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1-1-2014

Food patterns of Australian children ages 9 to 13 y in relation to ω-3 long chain polyunsaturated intake

Setyaningrum Rahmawaty University of Wollongong

Philippa Lyons-Wall Edith Cowan University, [email protected]

Marijka Batterham University of Wollongong, [email protected]

Karen Charlton University of Wollongong, [email protected]

Barbara J. Meyer University of Wollongong, [email protected]

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Recommended Citation Rahmawaty, Setyaningrum; Lyons-Wall, Philippa; Batterham, Marijka; Charlton, Karen; and Meyer, Barbara J., "Food patterns of Australian children ages 9 to 13 y in relation to ω-3 long chain polyunsaturated intake" (2014). Faculty of Science, Medicine and Health - Papers: part A. 1347. https://ro.uow.edu.au/smhpapers/1347

Research Online is the open access institutional repository for the University of Wollongong. For further information contact the UOW Library: [email protected] Food patterns of Australian children ages 9 to 13 y in relation to ω-3 long chain polyunsaturated intake

Abstract Objective: The aim of this study was to examine food patterns of Australian children ages 9 to 13 y in relation to ω-3 long-chain polyunsaturated fatty acid (ω-3 LCPUFA) intake.

Methods: Secondary analysis was conducted on nationally representative food data of 1110 Australian children ages 9 to 13 y (525 boys and 585 girls) that was obtained using two 24-h recalls. Principle component factor analysis was used to identify food patterns. Discriminant function analysis was used to identify the relationship between the food patterns and total ω-3 LCPUFA intake.

Results: Four major food patterns emerged for each sex. For boys these were labeled: “snack foods,” “soft drinks,” “,” and “pork and meat chops, steak, and mince.” For girls they were labeled: “vegetables,” “take-away,” “tea, coffee, iced coffee drinks” and “canned meals and soup.” Fish consumption bought from take-away outlets was more frequently consumed in the “soft drink” (r = 0.577) and take-away (r = 0.485) food pattern in boys and girls, respectively. In contrast, fish prepared at home was more often consumed in “vegetables” in both boys (r = 0.018) and girls (r = 0.106), as well as in the “pork and meat chops, steak and mince” food pattern in boys (r = 0.060). There was a trend that in boys, the “vegetables” group discriminated children who consumed ω-3 LCPUFA levels similar to adequate intakes (AI) (P = 0.067), whereas in girls, the take-away food pattern discriminated for being a fish consumer (P = 0.060).

Conclusions: Dietary patterns associated with a high consumption of vegetables and “take-aways” food that include meat and fish are likely to positively influence dietary ω-3 LCPUFA intake in Australian children.

Keywords food, patterns, intake, australian, children, 9, 13, relation, 3, long, chain, polyunsaturated, ages, y

Disciplines Medicine and Health Sciences | Social and Behavioral Sciences

Publication Details Rahmawaty, S., Lyons-Wall, P., Batterham, M., Charlton, K. & Meyer, B. J. (2014). Food patterns of Australian children ages 9 to 13 y in relation to ω-3 long chain polyunsaturated intake. Nutrition, 30 (2), 169-176.

This journal article is available at Research Online: https://ro.uow.edu.au/smhpapers/1347 This article was originally published as: Rahmawaty, S., Lyons‐Wall, P., Batterham, M., Charlton, K. & Meyer, B. J. (2014). Food patterns of Australian children ages 9 to 13 y in relation to ω‐3 long chain polyunsaturated intake. Nutrition, 30 (2), 169‐176.

Title page Food patterns of Australian children aged 9-13 years in relation to omega-

3 long chain polyunsaturated (n-3 LCPUFA) intake

Running head: Food patterns in relation to n-3 LCPUFA intake

Authors: Setyaningrum Rahmawaty1,2, Philippa Lyons-Wall3, Marijka Batterham4, Karen Charlton2, Barbara J Meyer1,2

Setyaningrum Rahmawaty 1Metabolic Research Centre and 2School of Health Sciences, University of Wollongong, Northfields Ave, Wollongong NSW 2522, Australia. Email: [email protected]

Philippa Lyons-Wall 3School of Exercise and Health Sciences, Edith Cowan University, 270 Joondalup Drive, Joondalup, WA 6027, Australia. Email: [email protected] previously at School of Health Sciences, University of Wollongong, Northfields Ave, Wollongong NSW 2522, Australia

Marijka Batterham 4Director, Statistical Consulting Service, University of Wollongong, Northfields Ave, Wollongong NSW 2522, Australia. Email: [email protected]

Karen Charlton 2School of Health Sciences, University of Wollongong, Northfields Ave, Wollongong NSW 2522, Australia. Email: [email protected]

Barbara J Meyer () 1Metabolic Research Centre and 2School of Health Sciences, University of Wollongong, Northfields Ave, Wollongong NSW 2522, Australia. Email: [email protected]. Tel.: +61 (0)2 4221 3459, Fax: +61 (0)2 4221 5945

Role of each author in the work: SR performed the statistical analysis and interpretation of the data and drafted the manuscript. PLW participated in study design and assisted with the first draft of the manuscript, MB provided statistical consultation and interpretation of the data, KC and BJ co-authored the manuscript and were involved in conceptualization of the study. All authors contributed to the manuscript, read and approved the final manuscript.

A word count for the entire manuscript (including figures and tables) 5000

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Abstract

Objective: This study examined foods patterns of Australian children aged 9-13 years in relation to omega-3 long chain polyunsaturated fatty acid (n-3 LCPUFA) intake.

Research Methods and Procedures: Secondary analysis was conducted on nationally representative food data of 1,110 Australian children aged 9-13 years (525 boys, 585 girls) that was obtained using two 24-hour recalls. Principle component factor analysis was used to identify food patterns.

Discriminant function analysis was used to identify the relationship between the food patterns and total n-3 LCPUFA intake.

Results: Four major food patterns emerged in each sex. For boys these were labelled: ‘snack foods’,

‘soft drinks’, ‘vegetables’ and ‘pork and meat chops, steak and mince’, and for girls: ‘vegetables’,

‘take aways’, ‘tea, coffee, iced coffee drinks’ and ‘canned meals and soup’. Fish consumption bought from take-away outlets was more frequently consumed in the ‘soft drink’ (r = 0.577) and

‘take aways’ (r = 0.485) food pattern in boys and girls, respectively. In contrast, fish consumption prepared at home was more often consumed in ‘vegetables’ in both boys (r = 0.018) and girls (r =

0.106), as well as in the ‘pork and meat chops, steak and mince’ food pattern in boys (r = 0.060).

There was a trend that in boys, the ‘vegetables’ group discriminated children who consumed n-3

LCPUFA levels similar to adequate intakes (AI) (p = 0.067), while in girls, the ‘take aways’ food pattern discriminated for being a fish consumer (p = 0.060).

Conclusion: Dietary patterns associated with a high consumption of vegetables and ‘take aways’ that include meat and fish are likely to positively influence dietary n-3 LCPUFA intake in

Australian children.

Keywords: dietary patterns, n-3 LCPUFA, children, Australia

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Introduction

Sufficient dietary intake of omega-3 long chain polyunsaturated fatty acid (n-3 LCPUFA) is required for optimal health, to reduce the incidence of chronic diseases such as cardiovascular diseases (CVD) in adults [1, 2] and to prevent obesity-related chronic diseases in children [3]. In

Australia, the National Health and Medical Research Council (NHMRC) has established the nutrient reference value for n-3 LCPUFA in children younger than 14 years based on adequate intake (AI) data [4] or observed median intakes from the National Dietary Survey[5]. For children aged 14-16 years, a suggested dietary target (SDT) recommended for prevention of chronic diseases, has been set at 610 and 430 mg/d for boys and girls, respectively, based on the observed

90th percentile of the population intake [4]. The AI is defined as ‘the average daily nutrient intake level based on observed or experimentally-determined approximations or estimates of nutrient intake by a group (or groups) of apparently healthy people that are assumed to be adequate’ and the

SDT is defined as ‘a daily average intake from food and beverages for certain nutrients that may help in prevention of chronic disease’ [4]. Hence the AI merely reflects the median intakes of the population and is not a recommended intake, while the SDT is a target intake for optimal health.

The National Heart Foundation of Australia recommends an n-3 LCPUFA intake of 500 mg per day for adults and that children should follow the adult recommendation [6]. Meyer and Kolanu (2011) have extrapolated SDTs for children younger than 14 years from adjusted energy intakes, by sex and age group [7].

The contribution of fish, particularly oily fish, as well as foods enriched with n-3 LCPUFA to improving n-3 LCPUFA intake has been widely reported [8, 9] including in Australian children

[10]. However, only approximately a fifth of Australian children consume fish regularly and less than 7% of children include n-3 enriched foods in their diets [7]. Our group has previously reported, using dietary modeling, that substitution of Australian children’s intakes of bread, egg, milk and yoghurt with n-3 enriched products that are commercially available in supermarkets would

3 significantly increase n-3 LCPUFA intake [11], but not to the suggested dietary target level recommended by the NHMRC [4].

Individuals consume meals or dishes that commonly comprise a variety of foods with complex combinations of nutrients that are likely to be interactive or synergistic [12], therefore it is necessary to examine the effect of overall diet rather than a single food when evaluating intakes of specific nutrients in relation to health outcomes [13]. Food pattern analysis has been used to examine the relationship between overall dietary patterns and the risk of chronic diseases [14,15], nutritional status [16,17]. This method has also been used to evaluate dietary guidelines or dietary recommendations due to the capability of the food pattern analysis to generate a broader picture of food consumption in a population [13]. It has been recommended that differences in dietary patterns between gender should be examined specifically when assessing the influence of parental lifestyle on the children’s food preferences [18,19] like fish consumption. Moreover, differences in disease risks such as coronary heart diseases were exist between sexes [20], and it can be reduced by consuming fish [21]. The aim of this study was to explore food patterns of Australian children in relation to n-3 LCPUFA intake, in the context of the whole diet.

Materials and Methods

Subjects

Food data from a nationally representative sample of 1,110 children aged 9-13 years (525 boys and

585 girls), obtained from the 2007 Australian National Children’s Nutrition and Physical Activity

Survey (Children’s survey) [22], were included in this study. The survey was conducted between

February and August 2007 according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by the NHMRC registered ethics committees of the Commonwealth Scientific and Industrial Research Organization and the

University of South Australia [22]. Permission to access the dataset was obtained from the

Australian Social Science Data Archive [23]. 4

Dietary assessment, food grouping and food pattern analysis

Dietary intake was assessed from two 24-hour recalls using a standardized multiple pass 24- hour dietary recall methodology during computer assisted personal interview (CAPI) and computer assisted telephone interview (CATI) [24].

A total of 500 food items in the two 24-hour recalls were grouped into 57 food groups based on the similarity of macronutrient composition (e.g. and fibre content), food behaviours (e.g. take- away, ethnic dishes and specialty items), mixed dishes, food sources of n-3 LCPUFA and foods that have the potential to be enriched with n-3 LCPUFA (Table 1). To examine food patterns associated with n-3 LCPUFA intake, intake of foods in each food group, rather than intake of specific nutrients, were used as principal components for analysis. Foods that did not contain n-3

LCPUFA’s were also included in the modeling, for example products and dishes, fruit products and dishes, confectionery and cereal/nut/fruit/ bars, non-alcoholic beverages, dairy substitutes, seed and nut products and dishes, as well as legume and pulse products and dishes [10].

The percentage contribution from each food group for each respondent was then entered for components analysis within the factor analysis.

To investigate the underlying structure of the 57 food group items, the data were subjected to principal axis factoring with varimax rotation by gender. Prior to conducting the principal axis factoring, the data were examined for normality. Furthermore, a linear relationship was identified among the variables. The anti-image matrices were used to examine the suitability of the data for factor analysis. Twenty five food groups in boys and thirteen food groups in girls were excluded before running the factor analysis, because the Kaiser-Meyer-Olkin (KMO) value was below 0.5 and therefore the amount of variance in the data that could be explained by the factors was deemed unacceptable [25]. Four factors (with Eigenvalues or the variance of the factors exceeding 1) were identified in boys and in girls as underlying the thirty two and forty four food groups respectively

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(see Table 2). These factors (food patterns) were labeled based on the highest factor score in each factor.

Statistical analysis

The statistical analysis was carried out using Statistical Package for the Social Sciences

(SPSS) software version 17.0, Chicago IL, USA. Intakes of eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA) and total n-3 LCPUFA (mg/d) are presented as mean ± SD and median (IQR). Intakes of n-3 LCPUFA were determined by fish eater status, based on consumption of at least one serve of fish (yes/no) from the two-24 hour recalls. Data were tested for normality using the Kolmogorov-Smirnov test and as the data were skewed, values were log-transformed

(log10). Factor analysis was used to identify food patterns based on the 57 food groups.

Discriminant function analysis was used to identify the relationship between food groups and the n-

3 LCPUFA intake status. The scores obtained by the factor analysis were considered as continuous independent variables, and n-3 intake was considered as a dichotomous dependent variable [26], expressed as either achieving or not achieving nutrient reference values (AI and SDT), as well as by fish eater status. Separate regression analyses were performed for each factor to test whether food patterns predicted changes in n-3 LCPUFA intake.

Results

Food patterns produced by factor analysis

Four food patterns were identified in boys and in girls, and were labeled according to the highest positive factor loading (Table 2). In boys, the identified food patterns ‘snack foods’, ‘soft drinks’, ‘vegetables’ and ‘pork and meat chops, steak and mince’ explained 5.3%, 5.2%, 5.2% and

5.1%, respectively, of the variance of food. In girls, derived food patterns were named ‘vegetables’,

‘take aways’, ‘tea, coffee, iced coffee drinks’ and ‘canned meals and soups’, which explained 4.2%,

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4.1%, 3.6% and 3.6%, respectively, of the variance of food. In total, these factors accounted for

20.8% and 15.5% of the variance of food among boys and girls respectively (Table 2).

In boys, the ‘snack foods’ pattern was characterized by high consumption (high positive factor loadings) of processed snack foods, chicken (all types, including nuggets, curry), and breakfast cereals, with low intakes (negative factor loadings) of fish, eggs and fatty meats; ‘soft drinks’ was characterized by high consumption of soft drink and take away items (meat, fish, cereal and mixed), with low consumption of fish; ‘vegetables’ was characterized by high intakes of vegetables, nuts and and sweet potato; while ‘pork and meat chops, steak and mince’ was characterized by high consumption of pork and meat chops, steak and mince, cheese, potato, ice cream and soft drink with low consumption of fish and rare intake of egg.

In girls, the ‘vegetables’ pattern was characterized by high consumption of vegetables, potato, sausage, nuts and seeds, sweet potato and soft drink, with low consumption of fish and rare intake of yoghurt; ‘take-aways’ was characterized by high consumption of take-aways (meat, fish, cereal and vegetable mixed), speciality (meat, cereal and vegetable mixed), soft drink and snack food, with low consumptions of fish and eggs; ‘tea, coffee, iced coffee drinks ‘ was characterized by high consumption of tea, coffee, iced coffee drinks and mixed vegetable dishes, with low intakes of fish and yoghurt; while ‘canned meals and soups’ was characterized by high consumption of canned meals and soup and speciality drinks (such as energy drink, sports drink, protein drink, malt drink, Powerade, Grapetiser, Gatorade, Cider, Yakult, powdered beverage), with low consumption of yoghurt and egg (Table 2).

Characteristics of the children in each food pattern group, by gender are shown in Table 3.

The characteristics of food patterns in this study were obtained from factor scores generated from the factor analysis, where this method produced continuous dietary factor scores for each child in each of the food pattern. High consumption of ‘take aways’ food including take away fish was shown in ‘soft drink’ and ‘take aways’ food patterns in boys and girls respectively (Table 3).

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Children in the ‘soft drink’ food pattern in boys (r = 0.577) and ‘take-away’ food pattern in girls (r = 0.485) were consuming fish purchased from take-away outlets (including fish and chips, fish cakes, and fish fingers crumbed, fried or deep fried), more frequently than children in other food pattern groups, while intake of fish prepared from home and canned fish was relatively rare (r

= -0.144 in boys, r = -0.046 in girls). Consumption of fish prepared from home and canned fish was slightly more frequent in the ‘vegetables’ food pattern in boys (r = 0.018) and girls (r = 0.106), and in the ‘pork and meat chops, steak and mince’ group in boys (r = 0.060) (Table 2).

Association between food patterns and n-3 LCPUFA intake status

Discriminant analysis was conducted to predict how well the food patterns separated children according to n-3 LCPUFA intake status and fish eater status (Table 4). Differences in mean intake that approached statistical significance were observed in the ‘vegetable’ food pattern for boys who consumed n-3 LCPUFA levels similar to the AI (p = 0.067), and in the ‘take-away’ food pattern for girls who were fish eaters (p = 0.06). No food patterns were able to predict the ability of children to achieve the SDT for n-3 LCPUFA (Table 4).

Discussion

In this nationally representative study of Australian children aged 9 to 13 y, food sources of n-

3 LCPUFA together with foods that have the potential to be enriched in Australia were examined, in the context of dietary patterns in children. Factor analysis was used to generate food patterns in which correlations between consumption of various foods were captured, and subjects were grouped according to scores based on diets with similar patterns of variation. Hence, major dietary habits can be characterized within a population [27].

The dietary patterns generated in this study support previous studies in Australian children, in which high food loadings have been reported for high fat and sugar intake [28, 29], vegetables [28], sugar sweetened beverages [30] and snack foods [31]. For example, a study of Western Australian

8 adolescents demonstrated that their food patterns reflected a Western diet characterized by high consumption of take-away foods, soft drinks, confectionery, , refined grains, full-fat dairy products and processed meats [29]. This pattern resulted in a high intake of saturated fat and monounsaturated fat, refined sugar and sodium, and low intake of fibre, folate and natural , and was associated clustering of CVD risk factors, including raised total cholesterol levels [30]. A

Western dietary pattern was also associated with poor behavior outcome scores, for example withdrawal, somatic complaints, anxiety/depression, delinquency and aggression, while a higher intake of leafy green vegetables and fresh fruit was correlated with better behavioral outcomes [32].

Another study reported that the intake of Victorian adolescents (12-13 y) did not conform to recommendations outlined by the Australian Guide to Healthy Eating (AGHE) [33], and that 22% reported eating fast foods every day, while over a third reported eating fruit rarely or never [34].

In the context of n-3 LCPUFA intake, the data in this study showed that girls in the ‘take- away’ food pattern and boys in the ‘soft drink’ food pattern more frequently consumed fish bought from outlets or restaurants, while boys and girls in the ‘vegetables’ food pattern, boys in the ‘pork and meat chops, steak and mince’ group and girls in the ‘take-away’ group more often consumed fish prepared from home (Table 2). About one fifth of boys and girls in these categories were fish consumers, on at least one of the days of the survey (Table 3). In contrast, children in the other food pattern groups rarely consumed fish, as demonstrated by a negative value of the correlation coefficients. Our data also demonstrated that consumption of ‘take aways’ in girls was likely associated with being a fish eater (p = 0.060). Fish is the major contributor to n-3 LCPUFA intake

[35] and meat also contain n-3 LCPUFA [5]. Australian children consume at least 8 times more meat than fish/seafood and therefore meat contributes quite substantially to n-3 LCPUFA intakes

[7]. Although overall fish intake was low in this study, the factor loadings of fish bought from take- away outlets in our study were approximately 4 times higher compared to fish prepared from home,

(Table 2) which could have nutritional implications. In a study among Australian families with young children aged 9-13 years, nearly half of the sample reported consuming take-away fish and

9 chips at least once a month [36]. Although the frying method does not necessarily impact on the total content of EPA and DHA in the fish, the oil used for frying will change the lipid content of the fish due to oil absorption during frying. It has been reported that the use of an n-6 oil, such as sunflower or safflower, results in a decreased ratio of n-3 to n-6 in fried salmon [37,38] codfish, hake, sole [39] mackerel and sardines [38].

In this study, food consumption in the ‘vegetables’ food pattern group in both boys and girls tended to be healthier compared to the other food patterns, with a greater variety of plant-based foods. These included higher intakes of a range of common vegetables such as carrots, broccoli and green beans, and salad items such as tomato, cucumber and beetroot, together with sweet potato, potato, nuts and seeds. Vegetable consumption was associated with intake of chicken in girls, and sausages and soft drinks in boys, and also lower intakes of fish in both groups. Furthermore, of foods that have potential to be enriched with n-3 LCPUFA, eggs were consumed within the

‘vegetables’ pattern for both sexes (Table 2). Vegetables are recommended as part of healthy eating guidelines [33] and benefits of vegetable consumption are well documented, including prevention of food-related chronic diseases risk such as CVD [40]. In a study of American families with young children [41], vegetable consumption demonstrated a powerful role in increasing the enjoyment of family meals, not only as part of balanced meal but also as a flavor enhancer. It has been reported that children meeting the SDT for n-3 LCPUFA intake consume more vegetables (178 g) than children who do not meet the SDT (138 g) [7].

The data in this study confirms previous reports [10] that most Australian children do not consume fish (Table 2). A number of barriers to fish consumption have been identified in children, such as negative attitude towards both the smell and the accompaniments, and fear of finding bones

[42], as well as the influence of other family members [43]. The level of confidence of the mother to serve fish for their family has been reported as a significant determinant of whether fish is included as a regular menu item in Australian families with young children [44]. We previously surveyed Australian families with young children, and demonstrated that cooking courses and

10 cooking books were scored as the most important nutrition education materials for improvement of the self-confidence of parents to serve fish for their family [36].

In countries with traditionally low fish consumption such as Australia, there are recommendations to consume more n-3 enriched products or to take fish oil supplements as alternative strategies to improve intake of n-3 LCPUFA [43]. The health benefits of consumption of products enriched with n-3 are well documented [44-48] even with small dose enrichment [49].

Some land-based plants such as flaxseed can produce short chain n-3 oils, but are unable to produce the long chain n-3 oils such as DHA. Currently, the Commonwealth Scientific and Industrial

Research Organization (CSIRO) are developing plants containing the n-3 LCPUFA typically found in fish oil using gene technology [50]. This could provide an alternative and more sustainable source of these oils for Australian to meet the dietary recommendation for n-3 LCPUFA.

A potential limitation of our study is that data were obtained from two 24-h dietary recalls.

However, while this tool may not capture usual individual intake, it is appropriate for obtaining patterns of intake at a group level. Our results are also comparable with those of other studies that have reported low fish consumption among Australian children [51,52]. A further limitation is that factor analysis, while being recognized as a valid method to assess dietary patterns in a group, it is sample specific and therefore the results may not be generalizable to other populations.

Conclusion

Different food patterns between boys and girls were obtained in Australian children’s diet.

Food patterns that include a high consumption of vegetables and ‘take aways’ that include meat and fish are likely to influence the total dietary n-3 LCPUFA intake. Nutrition education is needed to support the recommendation of two serving fish per week in order to meet dietary targets for n-3

LCPUFA intake, including information on appropriate cooking methods to maintain the health benefits of fish consumption associated with n-3 LCPUFA intake.

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Acknowledgements

We would like to thank to the Australian Social Science Data Archive for permission using the

2007 Children’s survey dietary data. We would also like to thank the Directorate General of Higher

Education Indonesia for sponsoring Setyaningrum Rahmawaty, a lecturer from the University of

Muhammadiyah Surakarta Indonesia for her PhD at the University of Wollongong, New South

Wales, Australia.

Conflict of Interests

None identified.

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Table 1. Food grouping used in this study of Australian children (age = 9-13 years, n = 1,110) Food/ Food Groups Food items on dietary recall using CAPI and CATI 1. Chicken†b Chicken, turkey, chicken nuggets, kangaroo, nuggets, chicken roll, duck, lemon chicken, quail 2. Pork and meat chops, Pork, bacon, meat, ham, sweet and sour pork, savoury mince steak and mince† 3. Sausages†a Sausage, , salami, cabanossi, devon, kabana, saveloy, pepperoni 4. Beef†a,b Beef, corned beef, veal, steak, pastrami, silverside, beef jerky, roast, venison 5. Lamb† Lamb 6. Take away (meat†, Frankfurter, hotdog, McDonald, Kentucky , meat sticks, patty, fish†, cereal and burger, , hamburger, , Dominos, Burger King, Hungry jacks, vegetable mixed) pizza pocket, pizza hut, bakers delight product, Subway, pastie, , pie, Chiko roll, fried/deep fried fish bought from take away outlet and restaurant (squid or calamari, fish cake and fish finger crumbled) 7. Mixed meat dishes Curry, luncheon meat, mince meat, meatloaf, meatballs, curry puff, chilli con carne, casserole, stew, rissoles 8. Specialty (meat, cereal , , dumpling, fajita, parmigiana, , bok choy, moussaka, dim and vegetable mixed)a sims, stroganoff, toad in the hole, , chop suey, kebbeh, , , yiros, , international food, spinach and feta roll, nachos, , recipe mean 9. Bread*a Toast, bread, bread roll, garlic bread, pita bread, French toast, mountain bread, fairy bread, 10. * Sandwich, filled bread/roll 11. Muffina Muffin, English muffin 12. Savoury biscuit & other , savoury biscuits, wrap*, foccacia, tortilla, craker, pide, pappadam, bread typea Corn Thins, Jatz, Damper, Nashu, crumpets 13. Specialty cereal Sushi, couscous, polenta, (mixed)a,b 14. Noodlesa Noodles, chow mein, , mie goreng 15. Breakfast cerealsb Cereal*, all bran, Weetbix , oats, Up and Go, barley, Quinoa, Wheat, Pupped wheat, porridge 16. Pastaa Pasta, lasagne, canned spaghetti, pasta sauce, macaroni cheese, pasta bake, spaghetti, homemade pasta, Bolognese sauce, bolognase, ravioli, cannelloni, carbonara 17. Rice Rice, risotto, fried rice, infant food 18. Sweet biscuit Sweet biscuit 19. Cake Cake, rice cake, tart, cup cake, custard tart 20. Pastry Pastry, pie, pancakes, bun (sweet), lamingtons, croissant, pikelets, doughnut, Loaf-sweet, hot cross bun, donut, cone/wafer, apple pie, filo pastry, Tim Tam, scones, strudel, brownie, profiterole, halawa, crepe, vol au vonts, souffle, crumble, roll, chiko roll, waffles, danish, snow delight 21. Dessert & puddingsa Pudding, Chocolate mousse, Mousse 22. Milk*a Milk 23. Flavoured milkb Milo, milky way, chocolate milk, Sustagen (ready made), milkshake, flavoured milk, Milo powder, milo drink, Sustagen powder, soy milk, strawberry milk, smoothie, Sip ahh straw, Ovaltine, Toodler formula (powder), Nesquick, hot chocolate, hot cocoa, Nesquick powder, drinking chocolate, Cocoa (eg. bournville cocoa), Babbycino, Quick 24. Cream*a Cream, cream bun, sour cream, custard, bavarian cream 25. Yoghurt*a Yoghurt, frozen yoghurt, soy yoghurt 26. Cheese* Cheese, cheese cake 27. Juicea,b Juice, cordial syrup, Ribena 28. Fruit juicea Apple juice, juice concentrate, Boost juice, Punch, Nectar 29. tea, coffee, iced Gloria Jeans drink, tea, coffee, iced coffee coffee drinks 13

30. Soft drink Soft drink, Coke, Fanta, frozen soft drink, squash, lemonade, Slushy (frozen soft drink) 31. Specialty drink Energy drink, sports drink, protein drink, malt drink, Powerade, Grapetiser, Gatorade, Cider, Yakult, powdered beverage 32. Potato Potato, potatoes, potato salad, gnocchi, mash, hash browns 33. Sweet potato Sweet potato 34. Vegetables Salad, zucchini, corn, mushroom, pumpkin, broccoli, carrot, tomato, vegetable, cabbage, cucumber, celery, cauliflower, lettuce, spinach, asparagus, salad roll, mixed vegetables, green beans, beetroot, snow , sprout, dressing, leek, pickle, gherkin, brussels sprout, parsnip, silverbeet, swede, turnip, okra, seaweed, capsicum 35. Mixed vegetable dishesa Stir-fry, mashed vegetables, , coleslaw, Asian greens, 36. Confectionarya Chocolate, lollies, Carob (chocolate), lollipop, Flake, Aero, Marshmallow, Nutella, M & MS, chewing gum, bubblegum, Twix, Fudge, Popslice, Kit Kat, Malteres, Rocky road, Crunchie, Time Out, licorice, chocolate crackles, Easter egg, Slice/balls (sweet), Ovalteenies, Mars bar, chocolate bar, Cherry ripe, Icy pole, Zooper doper, ice block 37. Snack bar†† Snack bar, fruit bar (e.g. Bellis), muesli bar, muffin bar, fruit bar, sport bar, snack bar, LCM, protein bar, Cheezels, cheese rings, picnic (bar) 38. Apple Apple 39. Bananaa Banana 40. Mandarin Mandarin 41. Other fruit Olive, grapes, kiwifruit, plum, orange, avocado, strawberry, rockmelon, pear, fruit, watermelon, mango, grape, apricot, raspberry, passionfruit, pineapple, peach, peaches, grapefruit, blueberry, pears, date, lemon, cantaloupe, apricots, melon, cherry, sultanas, cranberry, persimmon, prune, raisin, currant, figs, goji berries, tangerine, pomegranate, pawpaw, tangelo, guava, nectarine, honeydew melon 42. Fruit and dishesb Fruit salad, fruit snack, apple crumble, toffee apple, canned fruit, dried fruit, fruit crumble, sorbet, fruit leather/strap, rifle, fruche/frousse 43. Snack food Crisps, snack, crackers, dairy rice snack, Le snack, Cherrios, Oreo wafer stick, , Nachos, spring roll, rice crakers, roll up, pop corn, , pringels 44. Butter/margarine*a,b Butter, fat, margarine 45. Spreads*a,b Vegemite, spread, hummus, savoury, dip, peanut butter 46. Saucesa,b Gravy, mornay 47. Fish† Fish, salmon, tuna, fish fingers (includes canned fish) prepared from home 48. Other seafood†b Squid, lobster, scallop, prawns, calamari, seafood, prawn cracker, prawn toast, quiche 49. Egg†* Egg, omelette, scrambled egg 50. Legumes†† Beans, baked beans, lentils, tofu, kidney beans, , bean curd, TVP, dhal 51. Nut and seed†† Nuts, peanuts, , almond, seeds, chick peas, homus, pistachio, almonds 52. Sugar product and Jelly, sugar, jam, honey, syrup, icing, topping, jelly beans, candyfloss, mint, dishesa Chupa chup, pavlova, Meringue, hundreds and thousands, sprinkles 53. Ice cream Ice cream, Thick shake, Cone 54. Canned meals and Soup, canned meal (does not include canned fish) soupa 55. Dietary supplementa,b Dietary supplement, fish oil, supplement, , meal replacement powder, formula 56. Savoury saucesa Coconut milk, sauce simmer, herbs, onion, curry powder, coconut, garlic, chilli, ginger, tomato sauce, mustard, sauce, stock, tomato puree 57. Wine, beer & alcohola,b Alcoholic soda, Vodka, Beer, Ginger beer †Natural sources of n-3 LCPUFA; †† Natural sources of n-3 ALA; * Products available in supermarket contained omega-3 (ALA or n-3 LCPUFA) aWas dropped for running in factor analysis in boys, bWas dropped for running in factor analysis in girls because the Kaiser-Meyer-Olkin (KMO) value bellowed 0.5 (no strong correlation) 14

1 Table 2. Food patterns a of Australian children by gender (age = 9-13 years, n = 1,110) Boysb Snack food Soft drink Vegetables Pork and meat chops, steak and mince Snack foodc 0.511d Soft drink 0.619 Vegetables 0.663 Pork and meat chops, steak and 0.764 Chicken† 0.493 Take aways (meat, fish†e, 0.577 Nut and seed†† 0.657 mince† Sandwich* 0.477 cereal and vegetable mixed) Sweet potato 0.594 Cheese* 0.758 Breakfast cereals 0.446 Sweet biscuit 0.293 Potato 0.286 Potato 0.363 Tea, coffee 0.348 Specialty drink 0.253 Chicken† 0.213 Ice cream 0.370 Ice cream 0.268 Egg†* 0.097 Lamb 0.177 Soft drink 0.185 Nut and seed†† 0.253 Fish† -0.144 Egg†* 0.074 Fish† 0.060 Rice 0.235 Cake -0.263 Fish† 0.018 Egg†* -0.120 Flavoured milk 0.226 Apple -0.363 Fish† -0.004 Other fruit -0.514 Egg†* -0.100 Pork and meat chops, steak -0.201 and mince† Girlsb Vegetables Take aways Tea, coffee, iced coffee drinks Canned meals and soup Vegetables 0.620 Take aways (meat, fish†e, 0.485 Tea and coffee 0.751 Soup 0.406 Potato 0.584 cereal and vegetable mixed) Mixed vegetable dishes 0.669 Specialty drink 0.353 Sausages† 0.434 Specialty meat, cereal and 0.431 Dessert and pudding 0.292 Snack bar†† 0.297 Nut and seed 0.344 vegetable mixed Pastry 0.233 Snack food 0.269 Sweet potato 0.338 Soft drink 0.442 Fatty meat 0.231 Cake 0.262 Soft drink 0.323 Snack food 0.414 Rice 0.221 Potato 0.217 Lamb† 0.249 Confectionary 0.289 Fish† -0.098 Pork and meat chops, steak and 0.215 Legumes†† 0.237 Noodles 0.207 Yoghurt* -0.099 mince† Rice 0.220 Fish† -0.046 Egg†* -0.152 Specialty meat, cereal and 0.220 Savoury sauces and 0.216 Egg†* -0.094 Sandwich* -0.202 vegetable mixed condiments Vegetables -0.228 Yoghurt* -0.080 Cake 0.210 Banana -0.280 Egg†* -0.114 Fish† 0.106 Other fruit -0.321 Mandarin -0.302 Egg†* 0.075 Apple -0.318 Fish† -0.490 -0.051 -0.421 -0.538 Yoghurt* Yoghurt* Bread* 2 a Obtained from factor analysis with extraction method = principle component analysis, rotation method = varimax with Kaiser normalization and rotation converged in 15 iterations; 3 b Percent variance explained for boys: snack food = 5.3%, soft drink = 5.2%, vegetables = 5.2%, fatty meat = 5.1%; girls: vegetables = 4.2%, take away = 4.1%, take away = 3.6%, 4 soup = 3.6%; c Food/food groups refer to Table 1; d Correlation coefficient (r) that represent the magnitude and direction of correlation with food patterns; e includes fish and chips, 5 fish cakes, and fish fingers (crumbed, fried or deep fried); † Natural food sources of n-3 LCPUFA;†Natural sources of n-3 LCPUFA; †† Natural sources of n-3 ALA; * Products 6 available in supermarket contained omega-3 (ALA or n-3 LCPUFA)

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7 Table 3. Characteristics of Australian children within each food pattern group, by gender (age = 9-13 years, n = 1,110) Boys (n = 525) Snack fooda Soft drink Vegetables Pork and meat chops, steak Characteristics of children and mince (n = 121) (n = 156) (n = 135) (n = 113) Mean ± SD Median (IQR) Mean ± SD Median (IQR) Mean ± SD Median (IQR) Mean ± SD Median (IQR) Age (y) 11 ± 1.4 11 (10, 12) 11.2 ± 1.3 11 (10, 12) 11 ± 1.4 11 (10, 12) 11 ± 1.4 11 (10, 12) BMI (kg/m2) 18.8 ± 3.2 17.9 (16.3, 20.3) 19.1 ± 3.6 18.4 (16.5, 21.3) 19.0 ± 3.5 18.4 (16.6, 20.5) 19.5 ± 3.7 18.6 (17.0, 21) Total n-3 LCPUFA intake (mg/d) 120 ± 282 40.8 (17.4, 84.9) 85.4 ± 137 35.1 (13.4, 100.7) 84.2 ± 183 30.8 (12.4, 72.1) 79.3 ± 179 30.9 (17.4, 79.8) Total n-3 LCPUFA intake 12.5 ± 29.5 4.7 (2, 10.2) 8.7 ± 12.2 4.4 (1.4, 10.9) 9.3 ± 19.4 3.5 (1.5, 8.6) 9.0 ± 22.8 3.7 (1.6, 8.2) [mg/d/energy (MJ)] Fish consumptionb (g/d) 13 ± 43 0 (0, 0) 3.7 ± 17.9 0 (0, 0) 14 ± 38 0 (0, 0) 16 ± 60 0 (0, 0) Take aways (meat, fish, cereal and 38 ± 99 0 (0, 0) 135 ± 188 59 (0, 239) 20 ± 68 0 (0, 0) 35 ± 72 0 (0, 40) vegetable mixed) consumption (g/d) Fish eater [n (%)] 27 (22.3) 30 (19.2) 25 (18.5) 17 (15) Girls (n = 585) Vegetables Take aways Tea, coffee, iced coffee drinks Canned meals and soup Characteristics of children (n = 130) (n = 167) (n = 129) (n = 159) Mean ± SD Median (IQR) Mean ± SD Median (IQR) Mean ± SD Median (IQR) Mean ± SD Median (IQR) Age (y) 11 ± 1.5 11 (10, 12) 11 ± 1.5 11 (10, 13) 11 ± 1.4 11 (10, 12) 11 ± 1.3 11 (10, 12) BMI (kg/m2) 19.9 ± 3.7 19.1 (17.1, 22.5) 20.5 ± 4.8 20 (17.1, 22.5) 19.7 ± 3.8 18.8 (16.9, 21.9) 19.8 ± 4.1 18.8 (16.7, 21.7) Total n-3 LCPUFA intake (mg/d) 71.9 ± 161 31.5 (12.1, 61.8) 78.4 ± 175 29.4 (11.3, 72.5) 66.7 ± 114 30 (13.5, 72.9) 96.8 ± 198 40.8 (16.2, 83.3) Total n-3 LCPUFA intake 8.9 ± 17.5 4.1 (1.7, 8.5) 10 ± 20.2 3.7 (1.6, 8.3) 8.2 ± 13 4.1 (1.7, 8.9) 13.5 ± 30 5.4 (2.1, 10.4) [mg/d/energy (MJ)] Fish consumptionb (g/d) 32 ± 92 0 (0, 2.8) 5.4 ± 26 0 (0, 0) 12 ± 48 0 (0, 0) 4.3 ± 19 0 (0, 0) Take aways (meat, fish, cereal and 35 ± 99 0 (0, 0) 116 ± 212 0 (0, 153) 39 ± 100 0 (0, 2.7) 28 ± 76 0 (0, 0) vegetable mixed) consumption (g/d) Fish eater [n (%)] 19 (14.6) 35 (21) 19 (14.7) 32 (20.1) 8 a Obtained from factor analysis with extraction method = principle component analysis, rotation method = varimax with Kaiser normalization and rotation converged in 15 iterations 9 b Fish prepared from home (include canned fish and exclude fish bought from take-away outlets)

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10 Table 4. Relationship between food patternsa and n-3 LCPUFA intake status in Australian 11 children (age = 9-13 years, n = 1,110) - Discriminant analysis (test t of equality of group 12 means) Boys Food patterns Achieve p Achieve p Fish eater p SDTb AIb statusc Snack food 0.999 0.496 1.000 0.653 0.998 0.332 Soft drink 1.000 0.642 1.000 0.616 0.997 0.227 Vegetables 0.999 0.430 0.994 0.067 0.999 0.537 Pork and meat 0.999 0.428 0.997 0.211 0.998 0.335 chops, steak and mince Girls Food patterns Meet SDT p Meet AI p Fish eater p status Vegetables 1.000 0.834 0.999 0.415 1.000 0.678 Take aways 1.000 0.889 0.999 0.399 0.994 0.060 Tea, coffee, iced 0.998 0.292 0.996 0.145 0.997 0.200 coffee drink Canned meals 0.996 0.132 0.998 0.338 1.000 0.697 and soup 13 a Obtained from factor analysis with extraction method = principle component analysis, rotation method = 14 varimax with Kaiser normalization and rotation converged in 15 iterations 15 b Wilks’ Lambda, the proportion of the total variance in the discriminant score not explained by differences 16 among the groups 17

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