Statistical Area (level 2) of residence. Gender-stratified multilevel linear regression was used to estimate BMI growth curves across childhood in relation to green space availability. Family income, Australian Indigenous status, mothers’ education and language spoken were used to adjust for socio-economic confounding.


Age was found to be an effect modifier of associations between green space and BMI for boys (P=0.005) and girls (P=0.048). As children grew older, an inverse patterning of BMI by green space availability emerged. These findings held after adjustment for socio-economic circumstances for boys (P=0.009), though were less robust for girls after this adjustment (P=0.056).


A beneficial effect of green space on BMI emerges as children grow older. However, there was little additional benefit after a modest amount of green space was met. Further research is needed to understand whether the drivers of this effect are from age-specific mechanisms, or whether the benefit of living in a greener neighbourhood is accumulated through childhood.


It is often hypothesised that a child’s residential proximity to green space may influence their weight status.1, 2, 3, 4, 5 Although research into the influence of green space on adult health is increasing,6, 7, 8, 9 evidence for children remains less developed. Yet, these studies are essential for providing urban planners and policy makers with evidence-based recommendations on how much green space is desirable for creating healthy, liveable communities.

What little evidence has accumulated thus far has yielded mixed findings.10 Several studies have found a significant negative association between green space availability and childhood weight status.1, 2, 3, 4 For example, in a 2-year follow-up longitudinal study of children aged 3–16 years in the United States of America, Bell et al.1 noted an inverse relationship between a satellite-imagery-based green space measure and children’s 2-year BMI z-scores tended to be lower in areas with more green space (β=−0.07; 95% confidence interval=−0.11, −0.03). However, not all research has found statistically significant results.5 Reflecting upon theories in lifecourse epidemiology11 suggests that it may not be surprising, however, to find differences in results. Recent findings from a study of adults in the UK shows that the magnitude and direction of association between green space and mental health varies across the adult lifecourse and by gender.12 This could be attributable to gendered changes in lifestyles occurring with ageing that influence contact with nature (for example, participation in physical activity). Applying similar logic earlier in the lifecourse, it is already known that as children grow older, they will initiate, maintain or withdraw from pursuits that involve contact with nature while also developing different levels of independence and autonomy from parents/guardians.13 As such, an inverse association between child weight status and green space availability may not be consistent across childhood; rather, it could emerge through triggering of age-specific mechanisms (that is, the ‘critical or sensitive period’ model) and/or as a result of repeated exposure to the restorative qualities of green space (that is, the ‘accumulation’ model). These age-related contingencies may also be augmented by gender, with previous research suggesting that boys, though not girls, tend to be more physically active if they live in greener neighbourhoods.14

Accordingly, the aim of this study was to assess the degree of association between neighbourhood green space and trajectories in body mass index (BMI) across childhood. Further, we aimed to explore whether any such associations differed by gender.

Materials and methods


The Longitudinal Study of Australian Children (LSAC) collected data biennially from 2004 onwards. The full methodology of the LSAC is detailed elsewhere.15 Briefly, the LSAC is a large-scale project funded by the Australian Department of Families, Housing, Community Services, and Indigenous Affairs (FaHCSIA). A two-stage clustered design was used, with eligible children identified through Australia’s Medicare database. Medicare is Australia’s universal health-care scheme, tasked with providing and tracking health-care provision throughout Australia. The Medicare database was chosen as it covers all Australian permanent residents, and contains all of the information required to assess initial eligibility (that is, birthdate and postcode). The postcodes in which these children lived were then stratified by state and urban or rural status. A representative sample of postcodes was then randomly chosen with the children residing within those postcodes comprising the sample, such that 1-in-10 postcodes were sampled. A total of 9893 children were approached to participate by mail-out letter. Of those approached, 50.4% were successfully recruited, with 37.5% choosing to opt out and 15.2% unable to be contacted. Excluding those who were unable to be contacted, the overall response rate was 59.4%.15 Although only limited information is available on non-respondents, analysis by FaHCSIA suggests that mothers who spoke a language other than English and mothers with lower education levels were more likely to be non-respondents than educated, English-speaking mothers.16

LSAC data come primarily from the children’s parents, with additional data provided by other caregivers (for example, school teachers) and the children themselves, when they were of sufficient age. Data are collected primarily through biennial face-to-face interviews, with supplementary data coming from mail-out questionnaires and linked data, such as the Australian Census of Population and Housing17 or Medicare data. The total LSAC sample consists of 10 090 children, split between two age cohorts. The present study uses data from the older cohort (n=4983), who were 4–5 years old when the first data collection wave took place in 2004. Owing to adiposity rebound, where BMI growth curves rise and then fall between the ages of 8 months and 5.5 years,18, 19 we restricted our analysis to LSAC waves 2–5 (collected in 2006, 2008, 2010 and 2012) when the children were 6–13 years old (n=4423 at wave 2; 51% boys).

Body mass index (BMI)

At each face-to-face interview, a trained interviewer measured the child’s weight to the nearest 50 g using a set of glass bathroom scales (Salter Australia, Springvale, VIC, Australia; Code 79985), with the child wearing light clothing. The interviewer also measured the child’s height to the nearest 0.1 cm using a portable stadiometer (Invicta, Leicester, UK; Code IPO955).20 Two measurements of height were taken and the average used. Where the measures differed by more than 0.5 cm, a third measure was taken and the average of the two closest was used.20 Height and weight were used to construct an objective BMI measure for all participants at each survey wave.

Green space

A measure of neighbourhood green space was derived at the scale of the statistical area level 2 (SA2); the smallest area unit available in the LSAC.21 The SA2 was designed by the Australian Bureau of Statistics (ABS) to be representative of communities, with resident populations ranging between 3000 and 25 000 individuals.21 To derive the green space measure, land-use data were extracted from ABS mesh blocks circa 2006.22 Mesh blocks classify very small land parcels according to their main land use. For the purposes of this study, we isolated all mesh blocks that were classified as ‘parkland’ from other forms of land-use, including ‘farmland’ which would not typically be publically accessible. Parkland includes green land-uses of varied sizes, ranging from pocket parks to large sports fields. The percentage of green space land-use within each SA2 was calculated. Previous work applying a similar methodology has demonstrated association between this measure of green space and health outcomes among adults.6, 23, 24, 25, 26, 27 As the distribution of green space is highly skewed, we classified the measure into the following categories: 0–5%, 6–10%, 11–20%, 21–30%, 31–40% and >40%. The mesh block data were only available for 2006, and were therefore not able to vary over the study period.

Socio-economic status

As it is known that green space access varies by socio-economic status,28 and that socio-economic status is an important predictor of children’s BMI,20 it is important to properly control for any confounding effects of socio-economic status. This has been a limitation of previous research.1 Previous research conducted on the same sample of children20 has shown that various aspects of socio-economic status influence children’s BMI. Further evidence suggests that green space access varies by wealth.28 Therefore, socio-economic measures which are known to influence BMI were included to adjust for this potential source of confounding. The socio-economic measures chosen were (i) combined weekly income of caregivers (in thousands); (ii) whether the child was of Australian Aboriginal or Torres Strait Islander heritage (binary variable);29 (iii) whether the child spoke a language other than English at home; and (iv) the number of years of education the mother had received. Maternal education years were calculated according to the methodology of Blakemore, Gibbings and Strazdins,30 where the total number of years that the mother had received education are placed on a scale ranging from 0 (that is, never attended school) to 20 (completed postgraduate degree). Socio-economic data were collected at every wave, and entered into the models as repeated measures.

Statistical analysis

Descriptive statistics were used to characterise the sample. We then used multilevel linear regression to model the influence of neighbourhood green space on BMI. Each model consisted of the following hierarchical structure—the measure of weight status at each time point (level 1), nested within individuals (level 2), nested within Statistical Areas Level 2 s (level 3). On the basis of previous research into BMI trajectories,31 and the results of preliminary modelling, we chose to fit gender-stratified models. As multilevel models permit subjects having missing data at time points,32 all subjects sampled were included in the analyses.

To begin, a model consisting of the outcome measure (that is, child’s BMI), without explanatory variables, was fit. Age as linear and a quadratic term were then adjusted. The final step to build model 1 was to add the green space measure. In model 2, the contingency of any association between green space and BMI by age was examined by fitting a two-way interaction term. After adjusting for potential socio-economic confounding (model 3), the patterning of BMI by green space and age was visualised using multilevel model growth curves generated from these final models for further interrogation.12 Log-likelihood tests were used to determine whether explanatory variables significantly impacted on the models, with significance levels set at 5%. Statistical weighting is provided in the LSAC; however, we chose not to apply the weights in our analysis as the primary aim was to examine the association with green space, rather than estimate prevalence. All statistical analyses were conducted using STATA 12 (StataCorp, College Station, TX, USA).


Descriptive statistics of the sample are presented in Table 1. Owing to missing SA2 data, green space data were not available for 7 participants at wave 2 (0.2%), 139 participants at wave 3 (3.2%), 162 participants at wave 4 (3.9%) and 141 participants at wave 5 (3.6%). Similarly, BMI data were missing for 41 (0.9%) participants at wave 2, 42 (1.0%) at wave 3, 151 (3.6%) at wave 4 and 153 (3.9%) at wave 5. An unadjusted intraclass correlation coefficient of 0.04 was seen for the area level variance (that is, level 3) for boys, and 0.01 for girls, suggesting that only a small amount of that variation was explained by neighbourhoods. The intraclass correlation coefficient was much higher between individuals, with values of 0.54 for boys and 0.51 for girls.

Table 1 Sample characteristics

The outcomes of the multilevel linear regression analyses can be seen in Tables 2 and 3. The categorical green space variable was used to test the relationship between green space and BMI. In the first model, no relationship was detected between green space and BMI for girls’ BMI (P =0.371) or boys’ BMI (P=0.349).

Table 2 The influence of green space on boys’ BMI
Table 3 The influence of green space on girls’ BMI

To test whether the association between green space and BMI changed over time, an interaction effect was fit between green space and child age. For both boys (P=0.005) and girls (P=0.048), a statistically significant interaction was noted. Notably, the difference in BMI between green space categories widens as the children age, for both boys and girls. For example, boys living in areas with 6–10% green space increased BMI 0.08 per year slower than boys in 0–5% green space areas.

The final model included socio-economic controls which are known to be associated with child BMI in this sample. After controlling for socio-economic status, the overall interaction effect of green space by age on BMI remained statistically significant for boys (P=0.009), but failed to reach significance for girls (P =0.056). Some statistically significant differences were still noted for girls, with those in the 11–20% and 21–30% categories having differing trajectories to those in the 0–5% category, although the difference was marginal.

Multilevel growth curves generated from the final model (Figures 1 and 2) illustrate the widening BMI gap between high and low green space areas for boys and girls. For example, at age 12, boys living in areas with 6–10% green space had BMI scores an average of 0.5 lower than those in areas with 0–5% green space. Holding height at the sample average for 12-year-old boys (1.56 m), the difference in weight was 1.2 kgs, or 2.5% of average total body mass. A clear gender difference was noted in the growth curves, with the association between green space categories more distinct for boys than girls.

Figure 1

Multilevel growth curves for boys’ BMI over age, by green space category (fully adjusted).

Figure 2

Multilevel growth curves for girls’ BMI over age, by green space category (fully adjusted).


Whereas previous work on green space and child weight status has yielded equivocal results, we hypothesised that an inverse association between BMI and green space might emerge as a result of changes in lifestyle and autonomy which manifest across childhood. In line with our hypothesis, we found no general benefit of green space for child BMI per se; instead, the benefit of green space appeared to become more evident as children grew older. This was demonstrably the case for boys, but less so for girls after accounting for potential sources of socio-economic confounding. The key finding of this longitudinal study is, therefore, that associations between green space and child weight status are not consistent across childhood, but emerge as they grow older and are further modified by gender.

These results extend previous knowledge of the association between green space and child weight status based largely upon cross-sectional studies. One study found that children with a higher amount of neighbourhood green space had lower odds of being overweight or obese.4 Another study found that the odds of being overweight or obese was lower in neighbourhoods containing a higher number of recreational facilities (including green space).3 Meanwhile, a study of younger children (aged 4–8 years) reported a null association between green space and BMI.5 It is plausible that the pathways which trigger an inverse patterning of BMI by green space availability either occur at older ages (for example, the well-known decline in physical activity in adolescence33), or are accumulated across childhood and, therefore, may explain why an association was not observed in the latter study. Although there has been more recent work using longitudinal data,1 it was limited to examining change across two time points without explicit investigation of gender differences or age-related contingencies. Our study, therefore, not only takes the field of enquiry forwards in terms of the findings reported, but also the methodology used.

The gendered BMI trajectories by green space availability in this study also add to previous understandings. Previous work has suggested that contact with nature as a result of physical activity may be more important for boys than girls.14 Consequently, this may hold some explanation for the differences observed in our study. For girls, differences in BMI trajectories by green space were narrow and explained by controls for socio-economic status. For boys, in contrast, those living in greener neighbourhoods had lower BMIs compared with their counterparts in areas with less green space; but this was only evident at older ages. The overall effect of green space on boys’ BMIs was small, explaining approximately 9.3% of the area-level intraclass correlation coefficient. This suggests that investments in green space may have different influences for boys’ and girls’ BMIs, and it may be that different strategies for triggering regular use of local parks need to be used to ensure all children, regardless of gender, stand to benefit.

Our study also provides new insight on the policy-relevant question of ‘how much green space is needed for a healthier weight?’ For boys, only a small difference in green space (that is, from 0–5% to 6–10%) appeared to substantively improve their BMIs. Similar effect sizes observed at higher levels of green space availability indicates that there did not appear to be much additional benefit to boys’ BMI from further differences in the amount of green space. These observations are consistent with a possible threshold effect. It is important to note at this juncture, however, that the measure of green space used in this study focuses purely on the amount available and not on the type, function, composition or quality of the green space. Quality of green space may also be important as there is evidence to suggest that green space quality differs by socio-economic area,34 which may further confound findings. It is possible that additional benefits to child BMI in general may be accrued by accounting for these other dimensions; indeed, this may also be a factor in the low effect sizes observed for girls. Though clearly of interest, investigations of green space type, function, composition and quality in addition to quantity were beyond the remit of this study but do warrant future research.

Additionally, we found that the inverse patterning of BMI by green space emerged as children aged (particularly among boys). As we hypothesised, this increasing gap may be due to the triggering of age-specific pathways, such as participation in particular types or intensities of physical activity33 that may be related to increasing autonomy from parents,35 though not without gender differences.36 Such changes may occur at sensitive or critical periods, such as moving from primary to secondary school. It may also, however, reflect an accumulation of exposure to green space that is built up across childhood (that is, the accumulation hypothesis). It is known that there is less variation between individual children’s BMIs at younger ages,19 and it may be that a discernible benefit of green space for BMI only becomes evident through repeated exposure over time. Although this study was able to provide support for these hypotheses collectively, it was not methodologically possible to distinguish between the autonomy and the exposure hypotheses. This is clearly a gap in knowledge which requires further attention with mediation-type analyses if investments in green space are to be targeted effectively.

Among the strengths of the present study is the use of longitudinal data. This is among the first studies internationally to use longitudinal data to examine the influence of green space over early childhood leading into adolescence, and the first to use a sample approximately representative of Australian children. Further, the present study took advantage of objective measures of weight status, where previous research had used self-reported measures.4 Similarly, we used an objective measure of green space, rather than relying on perceived quantities of neighbourhood green space, which can result in spurious associations due to same source bias.3

The limitations of this study should also be noted. We have already reflected on the limitation of focussing on the amount, rather than the quality, of green space. Furthermore, owing to the availability of source data, the measure of green space was limited to one time point. Ideally, a measure of green space would be time-specific and includes data across the full range of dimensions that may be relevant for health.37 Additionally, the green space measure used covers only the area of residence for the subject, and is unable to take into account green spaces available outside their neighbourhood which children may use, such as parks they may enter on their way to school. However, gathering such data for the large number of areas included in this study spanning the entire country of Australia was not feasible. Further, the green space measure used covers only the area of residence for the subject, and is unable to take into account green space use in other areas—for example, travel by car to use a park or green space in the school area.

Participant non-response and dropout are well known limitations of longitudinal study designs. Although the LSAC sample was intended to be representative of the Australian population, differences were noted between the sample and Australian census data for parental language other than English spoken, parental education and whether the parents lived in a rented or owned home.16

This study has acknowledged that there remain many gaps in knowledge with respect to understanding the relationship between neighbourhood green space and child weight status. Future research is needed to examine the groups most influenced by proximity to green space, the other factors within neighbourhoods which moderate this association, and the mechanisms by which green space influences the health and body mass of both boys and girls. It is likely that any mechanisms explaining the influence of green space on child BMI would also present with gender differences. Research on adult populations suggests that physical activity is a likely mediator in the relationship between green space and health;38 however, this remains to be tested in a youth population. The information presented in this study, and that which may be gathered by the suggested future research, will be important in the design of healthy, family-centric neighbourhoods for increasingly urbanised populations.


The association between BMI and green space appears to be modified by age and gender. Older boys living in areas with little to no green space appear to have higher BMI trajectories than those living in areas with modest or high amounts of green space. However, there was little additional benefit beyond a modest quantity of green space. Similar associations were observed for girls, but these effects were largely attenuated after adjusting for socio-economic confounders. Future research is needed to consider the potential mediators that underpin these associations.


  1. 1

    Bell JF, Wilson JS, Liu GC . Neighborhood greenness and 2-year changes in body mass index of children and youth. Am J Prev Med 2008; 35: 547–553.

    Article  Google Scholar 

  2. 2

    Cohen DA, McKenzie TL, Sehgal A, Williamson S, Golinelli D, Lurie N . Contribution of public parks to physical activity. Am J Public Health 2007; 97: 509–514.

    Article  Google Scholar 

  3. 3

    Gordon-Larsen P, Nelson MC, Page P, Popkin BM . Inequality in the built environment underlies key health disparities in physical activity and obesity. Pediatrics 2006; 117: 417–424.

    Article  Google Scholar 

  4. 4

    Veugelers P, Sithole F, Zhang S, Muhajarine N . Neighborhood characteristics in relation to diet, physical activity and overweight of Canadian children. Int J Pediatr Obes 2008; 3: 152–159.

    Article  Google Scholar 

  5. 5

    Potestio ML, Patel AB, Powell CD, McNeil DA, Jacobson RD, McLaren L . Is there an association between spatial access to parks/green space and childhood overweight/obesity in Calgary, Canada? Int J Behav Nutr Phys Act 2009; 6: 77–86.

    Article  Google Scholar 

  6. 6

    Astell-Burt T, Feng X, Kolt GS . Greener neighborhoods, slimmer people? Evidence from 246 920 Australians. Int J Obes (Lond) 2014; 38: 156–159.

    CAS  Article  Google Scholar 

  7. 7

    Coombes E, Jones AP, Hillsdon M . The relationship of physical activity and overweight to objectively measured green space accessibility and use. Soc Sci Med 2010; 70: 816–822.

    Article  Google Scholar 

  8. 8

    Ellaway A, Macintyre S, Bonnefoy X . Graffiti, greenery, and obesity in adults: Secondary analysis of European cross sectional survey. BMJ 2005; 331: 611–612.

    Article  Google Scholar 

  9. 9

    Nielsen TS, Hansen KB . Do green areas affect health? Results from a Danish survey on the use of green areas and health indicators. Health Place 2007; 13: 839–850.

    Article  Google Scholar 

  10. 10

    Lachowycz K, Jones AP . Towards a better understanding of the relationship between greenspace and health: Development of a theoretical framework. Landscape and Urban Planning 2013; 118: 62–69.

    Article  Google Scholar 

  11. 11

    Kuh D, Ben-Shlomo Y, Lynch J, Hallqvist J, Power C . Life course epidemiology. J Epidemiol Community Health 2003; 57: 778–783.

    CAS  Article  Google Scholar 

  12. 12

    Astell-Burt T, Mitchell R, Hartig T . The association between green space and mental health varies across the lifecourse. A longitudinal study. J Epidemiol Community Health 2014; 68: 578–583.

    Article  Google Scholar 

  13. 13

    Carver A, Watson B, Shaw B, Hillman M . A comparison study of children's independent mobility in England and Australia. Children's Geographies 2013; 11: 461–475.

    Article  Google Scholar 

  14. 14

    Roemmich JN, Epstein LH, Raja S, Yin L . The neighborhood and home environments: disparate relationships with physical activity and sedentary behaviors in youth. Ann Behav Med 2007; 33: 29–38.

    Article  Google Scholar 

  15. 15

    Soloff C, Lawrence D, Johnstone R . LSAC technical paper number 1: Sample design, 2005, http://www.growingupinaustralia.gov.au/pubs/technical/tp1.pdf.

  16. 16

    Soloff C, Lawrence D, Misson S, Johnstone R . LSAC technical paper number 3: Wave 1 weighting and non-response, 2006, http://www.growingupinaustralia.gov.au/pubs/technical/tp3.pdf.

  17. 17

    Australian Bureau of Statistics Information Paper: Census of Population and Housing 2011. Australian Bureau of Statistics: Canberra.

  18. 18

    Cole TJ, Bellizzi MC, Flegal KM, Dietz WH . Establishing a standard definition for child overweight and obesity worldwide: International survey. BMJ 2000; 320: 1240–1243.

    CAS  Article  Google Scholar 

  19. 19

    Cole TJ, Freeman JV, Preece MA . Body mass index reference curves for the UK, 1990. Arch Dis Child 1995; 73: 25–29.

    CAS  Article  Google Scholar 

  20. 20

    Wake M, Hardy P, Canterford L, Sawyer MG, Carlin JB . Overweight, obesity and girth of Australian preschoolers: Prevalence and socio-economic correlates. Int J Obes (Lond) 2007; 31: 1044–1051.

    CAS  Article  Google Scholar 

  21. 21

    Australian Bureau of Statistics. Australian Statistical Geography Standard (ASGS) Volume 1 - Main Structure and Greater Capital City Statistical Areas. 2011, Australian Bureau of Statistics: Canberra.

  22. 22

    Australian Bureau of Statistics. Information Paper, Draft Mesh Blocks. 2005, Australian Bureau of Statistics: Canberra.

  23. 23

    Astell-Burt T, Feng X, Kolt GS . Mental health benefits of neighbourhood green space are stronger among physically active adults in middle-to-older age: Evidence from 260,061 Australians. Prev Med 2013; 57: 601–606.

    Article  Google Scholar 

  24. 24

    Astell-Burt T, Feng X, Kolt GS . Does access to neighbourhood green space promote a healthy duration of sleep? Novel findings from a cross-sectional study of 259 319 Australians. BMJ Open 2013; 3: e003094.

    Article  Google Scholar 

  25. 25

    Astell-Burt T, Feng X, Kolt GS . Neighbourhood green space and the odds of having skin cancer: multilevel evidence of survey data from 267 072 Australians. J Epidemiol Community Health 2013; 68: 370–374.

    Article  Google Scholar 

  26. 26

    Astell-Burt T, Feng X, Kolt GS . Is neighborhood green space associated with a lower risk of type 2 diabetes? Evidence from 267,072 Australians. Diabetes Care 2014; 37: 197–201.

    Article  Google Scholar 

  27. 27

    Astell-Burt T, Feng X, Kolt GS . Green space is associated with walking and moderate-to-vigorous physical activity (MVPA) in middle-to-older-aged adults: findings from 203 883 Australians in the 45 and Up Study. Br J Sports Med 2014; 48: 404–406.

    Article  Google Scholar 

  28. 28

    Astell-Burt T, Feng X, Mavoa S, Badland H, Giles-Corti B . Do low-income neighbourhoods have the least green space? A cross-sectional study of Australia's most populous cities. BMC Public Health 2014; 14: 292–302.

    Article  Google Scholar 

  29. 29

    Ou L, Chen J, Hillman K, Eastwood J . The comparison of health status and health services utilisation between Indigenous and non-Indigenous infants in Australia. Aust N Z J Public Health 2010; 34: 50–56.

    Article  Google Scholar 

  30. 30

    Blakemore T, Strazdins L, Gibbings J . Measuring family socioeconomic position. Aust Soc Policy J 2009; 8: 121–168.

    Google Scholar 

  31. 31

    Magee C, Caputi P, Iverson D . Identification of distinct body mass index trajectories in Australian children. Pediatric Obesity 2012; 8: 189–198.

    Article  Google Scholar 

  32. 32

    Kwok O-M, Underhill AT, Berry JW, Luo W, Elliott TR, Yoon M . Analyzing longitudinal data with multilevel models: An example with individuals living with lower extremity intra-articular fractures. Rehabil Psychol 2008; 53: 370–386.

    Article  Google Scholar 

  33. 33

    Alderman BL, Benham-Deal TB, Jenkins JM . Change in parental influence on children's physical activity over time. J Phys Act Health 2010; 7: 60–67.

    Article  Google Scholar 

  34. 34

    Crawford D, Timperio A, Giles-Corti B, Ball K, Hume C, Roberts R et al. Do features of public open spaces vary according to neighbourhood socio-economic status? Health Place 2008; 14: 889–893.

    Article  Google Scholar 

  35. 35

    Carver A, Timperio A, Crawford D . Playing it safe: The influence of neighbourhood safety on children's physical activity—A review. Health Place 2008; 14: 217–227.

    Article  Google Scholar 

  36. 36

    Timperio AF, Crawford DA, Telford A, Salmon J . Perceptions about the local neighborhood and walking and cycling among children. Prev Med 2004; 38: 39–47.

    Article  Google Scholar 

  37. 37

    Edwards N, Hooper P, Trapp GSA, Bull F, Boruff B, Giles-Corti B . Development of a Public Open Space Desktop Auditing Tool (POSDAT): A remote sensing approach. Appl Geogr 2013; 38: 22–30.

    Article  Google Scholar 

  38. 38

    Astell-Burt T, Feng X, Kolt GS . Green space is associated with walking and moderate-to-vigorous physical activity (MVPA) in middle-to-older-aged adults: Findings from 203 883 Australians in the 45 and Up Study. Br J Sports Med 2013; 48: 404–406.

    Article  Google Scholar 

Download references


This paper uses unit record data from Growing Up in Australia, the Longitudinal Study of Australian Children. The study is conducted in partnership between the Department of Social Services (DSS), the Australian Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS). The findings and views reported in this paper are those of the authors and should not be attributed to DSS, AIFS or the ABS. We also acknowledge the ABS for use of the 2006 mesh block data. TS is supported by an Australian Postgraduate Award. TAB is supported by a Fellowship with the National Heart Foundation of Australia.

Author information



Corresponding author

Correspondence to T Astell-Burt.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sanders, T., Feng, X., Fahey, P. et al. Greener neighbourhoods, slimmer children? Evidence from 4423 participants aged 6 to 13 years in the Longitudinal Study of Australian children. Int J Obes 39, 1224–1229 (2015). https://doi.org/10.1038/ijo.2015.69

Download citation

Further reading