Abstract
Objective
To assess the association between socio-economic position (SEP) and poor eating behaviours in a large representative sample of Australian secondary-school students.
Design
Cross-sectional survey of students’ vegetable, fruit, sugar-sweetened beverage and fast-food consumption assessed using validated instruments and collected via a web-based self-report format.
Setting
Secondary schools across all Australian states and territories.
Subjects
Secondary-school students (n 12 188; response rate: 54 %) aged 12–17 years participating in the 2009–10 National Secondary Students’ Diet and Activity (NaSSDA) survey.
Results
Overall, 25 % of students reported consuming ≤1 serving of vegetables/d and 29 % reported eating ≤1 serving of fruit/d. Fourteen per cent of students reported drinking at least 1–2 cups of sugar-sweetened beverages/d while 9 % reported eating fast food ≥3 times/week. After adjusting for other demographic factors, students of lower-SEP areas were more likely to report low intake of vegetables (F(4, 231) = 3·61, P = 0·007) and high frequency of consumption of sugar-sweetened beverages (F(4, 231) = 8·41, P < 0·001) and fast food (F(4, 231) = 4·59, P = 0·001) compared with students of high-SEP neighbourhoods. A positive SEP association was found for fruit consumption among female students only (F(4, 231) = 4·20, P = 0·003). Those from lower-SEP areas were also more likely to engage in multiple poor eating behaviours (F(4, 231)=5·80, P < 0·001).
Conclusions
Results suggest that socio-economic disparities in Australian adolescents’ eating behaviours do exist, with students residing in lower-SEP neighbourhoods faring less well than those from high-SEP neighbourhoods. Reducing social inequalities in eating behaviours among young people should be a key consideration of future preventive strategies.
Keywords: Socio-economic position, Diet, Adolescents, Australia
Socio-economic position (SEP) is inversely associated with obesity in adolescents( 1 ) and with poorer adolescent diets. International research and review articles have generally found that low SEP is associated with lower consumption of fruit and vegetables among adolescents( 2 – 8 ). However, Australian data are less definitive. A national survey of 654 adolescents found a significant positive association between SEP and fruit consumption (both sexes) and vegetables (girls only)( 9 ), a community-based study of 2529 Victorian adolescents found a positive association between SEP and adolescent fruit consumption( 10 ), while a cross-sectional survey of more than 18 000 Australian secondary students found no significant association between SEP and adolescent fruit and vegetable consumption( 11 ).
There has been considerably less research conducted assessing the association between SEP and adolescent consumption of energy-dense and nutrient-poor foods such as fast foods and sugar-sweetened beverages. Of the available Australian data, the association is generally negative, with adolescents of lower SEP consuming higher amounts of fast foods( 10 , 11 ) and sugar-sweetened beverages( 12 ). Internationally, Wardle et al.( 7 ) found that lower SEP was associated with high-fat diets, while Vereecken et al.( 5 ) reported that socio-economic differences in sugar-sweetened beverage consumption varied depending on the measure of disadvantage used.
Due to the limited Australian data, the aim of the current study was to assess the association between SEP and poor eating behaviours in a large representative sample of Australian secondary-school students.
Methods
Design and procedure
Data were obtained from students participating in the National Secondary Students’ Diet and Activity (NaSSDA) survey 2009–10 (school response rate: 39 %; student response rate: 54 %). A full description of the methods has been reported elsewhere( 1 ). In brief, the sampling procedure was a stratified two-stage probability design, with schools (government, Catholic and independent) randomly selected at the first stage of sampling and classes selected within schools at the second stage. A web-based self-report questionnaire assessing students’ eating, physical activity and sedentary behaviour was administered to participants in their regular class groups, and anthropometric measurements of students’ height, weight and waist circumference were taken by trained researchers in a confidential setting. Active parental consent was required for students to participate in each component of the study.
Measures
Eating behaviours
Short dietary questions developed by the NSW Centre for Public Health Nutrition for the purpose of population-based monitoring surveys were used to assess students’ eating behaviour( 13 ). These items have moderate to good validity and reliability among Australian children aged 10 to 12 years( 14 ). Students were asked to indicate how many servings of vegetables (not including potatoes, hot chips or fried potato) and how many servings of fruit (not including fruit juice) they usually eat each day. Students were shown on-screen visual aids and informed that a serving of vegetables is half a cup of cooked vegetables or one cup of salad vegetables, while a serving of fruit is one medium piece, two small pieces or one cup of diced fruit. Response options ranged from ‘less than one serving per day’ to ‘six or more servings per day’, with students also able to indicate they did not eat fruit or vegetables.
Intake of key non-core foods was assessed by asking students to indicate how frequently they consume sugar-sweetened beverages (e.g. soft drinks, cordials and sports drinks) and how often they have meals or snacks such as burgers, pizza, chicken or chips from fast-food or takeaway-food places. Response options ranged from ‘one cup (250 ml) per week’ to ‘five or more cups per day’ for sugar-sweetened beverages and from ‘less than once a week’ to ‘two or more times per day’ for fast food. Students were also given the option to report they never eat or drink these types of foods and beverages.
Responses were dichotomised to assess poor eating behaviours. As per Thompson et al.( 15 ) and Martinez-Gonzalez et al.( 16 ), low vegetable and fruit consumption was defined as one serving or less per day. High sugar-sweetened beverage consumption was defined as at least one to two cups per day, while high fast-food consumption was classified as three or more times per week. Cut-points for energy-dense and nutrient-poor items were selected to include the highest frequency possible while ensuring sufficient cell sizes.
The number of poor eating behaviours (low fruit, low vegetable, high sugar-sweetened beverage and high fast-food consumption) students engaged in (0–4 behaviours) was also calculated and dichotomised to assess clustering of multiple (two or more) poor eating behaviours.
Socio-economic position
A measure of SEP was determined according to the Socio-Economic Index for Areas Index of Relative Socio-Economic Disadvantage, based on the student's home postcode( 17 ). Students were categorised into SEP groups using the national deciles to create quintiles (first quintile: most disadvantaged; fifth quintile: least disadvantaged).
Potential confounders
Measurements of students’ height and weight were taken in accordance with standardised protocols( 18 ) and used to calculate BMI (weight/height2). Students were classified as either healthy weight/underweight or overweight/obese according to internationally recognised cut-offs developed for children and adolescents( 19 ).
Information on students’ sex and school year was collected. Postcode of residence was also used to classify the geographic location of students as either metropolitan or rural/regional according to the Rural, Remote and Metropolitan Areas Classification( 20 ).
Statistical analyses
Data were analysed using the statistical software package Stata SE 11·1 and weighted by state, education sector, school year and sex to ensure the sample obtained reflected the population distribution( 21 ). Separate logistic regression analyses examined the association between SEP and students’ vegetable, fruit, sugar-sweetened beverage and fast-food consumption, and adjusted Wald tests were used to identify significant SEP differences. All models controlled for sex, school year, geographic location, school-level clustering and education sector (government, Catholic and independent). Exploratory analyses were conducted with BMI category included as an additional control variable; however, as the pattern of results was comparable to what was found when BMI category was excluded, these data are not reported. Adjusted proportions estimated from the model and adjusted odds ratios and confidence intervals are reported in Table 2. Interactions between sex and SEP were tested, with significant results reported in the text.
Table 2.
Consumption of vegetables, fruit, sugar-sweetened beverages and fast food, and clustered poor eating behaviours, by quintile of socio-economic position (SEP) among secondary-school students (n 12 188) aged 12–17 years participating in the 2009–10 National Secondary Students’ Diet and Activity (NaSSDA) survey, Australia
| Low vegetable consumption (≤1 serving/d, n 12 106) | Low fruit consumption (≤1 serving/d, n 12 103) | High sugar-sweetened beverage consumption (at least 1–2 cups/d, n 12 072) | High fast-food consumption (≥3 times/week, n 12 035) | Clustered poor eating behaviours (2–4 poor behaviours, n 12 031) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SEP quintile | Adj. OR | 95 % CI | Adj. proportion (%) | Adj. OR | 95 % CI | Adj. proportion (%) | Adj. OR | 95 % CI | Adj. proportion (%) | Adj. OR | 95 % CI | Adj. proportion (%) | Adj. OR | 95 % CI | Adj. proportion (%) |
| 1st quintile | 1·48* | 1·17, 1·87 | 27·6 | 1·18 | 0·98, 1·41 | 29·3 | 1·82** | 1·43, 2·31 | 16·2 | 1·91** | 1·37, 2·68 | 11·7 | 1·77** | 1·40, 2·25 | 22·8 |
| 2nd quintile | 1·28 | 1·04, 1·57 | 24·8 | 1·13 | 0·92, 1·38 | 28·4 | 1·87** | 1·40, 2·50 | 16·6 | 1·81* | 1·30, 2·54 | 11·2 | 1·61** | 1·24, 2·09 | 21·2 |
| 3rd quintile | 1·41* | 1·16, 1·72 | 26·7 | 1·20 | 1·00, 1·44 | 29·7 | 1·76** | 1·34, 2·32 | 15·8 | 1·71* | 1·24, 2·36 | 10·6 | 1·65** | 1·27, 2·16 | 21·6 |
| 4th quintile | 1·29* | 1·08, 1·54 | 24·9 | 1·20 | 1·00, 1·43 | 29·6 | 1·49** | 1·21, 1·82 | 13·7 | 1·31 | 1·01, 1·71 | 8·4 | 1·47* | 1·17, 1·85 | 19·7 |
| 5th quintile | 1·00 | Ref. | 20·5 | 1·00 | Ref. | 26·0 | 1·00 | Ref. | 9·7 | 1·00 | Ref. | 6·5 | 1·00 | Ref. | 14·4 |
Adj., adjusted; Ref., referent category.
Odds ratios and proportions adjusted for sex, school year, geographic location, school-level clustering and education sector.
*P < 0·01, **P < 0·001.
Students were excluded from the sample if they were outside the target age range (n 53), had not completed at least 33 % of the survey questions (n 223), had not had their physical measurements taken (n 1515), or if no index value was available for their postcode (n 87). A total of 12 188 students were included in the final sample for analysis. Due to the large sample size, a conservative level of statistical significance (P < 0·01) was applied.
Results
As shown in Table 1, of the 12 188 students, 53 % were male. There was a slightly higher proportion of students in Years 8 (31 %) and 9 (27 %) and the majority of students lived in metropolitan locations (64 %). Twenty-six per cent of students were categorised as being in the fifth SEP quintile while 14 % were in the first SEP quintile (compared with 26 % in the fifth SEP quintile and 15 % in the first SEP quintile for the Australian population)( 22 ).
Table 1.
Demographic characteristics of the study population: secondary-school students (n 12 188) aged 12–17 years participating in the 2009–10 National Secondary Students’ Diet and Activity (NaSSDA) survey, Australia
| Frequency, n | Percentage | |
|---|---|---|
| Sex | ||
| Male | 6460 | 53·0 |
| Female | 5728 | 47·0 |
| Year level | ||
| Year 8 | 3772 | 30·9 |
| Year 9 | 3340 | 27·4 |
| Year 10 | 2647 | 21·7 |
| Year 11 | 2429 | 19·9 |
| Socio-economic position | ||
| 1st quintile (most disadvantaged) | 1757 | 14·4 |
| 2nd quintile | 2175 | 17·8 |
| 3rd quintile | 2525 | 20·7 |
| 4th quintile | 2537 | 20·8 |
| 5th quintile (least disadvantaged) | 3194 | 26·2 |
| Geographic location | ||
| Metropolitan | 7841 | 64·3 |
| Rural | 4347 | 35·7 |
Figures based on unweighted data. Percentages may not sum to 100 % due to rounding.
Overall, one-quarter of students reported consuming ≤1 serving of vegetables/d and 29 % reported consuming ≤1 serving of fruit/d. Fourteen per cent of students reported drinking at least 1–2 cups of sugar-sweetened beverages/d and approximately one in ten (9 %) reported consuming fast food ≥3 times/week.
After adjustment for all covariates in the model, adolescent vegetable (F(4, 231) = 3·61, P = 0·007), sugar-sweetened beverage (F(4, 231) = 8·41, P < 0·001) and fast-food consumption (F(4, 231) = 4·59, P = 0·001) differed significantly by SEP (see Table 2). Specifically, students from the first, third and fourth quintiles were more likely than students from the fifth quintile to report low consumption of vegetables. Those from the first through fourth quintiles were more likely to report high frequency of consumption of sugar-sweetened beverages. Students from the first through third quintiles were more likely to report high fast-food consumption compared with those from the fifth quintile.
A significant sex-by-SEP interaction was found for students’ fruit consumption (F(4, 231) = 3·68, P = 0·006). Separate logistic regression models for males and females indicated that fruit consumption varied by SEP only among females (F(4, 231) = 4·20, P = 0·003). Specifically, females in the first (OR = 1·50; 95 % CI 1·16, 1·93, P = 0·002), third (OR = 1·69; 95 % CI 1·31, 2·18, P < 0·001) and fourth (OR = 1·38; 95 % CI 1·09, 1·74, P = 0·007) quintiles were more likely than females in the fifth quintile to report low fruit consumption.
Approximately half (49 %) of students did not engage in any of the four poor eating behaviours. Thirty-two per cent of students engaged in one, 15 % in two, 4 % in three and 1 % in four poor eating behaviours. Logistic regression analysis indicated that clustering of poor eating behaviours differed significantly by SEP (F(4, 231) = 5·80, P < 0·001), with those from the first through fourth quintiles more likely to report two or more poor eating behaviours compared with students from the fifth quintile (Table 2).
Discussion
The current study provides evidence of an association between SEP and poor eating behaviours among a large representative sample of Australian secondary-school students. Adolescents residing in high-SEP areas were less likely to report low vegetable, low fruit (girls only), high sugar-sweetened beverage and high fast-food consumption. These results are consistent with a number of previous Australian studies( 9 , 10 ), international studies( 5 – 8 ) and review articles( 2 – 4 ) reporting lower fruit and vegetable intake and higher fast-food and sugar-sweetened beverage consumption among adolescents of lower SEP. However, the findings differ from a large study of Australian adolescents( 11 ) which found that adolescent fruit and vegetable consumption was unrelated to SEP. These equivocal findings may be attributed to the differences in fruit and vegetable consumption classifications used between the studies. While the current study explored low consumption, Scully et al.( 11 ) focused on whether adolescents were meeting the recommended daily consumption of fruit and vegetables.
The difference in fruit consumption among girls of varying SEP, but not boys, may be attributable to females placing greater importance on avoiding weight gain when making food choices( 23 ) and placing greater value on healthy eating( 24 ). Wardle and Marsland( 25 ) found that girls from higher-SEP schools were more likely to show weight concern and report dieting behaviours. The availability of fruit in the home is strongly related to SEP( 26 – 28 ) and is also more consistently positively associated with consumption in girls than in boys( 29 ). On the other hand, the lack of association between SEP and boys’ fruit intake may be attributed to their food choices being more strongly influenced by factors which may be unrelated to SEP such as peer norms and social influences( 23 ) as well as taste preference( 30 , 31 ). Given reviews have also found that disparities in dietary behaviours can vary when assessed using an area-based measure of SEP( 2 – 4 ), it would be beneficial for future research to aim to replicate these findings using individual-level SEP measures such as household income and parental education in addition to area-based measures.
It was notable that results in the current study did not support a clear socio-economic gradient. While poor eating behaviours were clearly less prevalent among high-SEP adolescents, similar proportions were generally observed for adolescents in the lower four SEP quintiles. Although the proportion of adolescents reporting low vegetable consumption in the second quintile did not significantly differ from that in the least disadvantaged group, there was an apparent trend (P = 0·022) in the expected direction.
Based on the adjusted odds ratios, the magnitude of SEP differences found in the current study for low vegetable consumption was weaker in comparison to those found for high sugar-sweetened beverage and fast-food consumption. However, the actual percentage difference between the low- and high-SEP groups was comparable across these three food categories (between 5 and 7 %), and is of practical significance when considering the potential impact at the population level.
Some study limitations need to be acknowledged. First, the cross-sectional study design precludes inferences of causality. Second, self-reported behaviour allows the possibility of recall and social desirability bias. For example, adolescents may have under-reported their fast-food and sugar-sweetened beverage consumption and over-reported their vegetable and fruit consumption. We would expect, however, that under- and/or over-reporting would have been similar across SEP quintiles. Lastly, the current study used an area-based measure of SEP and did not also include individual-level measures. A strength of the study was the large representative sample size.
Findings suggest there are SEP disparities in Australian adolescents’ eating behaviours, with those residing in lower-SEP neighbourhoods faring less well than those from high-SEP neighbourhoods. Additional research is needed to determine the underlying mechanisms for these observed differences, with nutrition knowledge( 32 , 33 ), availability of foods within the home( 34 ) and perceived cost of fresh produce( 35 ) all possible contributing factors. Reducing social inequalities in eating behaviours among young people should be a key consideration of future preventive strategies.
Acknowledgements
Sources of funding: The NaSSDA survey was jointly funded by state Cancer Councils through Cancer Council Australia, and by the National Heart Foundation of Australia. Cancer Council Australia and the National Heart Foundation of Australia had no role in the design, analysis or writing of this article. Ethics: The NaSSDA study was approved by the Human Research Ethics Committee of Cancer Council Victoria. In addition, approval was obtained from the education authority for each sector in each State and Territory, as well as the principal at each selected school. Conflicts of interest: No conflict of interest is declared. D.C. is supported by a Victorian Health Promotion Foundation Senior Public Health Fellowship; M.W. is supported by a Principal Research Fellowship from the National Health and Medical Research Council. Authors’ contributions: All authors have contributed to the present paper by being involved in conceiving and designing the study or in analysis and interpretation of data, and in writing and revising the paper. I-View was responsible for fieldwork coordination. Acknowledgements: The authors thank the school principals, teachers and students who participated in this study.
The NaSSDA Study Team comprises Cancer Council Victoria: Belinda Morley, Maree Scully, Melanie Wakefield; Technical Advisory Group: Louise Baur (Chair), Anthony Okely, Iain S. Pratt, Jane Bowen, Jo Salmon, Victoria Flood, David Crawford, Anthony Worsley.
References
- 1. Morley BC, Scully M, Niven PH et al. (2012) What factors are associated with excess body weight in Australian secondary school students? Med J Aust 196, 189–192. [DOI] [PubMed] [Google Scholar]
- 2. Rasmussen M, Krolner R, Klepp K-I et al. (2006) Determinants of fruit and vegetable consumption among children and adolescents: a review of the literature. Part I: quantitative studies. Int J Behav Nutr Phys Act 3, 22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Hanson MD & Chen E (2007) Socioeconomic status and health behaviors in adolescence: a review of the literature. J Behav Med 30, 263–285. [DOI] [PubMed] [Google Scholar]
- 4. Pearson N, Biddle SJH & Gorely T (2009) Family correlates of fruit and vegetable consumption in children and adolescents: a systematic review. Public Health Nutr 12, 267–283. [DOI] [PubMed] [Google Scholar]
- 5. Vereecken CA, Inchley J, Subramanian SV et al. (2005) The relative influence of individual and contextual socio-economic status on consumption of fruit and soft drinks among adolescents in Europe. Eur J Public Health 15, 224–232. [DOI] [PubMed] [Google Scholar]
- 6. Lien N, Jacobs DR Jr & Klepp K-I (2002) Exploring predictors of eating behaviour among adolescents by gender and socio-economic status. Public Health Nutr 5, 671–681. [DOI] [PubMed] [Google Scholar]
- 7. Wardle J, Jarvis MJ, Steggles N et al. (2003) Socioeconomic disparities in cancer-risk behaviors in adolescence: baseline results from the Health and Behaviour in Teenagers Study (HABITS). Prev Med 36, 721–730. [DOI] [PubMed] [Google Scholar]
- 8. Neumark-Sztainer D, Story M, Hannan PJ et al. (2002) Overweight status and eating patterns among adolescents: where do youths stand in comparison with the healthy people 2010 objectives? Am J Public Health 92, 844–851. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Giskes K, Turrell G, Patterson C et al. (2002) Socio-economic differences in fruit and vegetable consumption among Australian adolescents and adults. Public Health Nutr 5, 663–669. [DOI] [PubMed] [Google Scholar]
- 10. Ball K, MacFarlane A, Crawford D et al. (2009) Can social cognitive theory constructs explain socio-economic variations in adolescent eating behaviours? A mediation analysis. Health Educ Res 24, 496–506. [DOI] [PubMed] [Google Scholar]
- 11. Scully M, Dixon H, White V et al. (2007) Dietary, physical activity and sedentary behaviour among Australian secondary students in 2005. Health Promot Int 22, 236–245. [DOI] [PubMed] [Google Scholar]
- 12. Clifton PM, Chan L, Moss CL et al. (2011) Beverage intake and obesity in Australian children. Nutr Metab (Lond) 8, 87. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Flood V, Webb K & Rangan A (2005) Recommendations for Short Questions to Assess Food Consumption in Children for the NSW Health Surveys. Sydney: NSW Centre for Public Health Nutrition. [Google Scholar]
- 14. Gwynn JD, Flood VM, D'Este CA et al. (2011) The reliability and validity of a short FFQ among Australian Aboriginal and Torres Strait Islander and non-Indigenous rural children. Public Health Nutr 14, 388–401. [DOI] [PubMed] [Google Scholar]
- 15. Thompson RL, Margetts BM, Speller VM et al. (1999) The Health Education Authority's health and lifestyle survey 1993: who are the low fruit and vegetable consumers? J Epidemiol Community Health 53, 294–299. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Martínez-González MÁ, de la Fuente-Arrillaga C, López-Del-Burgo C et al. (2011) Low consumption of fruit and vegetables and risk of chronic disease: a review of the epidemiological evidence and temporal trends among Spanish graduates. Public Health Nutr 14, 2309–2315. [DOI] [PubMed] [Google Scholar]
- 17. Australian Bureau of Statistics (2008) Information Paper: An Introduction to Socio-Economic Indexes for Areas (SEIFA) 2006. ABS Catalogue no. 2039.0. Canberra: ABS. [Google Scholar]
- 18. Davies PSW, Roodveldt R & Marks G (2001) Standard Methods for the Collection and Collation of Anthropometric Data in Children. Canberra: Commonwealth Department of Health and Aged Care. [Google Scholar]
- 19. Cole T, Bellizzi M, Flegal K et al. (2000) Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 320, 1240–1243. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Department of Primary Industries and Energy, Department of Human Services and Health (1994) Rural, Remote and Metropolitan Areas Classification 1991 Census edition. Canberra: Commonwealth of Australia. [Google Scholar]
- 21. Australian Bureau of Statistics (2010) Schools Australia 2009. ABS Catalogue no. 4221.0. Canberra: ABS. [Google Scholar]
- 22. Australian Bureau of Statistics (2008) Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA) Australia – Data only 2006. ABS Catalogue no. 2033.0.55.001. Canberra: ABS. [Google Scholar]
- 23. Granner ML, Sargent RG, Calderon KS et al. (2004) Factors of fruit and vegetable intake by race, gender, and age among young adolescents. J Nutr Educ Behav 36, 173–180. [DOI] [PubMed] [Google Scholar]
- 24. Pearson N, Ball K & Crawford D (2011) Predictors of changes in adolescents’ consumption of fruits, vegetables and energy-dense snacks. Br J Nutr 105, 795–803. [DOI] [PubMed] [Google Scholar]
- 25. Wardle J & Marsland L (1990) Adolescent concerns about weight and eating; a social-development perspective. J Psychosom Res 34, 377–391. [DOI] [PubMed] [Google Scholar]
- 26. Neumark-Sztainer D, Wall M, Perry C et al. (2003) Correlates of fruit and vegetable intake among adolescents. Findings from Project EAT. Prev Med 37, 198–208. [DOI] [PubMed] [Google Scholar]
- 27. Bere E, van Lenthe F, Klepp KI et al. (2008) Why do parents’ education level and income affect the amount of fruits and vegetables adolescents eat? Eur J Public Health 18, 611–615. [DOI] [PubMed] [Google Scholar]
- 28. Lorson BA, Melgar-Quinonez HR & Taylor CA (2009) Correlates of fruit and vegetable intakes in US children. J Am Diet Assoc 109, 474–478. [DOI] [PubMed] [Google Scholar]
- 29. Cullen KW, Baranowski T, Owens E et al. (2003) Availability, accessibility, and preferences for fruit, 100 % fruit juice, and vegetables influence children's dietary behavior. Health Educ Behav 30, 615–626. [DOI] [PubMed] [Google Scholar]
- 30. Brug J, Tak NI, te Velde SJ et al. (2008) Taste preferences, liking and other factors related to fruit and vegetable intakes among schoolchildren: results from observational studies. Br J Nutr 99, Suppl. 1, S7–S14. [DOI] [PubMed] [Google Scholar]
- 31. Sandvik C, Gjestad R, Samdal O et al. (2010) Does socio-economic status moderate the associations between psychosocial predictors and fruit intake in schoolchildren? The Pro Children study. Health Educ Res 25, 121–134. [DOI] [PubMed] [Google Scholar]
- 32. Hendrie GA, Coveney J & Cox D (2008) Exploring nutrition knowledge and the demographic variation in knowledge levels in an Australian community sample. Public Health Nutr 11, 1365–1371. [DOI] [PubMed] [Google Scholar]
- 33. Coveney J (2005) A qualitative study exploring socio-economic differences in parental lay knowledge of food and health: implications for public health nutrition. Public Health Nutr 8, 290–297. [DOI] [PubMed] [Google Scholar]
- 34. MacFarlane A, Crawford D, Ball K et al. (2007) Adolescent home food environments and socioeconomic position. Asia Pac J Clin Nutr 16, 748–756. [PubMed] [Google Scholar]
- 35. Turrell G & Kavanagh AM (2006) Socio-economic pathways to diet: modelling the association between socio-economic position and food purchasing behaviour. Public Health Nutr 9, 375–383. [DOI] [PubMed] [Google Scholar]