Skip to content

Advertisement

You're viewing the new version of our site. Please leave us feedback.

Learn more

BMC Public Health

Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Assessing junk food consumption among Australian children: trends and associated characteristics from a cross-sectional study

  • S Boylan1Email author,
  • L. L. Hardy1,
  • B. A. Drayton2,
  • A. Grunseit1 and
  • S. Mihrshahi1
BMC Public HealthBMC series – open, inclusive and trusted201717:299

https://doi.org/10.1186/s12889-017-4207-x

Received: 1 October 2016

Accepted: 30 March 2017

Published: 5 April 2017

Abstract

Background

The ubiquitous supply of junk foods in our food environment has been partly blamed for the increased rates in overweight and obesity. However, consumption of these foods has generally been examined individually perhaps obscuring the true extent of their combined consumption and impact on health. An overall measure of children’s junk food consumption may prove useful in the development of child obesity prevention strategies. We describe the development of a children’s Junk Food Intake Measure (JFIM) to summarise temporal change in junk food consumption and examine the association between the JFIM and health-related behaviours.

Methods

Cross-sectional population surveillance survey of Australian children age 5–16 years collected in 2010 and 2015. Data were collected by questionnaire with parent’s proxy reporting for children in years K, 2 and 4 and children in years 6, 8 and 10 by self-report. Information on diet, screen-time and physical activity was collected using validated questionnaires. The JFIM comprised consumption of fried potato products, potato crisps/salty snacks, sweet and savoury biscuits/cakes/doughnuts, confectionary and, ice cream/ice blocks.

Results

A total of 7565 (missing = 493, 6.1%) and 6944 (missing n = 611, 8.1%) children had complete data on consumption of junk foods, in 2010 and 2015, respectively. The 2015 survey data showed that among students from high socio-economic status neighbourhoods, there were fewer high junk food consumers than low junk food consumers. Children from Middle Eastern cultural backgrounds had higher junk food consumption. High junk food consumers were more likely to consume take-away ≥3/week, eat dinner in front of the television, receive sweet rewards, be allowed to consume snacks anytime, have soft drinks available at home and a TV in their bedroom. There was a lower proportion of high junk food consumers in 2015 compared to 2010.

Conclusion

This is the first study to provide and examine a summary measure of overall junk food consumption among Australian children. The results indicate that junk food consumption among Australian children is lower in 2015, compared with 2010. Still, the public health workforce must continue their efforts as levels of junk food consumption remain of concern among Australian children.

Keywords

Junk foodEnergy-dense nutrient-poorChildrenAdolescents

Background

In 2011, it was estimated that over one quarter (25.3%) of Australian children aged 5–17 years were overweight or obese, with 17.7% being overweight and 7.6% obese [1]. While the aetiology of these conditions may be multi-factorial, exceeding dietary energy requirements is seen as a key contributor to increasing body weight [2]. The current food environment facilitates unhealthy weight gain in children through the widespread availability, affordability and accessibility of junk foods [3]. Junk foods generally contribute few micronutrients to the diet, contain substantial amounts of fat and/or sugar and are high in energy [4]. Examples of junk foods include the majority of foods sold at fast food outlets, snack foods such as sweet and savoury biscuits and confectionery [5].

Much of the research to date examining junk food consumption has investigated junk food consumption behaviours singularly. Reporting on individual, rather than the overall or combined frequency of junk foods, may hide the true extent of junk food consumption among children and adolescents as these foods are typically eaten in combination, for example potato chips and soft drink [6]. Information on the overall rating of children’s diets may prove useful in the development of strategies aimed to reduce consumption and prevent childhood obesity.

Since the mid-1990s, attempts to assess diet quality of populations have focused on healthy eating indices or scores which provide an overall rating on a numeric scale of an individual’s dietary intake in reference to nutrient and or dietary recommendations [7]. These indices are not without their limitations, which include underutilisation among children and adolescent populations, derivation from resource intensive dietary methodology [8, 9] and little application to unhealthy eating patterns [7].

We addressed this research gap by developing a Junk Food Intake Measure (JFIM) for children and adolescents to examine the cumulative intake of junk food [10]. The aims of this study were to a) describe the development of a children’s JFIM; b) summarise junk food consumption (c) examine the association between the JFIM and health related behaviours and d) examine change in the JFIM between 2010 and 2015 among children age 5–16 years.

Methods

Data collection

This is a secondary data analysis of the 2010 [11] and 2015 (in press) New South Wales (NSW) Schools Physical Activity and Nutrition Survey (SPANS). SPANS is a representative cross-sectional surveillance survey of weight and weight related behaviours of children age 5–16 years living in NSW, Australia’s most populous state (2015 pop 7.6 mil) and is conducted approximately every 5 years. The surveys are school-based and use comparable sampling frames that are based on a two-stage probability sample (school and student). The probability of school selection was proportional to size of the school enrolment. Schools were sampled from each education sector (Government, Independent, Catholic) proportional to enrolment in that sector, and then all students from one to two randomly selected classes in each target grade were invited to participate. Detailed descriptions of the survey methodology are published elsewhere [12]. Briefly, the study protocols are comparable for each survey year and data are collected by trained field teams during February – April. Ethics approvals were granted by the University of Sydney Human Research Ethics Committee, the NSW Department of Education and Training (DET) and the NSW Catholic Education Commission.

Measures

Parents of children in kindergarten, years 2 and 4 (i.e. younger children; ages 5.4–9.3 years) completed the questionnaire for their child. Children in years 6, 8 and 10 (i.e. older children; ages 11.3–15.4 years) self-completed the same questionnaire. Socio-demographic information included the child’s sex, language spoken most often at home, and postcode of residence. Postcode of residence was a proxy measure of socioeconomic status (SES) using the Australian Bureau of Statistics’ Socioeconomic Index for Areas (SEIFA) Index of Relative Socioeconomic Disadvantage [13]. SEIFA summarizes census-obtained socioeconomic indicators for geographic areas including income, educational attainment, unemployment and proportion of people in unskilled occupations. SEIFA scores from the national census most proximal to the survey year were used to rank students into low, middle, and high tertiles of SES background. Language spoken most often at home was used to categorise children into four main cultural groups according to the Australian Standard Classification of Languages [14]; English speaking, Asian, European and Middle Eastern.

Physical activity was measured by a validated one item question which asked, over the past 7-days how many days they/their child engage in moderate-to-vigorous physical activity for at least 60 min each day [15]. The response categories were 0–7 days, and dichotomised according to physical activity recommendations: 7 days (met physical activity recommendation) or <7 days (did not meet physical activity recommendation) [16, 17].

Information on screen time (television (TV), videos/DVDs, computer smart phone, tablets, e-games) was collected using a questionnaire from the Adolescent Sedentary Activity Questionnaire [18] and dichotomised for the analysis according to screen time recommendations; <2 h/day (meets recommendation) or ≥2 h/day (does not meet recommendation).

Diet indicators

Indicators of diet were collected using a validated short food frequency questionnaire developed for population-based monitoring surveys [19]. Respondents reported consumption of fruit, vegetables, fatty meat products, red meat, fried potato products, salty snack foods, snack foods, confectionary, ice cream and beverages including sugar sweetened drinks (i.e., soft drink, diet soft drink, fruit juice), water and milk. Frequency response categories for food items were: Never or rarely, 1–2 times per week, 3–4 times per week, 5–6 times per week, 1 time per day, 2 times per day. Drinks response categories were: 1 cup or less per week, 2–4 cups per week, 5–6 cups per week, 1 cup per day, 2 cups per day (one cup defined as 250 ml).

Junk food intake measure (JFIM)

The JFIM was based on the consumption of fried potato products (e.g. hot chips), potato crisps/salty snacks, sweet and savoury biscuits/cakes/doughnuts, confectionary and, ice cream/ice blocks. These foods are commonly consumed among Australian children [4, 20]. Each food item was assigned a score of 0–5 depending on frequency of food intake (0 being never/rarely and 5 being 2 or more times per day) so that the JFIM ranged from 0 to 25 (0 being no junk food consumed). To assess the general structure of the variables and the measure, we used principal component analysis as the data reduction technique as all the measures were discrete [21]. Principal component factor method of extraction with varimax rotation was used for all respondents with complete data. Items with loadings greater than 0.3 were used to interpret the factors. Spearman correlations were calculated between the measure and 1) anthropometric measures (waist circumference and body mass index) 2) other unhealthy food consumption (i.e., soft drink) 3) other healthy food consumption (i.e., fruit and vegetables) and 4) family food practices (soft drink availability in the home, frequency of fast food for family meals, rewarding good behaviour with sweet treats, offering water to drink with meals 5) other obesogenic behaviours (recreational screen time).

Principal component analysis generated a single factor scale with a similar structure for younger and older children with high internal consistency (Cronbach’s alpha = .744 and .749 respectively). The JFIM showed correlations in the expected direction with most variables for younger and older children. The measure was negatively and significantly correlated with fruit intake, vegetable intake and increasing frequency of the child being offered water with meals (younger children only). Similarly, positive correlations were found between the measure and soft drink and fast food consumption, increasing frequency of family meals at fast food restaurants, having sweets as a reward for good behaviour, higher soft drink availability in the home, and increasing time spent in small screen recreation.

In the current study, the JFIM was categorised into tertiles so that low, middle and high tertiles denoted scores in the ranges [0–5], [68], and [925], respectively. The JFIM was also treated as a continuous variable when examining the mean difference in the JFIM between 2010 and 2015.

Statistical analyses

Data were analysed between January and March 2016 using SPSS Complex Sample Analysis (version 22 for Windows; IBM, Chicago, IL, USA) to account for the complex sampling design [22]. Analyses were replicated using SAS version 9.4. Post stratification weights were calculated to account for variations in response rates, along with cluster and stratification variables to account for the complex sampling design. Analyses were stratified by method of completion i.e. proxy report by parents for younger children (Years K, 2 and 4) and self-report for older children (Years 6, 8 and 10). Descriptive summaries of child characteristics by age group (i.e., younger and older) were produced for the 2010 and 2015 data and across the JFIM tertiles for the 2015 data. The mean change in the JFIM between 2010 and 2015 were also examined as well as the proportions of younger and older children in each of the JFIM tertiles in both survey years.

Results

Subject characteristics

In total, 8058 and 7555 children age 5–16 years participated in SPANS 2010 and 2015, respectively (response rate = 61.8% and 71%, respectively). A total of 7565 (missing = 493, 6.1%) and 6944 (missing n = 611, 8.1%) children had complete data on consumption of junk foods, in 2010 and 2015, respectively. The characteristics of the children are shown in Table 1. Younger children comprised of 45.4% and 50.2% of the 2010 and 2015 sample, respectively. The proportions of boys and girls for each survey year were similar, with each sex contributing to approximately half of the total sample. The majority of children were from English-speaking backgrounds in both survey years (younger: 2010 (86%), 2015 (88.4%); older: 2010 (87.4%), 2015 (87.6%). Compared with the 2010 sample, the 2015 sample had significantly more favourable behaviours with more children meeting recommended serves for fruit and for vegetables (older children only) and fewer children eating fast food regularly (older children only), eating dinner in front of the TV (younger children only), having soft drink available at home (older children only) and having a TV in their bedroom. Compared with the 2010 sample, a higher proportion of younger children consuming fast food regularly and a lower proportion of older children met the physical activity recommendation in the 2015 sample.
Table 1

Characteristics of the 2010 and 2015 samples (%)1

 

Younger children

(years K/2/4)

Older children

(years 6/8/10)

 

2010

n = 3437

2015

n = 3489

2010

n = 4128

2015

n = 3455

Sex

 Boys

51.6

48.9

52.0

50.9

 Girls

48.4

51.1

48.0

49.1

Socioeconomic tertile

 Low

29.9

20.3

28.3

31.4

 Middle

43.4

33.7

38.7

33.4

 High

26.7

46.0

33.0

35.2

Cultural background

 English-speaking

86.0

88.4

87.4

87.6

 European

0.9

1.5

1.8

1.3

 Middle Eastern

4.7

3.6

3.2

3.2

 Asian

8.4

6.5

7.6

7.8

Locality

 Urban

88.3

78.4

80.1

73.7

 Rural

11.7

21.6

19.9

26.3

Meets fruit serve recommendation

 No

29.9

22.8 4

25.0

19.2 2

 Yes

70.1

77.2

75.0

80.8

Meets vegetable serve recommendation

 No

97.3

97.4

93.4

88.9 4

 Yes

2.7

2.6

6.6

11.1

Takeaways ≥3/week

 No

99.2

98.5 3

96.8

97.6 2

 Yes

0.8

1.5

3.2

2.4

Eat breakfast daily

 No

12.6

13.0

30.5

35.7

 Yes

87.4

87.0

69.5

64.3

Eats dinner in front of the TV

 No

81.1

84.7 2

77.4

80.4

 Yes

18.9

15.3

22.6

19.6

Sweets as a reward for good behaviour

 Rarely/never

40.1

40.8

53.9

52.1

 Sometimes

49.8

52.7

39.3

42.0

 Usually

10.1

6.6

6.8

5.9

Unrestricted snacking at home

 Yes

-

89.9

-

50.2

 No

-

10.1

-

49.8

Soft drinks available in the home

 Rarely/never

-

66.5

31.9

42.8 4

 Sometimes

-

27.1

40.4

41.3

 Usually

-

6.5

27.7

15.9

Has TV in the bedroom

 No

76.0

85.4 2

60.1

70.1 4

 Yes

24.0

14.6

39.9

29.9

Meets physical activity recommendation

 No

-

74.6

-

87.1 4

 Yes

-

25.4

-

12.9

Meet screen time recommendation (weekdays)

 No

41.7

38.0

54.7

55.7

 Yes

58.3

62.0

45.3

44.3

Meet screen time recommendation (weekend days)

 No

85.2

83.5

79.7

76.6

 Yes

14.8

16.5

20.3

23.4

1Population-weighted proportions; Differences between 2010 and 2015 prevalences 2 p < 0.05; 3 p < 0.01; 4 p < 0.001

Junk food intake measure

Distribution of the JFIM

Table 2 shows the mean frequency of food intake across the JFIM tertiles. The mean frequency for each food increases relative to each tertile and shows that a higher mean frequency of intakes in the high tertiles compared with the lower and middle tertiles.
Table 2

Mean (SD) food servings per week by JFIM tertile and age groups in 2015

 

2015 JFIM tertile

 

Low

Middle

High

Frequency of intake

Mean

SD

Mean

SD

Mean

SD

Younger children

    

 Fried potato products

0.30

0.02

0.72

0.02

1.12

0.04

 Potato crisps/salty snacks

0.34

0.03

1.03

0.04

2.22

0.08

 Sweet/savoury biscuits

1.00

0.03

1.73

0.04

2.73

0.06

 Lollies/chocolate

0.48

0.02

1.12

0.02

1.95

0.05

 Ice-cream/ice-blocks

0.72

0.03

1.32

0.02

2.22

0.04

Older children

    

 Fried potato products

0.33

0.02

0.84

0.02

1.41

0.04

 Potato crisps/salty snacks

0.54

0.03

1.29

0.03

2.37

0.05

 Sweet/savoury biscuits

0.80

0.03

1.59

0.03

2.75

0.05

 Lollies/chocolate

0.42

0.02

1.13

0.02

2.30

0.05

 Ice-cream/ice-blocks

0.46

0.02

1.08

0.03

1.96

0.06

Relationship between the JFIM and subject characteristics and behaviours

The association between children’s demographic characteristics, health behaviours and JFIM tertiles are summarised in Table 3. There were significant differences in the JFIM across SES tertiles among younger children (p = 0.02). Younger children from high SES neighbourhoods had a lower JFIM, compared with those from low SES neighbourhoods. Approximately half of children from Middle Eastern cultural backgrounds were high junk food consumers according to the JFIM (52.1% and 44% among younger and older students, respectively), compared with one quarter of children from European cultural backgrounds (25.9% and 23% among younger and older students, respectively; significant among younger children only p < 0.001).
Table 3

2015 JFIM tertile prevalence by demographics and health related behaviours

 

Younger Children

Older children

  

JFIM tertile

 

JFIM tertile

  

Low

Middle

High

  

Low

Middle

High

 

Characteristic

n

%

%

%

P-value

n

%

%

%

P-value

Boys

1638

33.9

33.2

32.9

0.14

1737

39.4

31.8

28.8

0.72

Girls

1851

32.9

36.4

30.7

 

1718

40.8

30.4

28.9

 

Socioeconomic tertile

 Low

679

25.4

35.8

38.8

0.02

979

37.8

29.3

32.9

0.07

 Middle

1174

33.6

34.1

32.3

 

1232

41.3

30.6

28.1

 

 High

1636

36.7

34.9

28.3

 

1244

40.9

33.3

25.9

 

Cultural background

 English-speaking

3054

34.4

34.8

30.8

<0.001

2996

40.3

31.7

28.0

0.07

 European

53

48.8

25.3

25.9

 

47

47.4

29.7

23.0

 

 Middle Eastern

137

10.2

37.7

52.1

 

113

34.6

21.4

44.0

 

 Asian

216

29.0

37.4

33.6

 

265

38.4

29.5

32.1

 

Locality

 Urban

2677

32.7

34.6

32.6

0.47

2613

39.6

30.9

29.5

0.57

 Rural

812

35.7

35.6

28.6

 

842

41.3

31.8

27.0

 

Meets fruit recommendations

 No

808

24.3

35.4

40.3

<0.001

634

31.6

32.8

35.6

<0.001

 Yes

2659

36.1

34.7

29.2

 

2706

41.8

30.6

27.6

 

Meets vegetable serve recommendation

 No

3362

33.4

34.4

32.3

0.01

2975

38.6

31.6

29.8

0.003

 Yes

84

37.7

46.0

16.3

 

368

48.1

28.1

23.9

 

Takeaways ≥3/week

 No

3431

33.8

34.9

31.3

<0.001

3345

40.8

31.3

27.9

<0.001

 Yes

44

1.4

25.4

73.2

 

81

8.8

20.9

70.3

 

Eat breakfast daily

 No

435

22.1

35.8

42.1

<0.001

1144

36.0

33.0

30.9

0.01

 Yes

3031

34.9

34.6

30.4

 

2266

42.1

30.2

27.7

 

Eats dinner in front of the TV

 No

2980

35.9

35.3

28.8

<0.001

2767

42.8

32.2

25.0

<0.001

 Yes

490

19.1

32.5

48.4

 

655

28.4

27.1

44.5

 

Sweets as a reward for good behaviour

 Rarely/never

1440

44.9

31.2

23.9

<0.001

1750

51.4

27.8

20.7

<0.001

 Sometimes

1812

27.3

38.5

34.2

 

1433

30.2

35.5

34.3

 

 Usually

216

10.1

30.1

59.8

 

193

10.6

26.0

63.4

 

Unrestricted snacking at home

 Yes

3097

14.6

30.6

54.8

<0.001

1759

28.8

33.0

38.2

<0.001

 No

371

35.5

35.4

29.1

 

1616

51.3

29.0

19.7

 

Soft drinks available in the home

 Rarely/never

2290

42.0

35.2

22.8

<0.001

1504

53.5

27.0

19.6

<0.001

 Sometimes

937

17.0

36.4

46.6

 

1355

33.1

36.1

30.8

 

 Usually

235

14.5

24.9

60.6

 

527

22.3

28.7

49.0

 

Has TV in the bedroom

 No

2985

35.4

35.1

29.5

<0.001

2401

42.0

31.6

26.4

<0.001

 Yes

480

21.4

33.8

44.8

 

976

35.4

29.8

34.9

 

Meets physical activity recommendation

 No

2588

32.5

34.9

32.5

0.04

2793

39.7

32.0

28.2

0.02

 Yes

845

36.9

33.8

29.2

 

422

43.9

24.7

31.3

 

Meets screen time recommendation (weekdays)

 No

1317

21.3

32.7

45.9

<0.001

1838

32.9

31.9

35.2

<0.001

 Yes

2148

40.8

36.1

23.0

 

1591

48.9

30.3

20.8

 

Meets screen time recommendation (weekend days)

 No

2896

29.1

36.4

34.5

<0.001

2548

35.8

32.9

31.3

<0.001

 Yes

550

54.4

27.3

18.3

 

836

54.2

25.9

19.9

 

Generally, meeting fruit and vegetable recommendations (compared with not meeting them) seemed to be significantly associated with a lower proportion of younger and older children reporting high junk food consumption (fruit: p < 0.001 for both younger and older children; vegetables p = 0.01 and p = 0.003 for younger and older children, respectively). Over 70% of both younger and older children who consumed takeaway three or more times per week had a high JFIM compared with approximately one-third of younger and older children who reported eating takeaway foods less frequently (p < 0.001). Similarly, eating dinner in front of the TV (compared with not) (p < 0.001), receiving sweets as a reward for good behaviour (usually compared with less frequently) (p < 0.001), being allowed to consume snacks at any time (p < 0.001), soft drinks being usually available in the home (p < 0.001), and having a TV in the child’s bedroom (p < 0.001) were all significantly associated with a higher JFIM among younger and older children. Not eating breakfast daily appeared to be associated with a higher JFIM for younger students only (p < 0.001). There were significant differences in the JFIM between those who did and did not meet screen time recommendations, with over half of the younger and older children who met screen time recommendations at weekends being in the low JFIM tertile (p < 0.001).

Compared with 2010, the proportion of low junk food consumers increased, and the proportion reporting high junk food consumers was lower in 2015 for both age groups (Table 4 ). The proportions within the middle tertiles were similar between the two survey years for the two age groups. The mean JFIM were significantly lower in 2015 compared to 2010 for both younger (p < 0.001) and older (p < 0.001) children.
Table 4

JFIM tertile prevalence by age group in 2010 and 2015

 

Younger children

Older children

JFIM tertile

2010

2015

2010

2015

Low (%)

JFIM Score 0–5

37.6

45.9

41.5

51.4

Middle (%)

JFIM Score 6–8

34.1

32.1

32.3

26.4

High (%)

JFIM Score 9–25

28.3

22.0

26.2

22.2

JFIM Score (mean 95% CI)

7.17

(7.05–7.30)

6.26

(5.98–6.55)

6.73

(6.40–7.06)

5.98

(5.76–6.19)

p-value (between survey years)

<0.001

<0.001

Discussion

Examining the intake of individual junk foods may hide the true extent of their consumption. Therefore, we developed the Junk Food Intake Measure (JFIM) for children and adolescents to measure the cumulative intake of junk food. The results of this study indicate that junk food consumption among school age children, measured in aggregate by the JFIM, is lower in 2015 compared with 2010. This promising downward trend may be partly due to state wide health promotion efforts [23] and the growing negative media coverage which sugar consumption, in particular, has been receiving in recent years [24, 25]. However, a recent national survey showed that on average, just over one-third (35%) of total daily energy was from ‘discretionary foods’ (junk foods) and the proportion of energy from discretionary foods was highest among 14–18 year olds (41%) [20]. In addition, this current study indicates that intakes of junk foods are still of concern among specific sub-populations such as children from Middle Eastern and low SES backgrounds.

Our analysis shows that junk food consumption is heterogeneous across a range of sociodemographic subgroups. Specifically, children from high SES neighbourhoods were more likely to have a low JFIM compared with those from low SES neighbourhoods. This is consistent with other research that shows children experiencing socioeconomic disadvantage have lower quality diets and higher intakes of junk foods and beverages [26, 27]. A recent study from NSW showed that, among low SES children, there were also clear differences in weight and weight-related behaviours according to cultural background [28]. While it has been shown that parents of children from Middle Eastern cultural backgrounds generally encouraged healthy behaviours, they also reported making regular exemptions [29]. The analysis presented here is consistent with this research, showing that approximately half of children of Middle Eastern cultural backgrounds were in the high JFIM tertile.

Watching TV while eating dinner has been associated with a lower quality diet and a higher body mass index [30, 31]. In the currently study, there were more children who eat dinner in front of the TV in the high JFIM tertile (approximately half of younger and older children), compared with the low JFIM tertile (19.1% and 28.4% younger and older children, respectively). Further, over half of younger and older children who met the screen time recommendations at weekends, were low junk food consumers. Screen time is the primary contributor to the total time spent in sedentary behaviours among young people (Biddle et al. 2014). It has been suggested that screen time, particularly TV, has an important role in the aetiology of obesity due to its relationship with other unhealthy behaviours such as snacking on junk foods, displacing physical activity and inadequate sleep [32]. In addition, advertising of junk food on TV has the potential to promote unhealthy dietary practices among children [33, 34]. The results from this current study also found that approximately 45% and 35% of younger and older children who have a TV in their bedroom, respectively, were high junk food consumers. This relationship may partly explain why children with a TV in their bedroom are also at greater risk of developing overweight and obesity [35].

We also found that a number of unhealthy parenting and home environment measures were associated with high scores on the JFIM. Despite evidence pointing towards negative long term health outcomes related to overeating and increased intake of unhealthy foods, parents commonly reward children’s behaviour with sweet foods [3638]. Furthermore, if sweets are given as a reward food to children for eating their fruit or vegetables, children may learn to place less value on fruit and vegetables [39]. Our analysis found that approximately 60% of those who received sweets as a reward for their behaviour were high junk food consumers. In addition, 60% and 49% of younger and older children, respectively, who have soft drinks available at home, were also high junk food consumers. Such findings are of concern as frequent soft drink consumption replaces healthier beverages in the diet (such as water and milk) and may increase the risk of obesity, type 2 diabetes, dental caries, and bone fractures [40, 41]. Taken together, these associations may reflect the contribution of obesogenic household culture to unhealthy food consumption of children within these households [42]. Frequent consumption of fast food is also of concern as these foods are typically high in kilojoules, fat, saturated fat, sugar, and salt and regular fast food consumption is associated with higher caloric intake, and poorer diet quality, characterised by a diet higher in fat, carbohydrate, and sugar [43]. Over 70% of the children in the current study who reported consuming takeaways three or more times per week were high junk food consumers, again indicating that junk foods are typically eaten in combination.

Strength and limitations

The strengths of our study include a random cluster sample, representative of NSW school age children, a relatively high response rate and the use of a JFIM. The JFIM is consistent with other health behaviours in this current analysis, confirming the tendency of these behaviours to cluster and mutually influence [44]. In addition, food frequency and descriptive terms currently used do not provide meaningful or consistent nutritional guidance [45]. Messages which more accurately reflect consumption patterns may be more effective in health promotion efforts.

Limitations to consider in the interpretation of the findings, include parent’s proxy reporting for children in Years K 2 and 4. Although parents of these children are the main providers of their child’s food, they may not necessarily be aware of foods consumed during school hours. In addition, older children, who self-reported intake in this current study, are particularly likely to misreport food intake [46]. The cross-sectional design prevents comment on causal direction. This information however is useful to determine further investigation of the associations through other research designs with temporal measures (e.g., longitudinal or pre-post intervention studies). Short questions do not accurately quantify amounts of foods consumed therefore estimates of the percentage of students meeting dietary recommendations must be interpreted with caution. However the questions used here rank individuals according to their intakes, and indicate differences in diet quality between response categories. They can also give an indication of changes in food consumption by examining the distribution of responses over time and to establish trends, provided the same survey questions are used [19].

Conclusion

We describe a novel measure for examining overall junk food consumption. This measure is associated with a range of other health related behaviours and indicates both convergent and discriminant validity. This highlights the importance of examining clustering of food intake as assessing consumption of individual foods may not provide a complete picture of dietary health. The results of this study indicate that junk food consumption among NSW school children is lower than reported in 2010. The public health workforce must continue to roll out successful programs to address unhealthy diet behaviour and risk of overweight and obesity as levels of junk food consumption remains of concern among children.

Abbreviations

BMI: 

Body mass index

FFQ: 

Food frequency questionnaire

JFIM: 

Junk food intake measure

NSW: 

New South Wales

SEIFA: 

Socioeconomic index for areas index of relative socioeconomic disadvantage

SES: 

Socioeconomic status

SPANS: 

School physical activity and nutrition survey

TV: 

Television

Declarations

Acknowledgements

The authors wish to thank the schools and students for their participation. This work was completed while BAD was employed as a trainee on the NSW Biostatistics Training Program funded by the NSW Ministry of Health. He undertook this work whilst based at the Prevention Research Collaboration, Sydney School of Public Health.

Funding

Funding for SPANS 2015 came from the NSW Ministry of Health. The NSW Ministry of Health had no role in data collection, analysis, and interpretation of data or in writing of the manuscript.

Availability of data and supporting materials

The data is not available. The data is owned by the NSW Ministry of Health.

Authors’ contributions

SB conceptualised the manuscript, conducted the analysis, and prepared the first draft of the manuscript. AG participated in conceptualising the statistical analysis. BAD, AG and LLH offered guidance on statistical analysis and in drafting of the paper. LLH, SM, BAD and AG provided critical review of multiple drafts. LLH was instrumental in developing the instruments and overseeing management of the survey. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Ethics approvals were granted by the University of Sydney Human Research Ethics Committee, the NSW Department of Education and Training (DET) and the NSW Catholic Education Commission.Written informed consent from each child’s parent/carer was a requirement for participation.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Prevention Research Collaboration, School of Public Health, The Hub, The University of Sydney
(2)
NSW Biostatistics Training Program, NSW Ministry of Health

References

  1. Australian Health Survey. Updated Results, 2011–2012. Overweight and obesity [http://www.abs.gov.au/ausstats/abs@.nsf/Lookup/33C64022ABB5ECD5CA257B8200179437?opendocument] Accessed 6 Apr 2016
  2. Australian Institute of Health and Welfare. Cardiovascular disease, diabetes and chronic kidney disease—Australian facts: risk factors. Canberra: Australian Institute of Health and Welfare; 2015.Google Scholar
  3. Swinburn BA, Sacks G, Hall KD, McPherson K, Finegood DT, Moodie ML, Gortmaker SL. The global obesity pandemic: shaped by global drivers and local environments. Lancet. 2011;378(9793):804–14.View ArticlePubMedGoogle Scholar
  4. Rangan AM, Randall D, Hector DJ, Gill TP, Webb KL. Consumption of 'extra' foods by Australian children: types, quantities and contribution to energy and nutrient intakes. Eur J Clin Nutr. 2007;62(3):356–64.View ArticlePubMedGoogle Scholar
  5. Rangan AM, Kwan J, Flood VM, Louie JC, Gill TP. Changes in 'extra' food intake among Australian children between 1995 and 2007. Obesity Res Clin Pract. 2011;5(1):e1–e78.View ArticleGoogle Scholar
  6. Innes-Hughes C, Hardy LL, Venugopal K, King LA, Wolfenden L, Rangan A. Children’s consumption of energy-dense nutrient-poor foods, fruit and vegetables: are they related? An analysis of data from a cross sectional survey. Health Promot J Austr. 2011;22(3):210–6.PubMedGoogle Scholar
  7. Marshall S, Burrows T, Collins CE. Systematic review of diet quality indices and their associations with health-related outcomes in children and adolescents. J Hum Nutr Diet. 2014;27(6):577–98.View ArticlePubMedGoogle Scholar
  8. Hendrie GA, Viner Smith E, Golley RK. The reliability and relative validity of a diet index score for 4-11 year-old children derived from a parent-reported short food survey. Public Health Nutr. 2014;17(7):1486–97.View ArticlePubMedGoogle Scholar
  9. Robinson LN, Rollo ME, Watson J, Burrows TL, Collins CE. Relationships between dietary intakes of children and their parents: a cross-sectional, secondary analysis of families participating in the family diet quality study. J Hum Nutr Diet. 2015;28(5):443–51.View ArticlePubMedGoogle Scholar
  10. Grunseit AC, Hardy LL, King L, Rangan A. A junk food index for children and adolescents. Sydney: Physical Activity Nutrition Obesity Research Group. NSW Ministry of Health; 2012.Google Scholar
  11. Hardy LL, King L, Espinel P, Cosgrove C, Bauman A. NSW schools physical activity and nutrition survey (SPANS) 2010: full report. Sydney: NSW Ministry of Health; 2010.Google Scholar
  12. Hardy L, King L, Espinel P, Okely AD, Bauman A. Methods of the NSW schools physical activity and nutrition survey 2010 (SPANS 2010). J Sci Med Sport. 2011;14(5):390–6.View ArticlePubMedGoogle Scholar
  13. Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia - Data only, 2011 [http://www.abs.gov.au/ausstats/abs@.nsf/mf/2033.0.55.001] Accessed 7 Apr 2016
  14. Australian Standard Classification of Languages (ASCL) 2nd edition, 2011 [http://www.abs.gov.au/ausstats/abs@.nsf/mf/1267.0] Accessed 7 Apr 2016
  15. Prochaska JJ, Sallis JF, Long B. A physical activity screening measure for use with adolescents in primary care. Arch Pediatr Adolesc Med. 2001;155(5):554–9.View ArticlePubMedGoogle Scholar
  16. Department of Health. Australia's physical activity and sedentary behaviour guidelines for children (5–12 years). Canberra: Commonwealth of Australia; 2014.Google Scholar
  17. Department of Health: Australia's Physical Activity and Sedentary Behaviour Guidelines for Children (13–17 years)..In. Canberra: Commonwealth of Australia; 2014.Google Scholar
  18. Hardy LL, Booth ML, Okely AD. The reliability of the adolescent sedentary activity questionnaire (ASAQ). Prev Med. 2007;45(1):71–4.View ArticlePubMedGoogle Scholar
  19. Flood VM, Webb K, Rangan A. Recommendations for short questions to assess food consumption in children for the NSW health surveys: NSW Centre for Public Health Nutrition; 2005.Google Scholar
  20. Australian Health Survey. Nutrition First Results - Foods and Nutrients, 2011–12 Discretionary Foods [http://www.abs.gov.au/ausstats/abs@.nsf/Lookup/by%20Subject/4364.0.55.007~2011-12~Main%20Features~Discretionary%20foods~700] Accessed 1 Mar 2016
  21. Manisera M, van der Kooij AJ, Dusseldorp E. Identifying the component 22 structure of satisfaction scales by nonlinear principal components analysis. Qual Technol Quant Manage. 2010;7(2):97–115.View ArticleGoogle Scholar
  22. IBM Corp. IBM SPSS statistics for windows, version 22.0. Armonk, NY: IBM Corp; 2013.Google Scholar
  23. NSW Ministry of Health. Preventing overweight and obesity in new South Wales 2013–2018. In: NSW healthy eating and active living strategy. Sydney: NSW Ministry of Health. p. 2013.Google Scholar
  24. WHO warns against added sugar. The sweet life [https://www.choice.com.au/food-and-drink/nutrition/sugar/articles/who-releases-recommendations-on-free-sugar-consumption-110315] Accessed 8 Apr 2016
  25. Sugar the culprit in children’s diabetes, US study finds [http://www.theaustralian.com.au/business/wall-street-journal/sugar-the-culprit-in-childrens-diabetes-us-study-finds/news-story/123f1cc935af7566113dd29d1465a762] Accessed 8 Apr 2016
  26. Cameron AJ, Ball K, Pearson N, Lioret S, Crawford DA, Campbell K, Hesketh K, McNaughton SA. Socioeconomic variation in diet and activity-related behaviours of Australian children and adolescents aged 2-16 years. Pediatr Obes. 2012;7(4):329–42.View ArticlePubMedGoogle Scholar
  27. Darmon N, Drewnowski A. Does social class predict diet quality? Am J Clin Nutr. 2008;87(5):1107–17.PubMedGoogle Scholar
  28. Hardy LL, King L, Hector D, Baur LA. Socio-cultural differences in Australian primary school children's weight and weight-related behaviours. J Paediatr Child Health. 2013;49(8):641–8.View ArticlePubMedGoogle Scholar
  29. Hardy LL, Hector D, Saleh S, King L. Australian middle Eastern parents' perceptions and practices of children's weight-related behaviours: talking with Parents' study. Health Soc Care Community 2015;24(5):e63–71.Google Scholar
  30. MacFarlane A, Cleland V, Crawford D, Campbell K, Timperio A. Longitudinal examination of the family food environment and weight status among children. Int J Pediatr Obes. 2009;4(4):343–52.View ArticlePubMedGoogle Scholar
  31. Vik FN, Bjørnarå HB, Øverby NC, Lien N, Androutsos O, Maes L, Jan N, Kovacs E, Moreno LA, Dössegger A, Manios Y, Brug J, Bere E. Associations between eating meals, watching TV while eating meals and weight status among children, ages 10–12 years in eight European countries: the ENERGY cross-sectional study. Int J Behav Nutr Phys Act. 2013;10:58.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Biddle SJ, Petrolini I, Pearson N. Interventions designed to reduce sedentary behaviours in young people: a review of reviews. Br J Sports Med. 2014;48(3):182–6.View ArticlePubMedGoogle Scholar
  33. Dixon HG, Scully ML, Wakefield MA, White VM, Crawford DA. The effects of television advertisements for junk food versus nutritious food on children's food attitudes and preferences. Soc Sci Med. 2007;65(7):1311–23.View ArticlePubMedGoogle Scholar
  34. Lobstein T, Dibb S. Evidence of a possible link between obesogenic food advertising and child overweight. Obes Rev. 2005;6(3):203–8.View ArticlePubMedGoogle Scholar
  35. Borghese MM, Tremblay MS, Katzmarzyk PT, Tudor-Locke C, Schuna JM, Leduc G, Boyer C, LeBlanc AG, Chaput J-P. Mediating role of television time, diet patterns, physical activity and sleep duration in the association between television in the bedroom and adiposity in 10 year-old children. Int J Behav Nutr Phys Act. 2015;12(1):1–10.View ArticleGoogle Scholar
  36. Birch L, Savage JS, Ventura A. Influences on the development of Children's eating Behaviours: from infancy to adolescence. Can J Diet Pract Res. 2007;68(1):s1–s56.PubMedPubMed CentralGoogle Scholar
  37. Carnell S, Cooke L, Cheng R, Robbins A, Wardle J. Parental feeding behaviours and motivations. A qualitative study in mothers of UK pre-schoolers. Appetite. 2011;57(3):665–73.View ArticlePubMedGoogle Scholar
  38. Mazarello Paes V, Ong KK, Lakshman R. Factors influencing obesogenic dietary intake in young children (0–6 years): systematic review of qualitative evidence. BMJ Open. 2015;5(9):e007396.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Gibson EL, Kreichauf S, Wildgruber A, Vogele C, Summerbell CD, Nixon C, Moore H, Douthwaite W, Manios Y. A narrative review of psychological and educational strategies applied to young children's eating behaviours aimed at reducing obesity risk. Obes Rev. 2012;13(Suppl 1):85–95.View ArticlePubMedGoogle Scholar
  40. Skinner J, Byun R, Blinkhorn A, Johnson G. Sugary drink consumption and dental caries in new South Wales teenagers. Aust Dent J. 2015;60(2):169–75.View ArticlePubMedGoogle Scholar
  41. Vartanian LR, Schwartz MB, Brownell KD. Effects of soft drink consumption on nutrition and health: a systematic review and meta-analysis. Am J Public Health. 2007;97(4):667–75.View ArticlePubMedPubMed CentralGoogle Scholar
  42. Grunseit AC, Taylor AJ, Hardy LL, King L. Composite measures quantify households' obesogenic potential and adolescents' risk behaviors. Pediatrics. 2011;128(2):e308–16.View ArticlePubMedGoogle Scholar
  43. Powell LM, Nguyen BT. Fast-food and full-service restaurant consumption among children and adolescents: effect on energy, beverage, and nutrient intake. JAMA Pediatr. 2013;167(1):14–20.View ArticlePubMedPubMed CentralGoogle Scholar
  44. Hardy LL, Grunseit A, Khambalia A, Bell C, Wolfenden L, Milat AJ. Co-occurrence of obesogenic risk factors among adolescents. J Adolesc Health. 2012;51(3):265–71.View ArticlePubMedGoogle Scholar
  45. King L, Watson WL, Chapman K, Kelly B, Louie JC, Hughes C, Crawford J, Gill TP. Do we provide meaningful guidance for healthful eating? An investigation into consumers' interpretation of frequency consumption terms. J Nutr Educ Behav. 2012;44(5):459–63.View ArticlePubMedGoogle Scholar
  46. Rangan A, Flood V, Gill T. Misreporting of energy intake in the 2007 Australian Children’s survey: identification. Characteristics Impact Misreporters Nutr. 2011;3(2):186.Google Scholar

Copyright

© The Author(s). 2017

Advertisement