Caring for Indigenous Data to Evaluate the Benefits of Indigenous Environmental Programs.

Advances in open data, big data and data linkage allow us to analyse more data and on a larger scale than ever before. However, this brings with it the challenge of ensuring that Indigenous data sets are used in a way that protects Indigenous rights to that data and maximises benefits for Indigenous peoples. The CARE principles for Indigenous data governance-Collective Benefit, Authority to Control, Responsibility and Ethics-were developed to protect Indigenous data sovereignty, but there are few examples of how to translate these principles into practice. In this paper, we show how these CARE principles can be applied to data collection, integration, analysis and translation practices. Our case study is a project that used data reported by Indigenous ranger groups to capture the multiple benefits of Indigenous land and water management activities. Through this case study, we offer a framework for the design and use of CARE-informed data practices, which can be embedded into project design to enable the ethical and responsible use of Indigenous data to improve Indigenous policies and services. Such practices are critical in the context of ongoing demand for Indigenous data for bureaucratic purposes, and Indigenous interest in using that data to influence management and policy decisions affecting their estates and resources.

Vegetable intake in Australian children and adolescents: the importance of consumption frequency, eating occasion and its association with dietary and sociodemographic factors.

OBJECTIVE: We aimed to profile vegetable consumption and its association with dietary and sociodemographic factors. DESIGN: Secondary analysis of a nationally representative nutrition survey. 'Vegetables' refers to non-discretionary 'vegetables and legumes/beans' as defined by the Australian Dietary Guidelines (ADG). Prevalence of vegetable consumption, frequency of intake, proportion meeting ADG recommendations, most popular food groups, intake at each reported eating occasion, and the profile of high and low vegetable consumers (based on the median servings) were determined. SETTING: Australian 2011-2012 National Nutrition and Physical Activity Survey. PARTICIPANTS: Children and adolescents aged 2 to 18 years (n 2812). RESULTS: Vegetables were consumed by 83·0% (95% CI 81·6, 84·4%) of participants, but the median vegetable servings was less than a third of the ADG recommendations. 'Leaf and stalk vegetables' and 'potatoes' were the most popular vegetable-dense food groups at lunch and dinner, respectively. Sixty-four percent had vegetables once a day, and predominantly at dinner. Vegetable frequency was positively associated with daily vegetable servings and variety. Participants who consumed vegetables twice a day generally had vegetables at both lunch and dinner and had nearly double the servings (2·6, sd 1·9) of those who consumed them once (1·5, sd 1·5). High vegetable consumers were older, had higher total energy, but lower discretionary energy intake and were less likely to be at risk of metabolic complications. CONCLUSION: Increasing the frequency of vegetable consumption may assist with increasing daily vegetable servings. A focus on consuming vegetables at lunch may assist with increasing both total servings and variety.

Discretionary intake among Australian adults: prevalence of intake, top food groups, time of consumption and its association with sociodemographic, lifestyle and adiposity measures.

OBJECTIVE: To profile discretionary food and beverage (DF) consumption among Australian adults. DESIGN: Cross-sectional analysis. Dietary and sociodemographic data were used to profile DF intake. Prevalence of DF consumption, DF servings (1 serving=600 kJ), nutrient contribution from DF and top DF food groups by self-reported eating occasions were determined. DF consumers (>0 g) were classified according to quartile of DF intake and general linear models adjusted for age and sex were used to determine associations. SETTING: 2011-12 National Nutrition and Physical Activity Survey (NNPAS).ParticipantsAdults aged ≥19 years (n 9341) who participated in the NNPAS 2011-12. RESULTS: Most adults consumed DF (98 %) and over 60 % exceeded 3 DF servings/d, with a mean of 5·0 (se 0·0) DF servings/d. Cakes, muffins, scones, cake-type desserts contributed the most DF energy (8·4 %) of all food groups, followed by wines (8·1 %), pastries (8·0 %) and beers (6·1 %), with all these food groups consumed in large portions (2·3-3·0 DF servings). Lunch and dinner together contributed 45 % of total DF energy intake. High DF consumers had an average of 10 DF servings, and this group contained more younger adults, males, low socio-economic status, lower usual fruit intake and higher mean waist circumference, but not higher BMI. CONCLUSIONS: A focus on DF consumed in large portions at lunch and dinner may help improve interventions aimed at reducing DF intake and addressing negative adiposity-related measures found in high DF consumers.

Weekday snacking prevalence, frequency, and energy contribution have increased while foods consumed during snacking have shifted among Australian children and adolescents: 1995, 2007 and 2011-12 National Nutrition Surveys.

BACKGROUND: There are limited data on the evolution of eating habits, including snacking, in Australia. This study aimed to understand snacking trends among Australian children over three previous National Nutrition Surveys. METHODS: Data were analysed from a single weekday 24-h recall in the National Nutrition Surveys 1995, 2007, 2011-12 among children 2-16y (n = 8258). A snacking occasion was defined as an eating occasion that occurred between meals based on time of day. RESULTS: The percentage of children snacking increased over time (92.5 ± 0.5(SE) % in 1995, 98.1 ± 0.3% in 2007, and 95.8 ± 0.4% in 2011-12) (P < 0.001), particularly among those having four or more snacking occasions (7.1 ± 0.5% in 1995, 17.9 ± 0.6% in 2007, and 18.5 ± 0.8% in 2011-2) (P < 0.001). The mean number of snacking occasions increased from 2.0 ± 0.0 in 1995, to 2.5 ± 0.0 in 2007 and 2011-12 (P < 0.001). The energy contribution from snacking increased from 24.1 ± 0.3% in 1995 to 27.7 ± 0.3% in 2007 and 30.5 ± 0.4% in 2011-12 (P < 0.001), while the energy from discretionary food during snacking decreased from 56.5 ± 0.7% in 1995 to 47.3 ± 0.5% in 2007 and 47.9 ± 0.7% in 2011-12 (P < 0.001). There were differences in the top foods consumed during snacking: non-alcoholic beverages were prominent contributors in 1995 but not in 2007 or 2011, and pome fruit was the second top energy contributor during snacking in 2007 and 2011 but only fourth in 1995. CONCLUSIONS: Snacking is a prominent dietary pattern that has increased over time in frequency and energy contribution. Foods and beverages consumed during snacking occasions include a mix of core foods and discretionary foods, and while the contribution of discretionary foods has decreased, there is still an opportunity to encourage consumption of more nutrient dense foods during snacking.

Relationship between discretionary food intake and sex, body image, health, and geographical remoteness among Indigenous Australian adolescents.

AIM: Determine the discretionary energy intake of Indigenous Australian adolescents and its relationship with sex, body image, health, and geographical remoteness. METHODS: Cross-sectional data from the 2012 to 2013 National Aboriginal and Torres Strait Islander Nutrition and Physical Activity Survey (n = 264, 15-17 years). Dietary data were collected using an Automated Multiple-Pass Method, anthropometric data by trained interviewers; self-perceived measures of body weight, level of satisfaction with current weight, and self-assessed health were self-reported. General linear models were used to investigate predictors. RESULTS: Discretionary energy intake contributed 35.4% and 54.2% of total energy intake for males and females, respectively, primarily from the sub-groups: soft drinks; pastries; potatoes; sugar, honey and syrups; cordials; and potato snacks. Discretionary energy intake was associated with higher energy intake (p < 0.001) and self-perceived body weight (p = 0.022), while sex had significant interactions with self-assessed health (psex  = 0.005), satisfaction with current weight (psex  < 0.001), and geographical remoteness (psex  = 0.007). Contribution of discretionary energy intake to total energy intake was greatest for males with an increased risk of metabolic complications (50% vs. 37%; p > 0.05), those who perceived themselves to be overweight (56% vs. 27%; p < 0.001), and those who were dissatisfied with their weight (56% vs. 19%; p < 0.001), compared to females. No differences were found by dieting status, risk of metabolic complications, and under-reporting of energy intake. CONCLUSIONS: Discretionary energy intake was excessive among Indigenous Australian adolescents and had relationships with self-perceived health, weight satisfaction, and geographical remoteness, which was moderated by sex. To successfully reduce discretionary food intake among Indigenous Australian adolescents, further research is required to develop sex specific and culturally appropriate strategies.

Patterns of discretionary food intake among Australian children and their association with socio-demographic, lifestyle, and adiposity measures.

AIM: Australian children consume 35% of energy from discretionary food and beverages which increases their risk of non-communicable diseases like type 2 diabetes. Despite this concerning statistic, broad analysis of the profile of discretionary food intake has not been fully undertaken. This study asks: what is the discretionary food and beverage intake profile, contribution to nutrient intakes, and associations with demographic and health characteristics? METHODS: Cross-sectional data from the 2011-12 National Nutrition and Physical Activity Survey (n = 2812, 2-18 years) were used to profile discretionary food consumption. Dietary intake was assessed by 24-h recall. General linear models tested the difference in respondent characteristics by age group, sex, and quartiles of discretionary food energy contribution. RESULTS: Ninety-nine percent of respondents consumed discretionary foods, 74% exceeded the maximum discretionary food recommended serves. Among 10 eating occasions available to select: snack, dinner, lunch and morning tea appeared to contribute 76% of discretionary food energy, with snack and dinner contributing 24% each. Age and frequency of discretionary food consumption were positively associated with energy intake from discretionary foods (p < 0.001); while sex, socio-economic status, physical activity and body composition had no association. High discretionary food consumers chose specific discretionary food items in a large quantity (1.0-3.5-serves/discretionary food) compared to low discretionary food consumers (0.4-1.4-serves/discretionary food). CONCLUSIONS: Nearly all Australian children and adolescents consumed discretionary food daily. No demographic or anthropometric characteristics beyond increasing age were associated with higher discretionary food. Targeted public health policy and community interventions are required to focus on addressing the largest contributors to discretionary food intake in terms of equivalent serve sizes, popularity, and eating occasion.

Balanced carbohydrate ratios are associated with improved diet quality in Australia: A nationally representative cross-sectional study.

BACKGROUND: Carbohydrate quality influences major health outcomes; however, the best criteria to assess carbohydrate quality remain unknown. OBJECTIVE: The objectives were to: i) evaluate whether a diet that meets a carbohydrate ratio (simple, modified or dual ratio) is associated with higher nutrient intakes and diet quality, and ii) model the impact of substituting carbohydrate foods that meet the proposed ratios in place of foods that do not, on nutrient intakes. DESIGN: A secondary analysis of cross-sectional data from the 2011-12 Australian National Nutrition and Physical Activity Survey. PARTICIPANTS/SETTING: National data from participants aged 2 years and older (n = 12,153). MAIN OUTCOME MEASURES: Ratios were defined as (i) simple ratio, 10:1 (10g carbohydrate:≥1g dietary fiber); (ii) modified ratio, 10:1:2 (10g carbohydrate:≥1g dietary fiber:≤2g free sugars); and (iii) dual ratio, 10:1 & 1:2 (10g carbohydrate:≥1g dietary fiber & ≤2g free sugars per 1g dietary fiber). Ratios were compared to nutrient intakes obtained via automated multiple-pass 24-hour dietary recall and diet quality calculated using the Australian Healthy Eating Index. STATISTICAL ANALYSES PERFORMED: Substitution dietary modelling was performed. Data were analyzed using paired and independent sample t-tests. RESULTS: Ratio adherence was highest for simple (50.2% adults; 28.6% children), followed by dual (40.6% adults; 21.7% children), then modified (32.7% adults; 18.6% children) ratios. Participants who met any ratio reported higher nutrient intake and diet quality compared to those who failed to meet the respective ratio (P < .001 for all), with the greatest nutrient intakes found for those who met modified or dual ratios. Dietary modelling improved nutrient intakes for all ratios, with the greatest improvement found for the dual ratio. CONCLUSIONS: All carbohydrate ratios were associated with higher diet quality, with a free sugars constraint in the dual ratio providing the greatest improvements.

Contribution of dietary snacking behaviours to discretionary energy intake and anthropometric measures in Australian adults: A comparison using an objective vs subjective definition for snacking.

AIM: There is limited information on the snacking behaviour of Australian adults, and the role of snacking in the diet may depend on how it is defined. This study aimed to compare the dietary snacking behaviours and associated nutritional intake and body composition in Australian adults, using an objective vs a subjective definition for snacking. METHODS: Cross-sectional data were analysed from the 2011 to 2012 National Nutrition and Physical Activity Survey (n = 8361, 19+ years). Objective snacking was defined based on time of day between main meals. Subjective snacking was self-reported by participants. RESULTS: Using the objective definition, 88.2% of adults were snack consumers; where snacking contributed 20.0% (SD 20.0%) of total daily energy intake and 27.0% (SD 31.4%) of total daily discretionary energy. 41.3% (SD 37.1%) of snacking energy intake came from discretionary foods. Using the subjective definition, 98.5% of adults were snack consumers, where discretionary foods contributed 52.6% (SD 35.2%) of all snacking energy. The proportion of objective and subjective snacking energy from discretionary foods did not differ across body mass index groups. CONCLUSIONS: Objective snacking energy contributed more to core food groups than discretionary, was not associated with anthropometric measures, and contributed less to total discretionary energy than the evening meal. When snacking was defined subjectively, more than half of snacking energy was discretionary, suggesting that adults are more likely to perceive discretionary foods as snacks. Differences between snacking definitions means that associations between self-reported snacking and diet or health outcomes, should be interpreted with caution.

Development and validation of a prognostic 40-day mortality risk model among hospitalized patients with COVID-19.

OBJECTIVES: The development of a prognostic mortality risk model for hospitalized COVID-19 patients may facilitate patient treatment planning, comparisons of therapeutic strategies, and public health preparations. METHODS: We retrospectively reviewed the electronic health records of patients hospitalized within a 13-hospital New Jersey USA network between March 1, 2020 and April 22, 2020 with positive polymerase chain reaction results for SARS-CoV-2, with follow-up through May 29, 2020. With death or hospital discharge by day 40 as the primary endpoint, we used univariate followed by stepwise multivariate proportional hazard models to develop a risk score on one-half the data set, validated on the remainder, and converted the risk score into a patient-level predictive probability of 40-day mortality based on the combined dataset. RESULTS: The study population consisted of 3123 hospitalized COVID-19 patients; median age 63 years; 60% were men; 42% had >3 coexisting conditions. 713 (23%) patients died within 40 days of hospitalization for COVID-19. From 22 potential candidate factors 6 were found to be independent predictors of mortality and were included in the risk score model: age, respiratory rate ≥25/minute upon hospital presentation, oxygenation <94% on hospital presentation, and pre-hospital comorbidities of hypertension, coronary artery disease, or chronic renal disease. The risk score was highly prognostic of mortality in a training set and confirmatory set yielding in the combined dataset a hazard ratio of 1.80 (95% CI, 1.72, 1.87) for one unit increases. Using observed mortality within 20 equally sized bins of risk scores, a predictive model for an individual's 40-day risk of mortality was generated as -14.258 + 13.460*RS + 1.585*(RS-2.524)^2-0.403*(RS-2.524)^3. An online calculator of this 40-day COVID-19 mortality risk score is available at www.HackensackMeridianHealth.org/CovidRS. CONCLUSIONS: A risk score using six variables is able to prognosticate mortality within 40-days of hospitalization for COVID-19. TRIAL REGISTRATION: Clinicaltrials.gov Identifier: NCT04347993.

Breakfast Choice Is Associated with Nutrient, Food Group and Discretionary Intakes in Australian Adults at Both Breakfast and the Rest of the Day.

Breakfast choice is correlated with daily nutrient intakes, but this association may not be solely explained by the breakfast meal. We profiled breakfast consumer groups among Australian adults and compared the role that breakfast versus the rest of the day had on daily intakes of the Five Food Groups, discretionary foods, and nutrients. Breakfast groups were breakfast cereal consumers, non-cereal breakfast consumers, and breakfast skippers. One-day dietary recall data from the 2011⁻2012 National Nutrition and Physical Activity Survey were analysed (n = 9341, ≥19 years), as well as socio-demographic and anthropometric measures. Twelve per cent of adults were breakfast skippers, 41% were breakfast cereal consumers, and 47% were non-cereal breakfast consumers. Females were more likely to have a non-cereal breakfast than males, and the non-cereal breakfast was predominantly bread-based. Breakfast skipping decreased with age (p < 0.001), while breakfast cereal consumption increased with age (p < 0.001). Breakfast skippers were more likely to be male, had a lower socio-economic status, and lower physical activity levels (p < 0.001). Breakfast skippers had the highest mean body mass index (BMI) and waist circumference (p < 0.001), the lowest intake of wholegrain foods, fruits and vegetables, and the highest intake of discretionary foods (p < 0.001). Breakfast cereal consumers had the lowest mean BMI and waist circumference (p < 0.001) and had healthier diets at both breakfast and throughout the rest of the day. They were the most likely to meet the daily recommended serves for grain foods, fruit, dairy, and vegetables, had the highest wholegrain food intake, and the lowest discretionary intake (p < 0.001). Additionally, breakfast cereal consumers had the most favourable daily nutrient intakes, including the lowest added sugars intakes. Differences in daily diet between breakfast groups were attributed to differences in food choices both at breakfast and throughout the rest of the day.

Australian children and adolescents who were drinkers of plain and flavored milk had the highest intakes of milk, total dairy, and calcium.

Dairy is important for children and adolescents' bone development and nutritional needs, and it is not known how the type of milk consumed relates to overall dietary intakes. This study assessed the hypothesis that Australian children and adolescents who drink milk have greater milk, dairy, and micronutrient intakes compared to those who consume milk in other ways or avoid milk. A secondary analysis using day 1 data from the 2011-2012 National Nutrition and Physical Activity Survey was performed (n = 2812, 2-18 years). Children were classified as plain or flavored milk drinkers, other milk drinkers (eg, smoothies, milkshakes), nondrinkers of milk (eg. on cereal), or milk avoiders. Intakes of total dairy, milk, calcium, free sugars, and sugar-sweetened beverages were determined and adjusted for confounders. Milk consumption was popular (81%), and the majority (63%) of milk consumers were drinkers, and of those, 46% were plain, 32% were flavored, and 21% were other milk drinkers. Flavored and plain milk drinkers had higher total daily milk (480, 95% confidence interval [CI] 459-501 and 445, 95% CI 427-462 g, respectively), dairy (2.8, 95% CI 2.7-2.9 and 2.6, 95% CI 2.5-2.7 servings, respectively), and calcium (1049 ±â€¯18 and 980 ±â€¯15 mg, respectively) intakes than all other groups. Plain milk drinkers (10.7%, SD 6.2%) had a lower proportion of daily energy from free sugars than all other groups, but there was no difference in free sugars intake between plain and flavored milk drinkers, and there were no differences in anthropometric measures across groups. Plain and flavored milk drinkers and nondrinkers of milk had the lowest prevalence of sugar-sweetened beverages intake (P < .001). Plain milk drinkers followed by flavored milk drinkers had the most favorable milk, dairy, and nutrient intakes.

Dietary Fibre Intake in Australia. Paper I: Associations with Demographic, Socio-Economic, and Anthropometric Factors.

Dietary fibre is important for regular laxation and reduces chronic disease risk. The National Health and Medical Research Council outlines daily fibre intake targets, yet the proportion of the population that meets these targets is unknown. Using the 2011⁻2012 National Nutrition and Physical Activity Survey, we profiled fibre intake among Australian children and adults. Data from one-day dietary recalls were analysed (n = 12,153, ≥2 years) as well as demographic and anthropometric factors. The median fibre intake was 18.2 g (interquartile range [IQR] 13.2⁻25.0) in children and 20.7 g (IQR 14.3⁻28.7) in adults. We found that 42.3% (95% CI 40.5⁻44.1%) of children and 28.2% (95% CI 27.3⁻29.1%) of adults met the Adequate Intake (AI), and less than 20% of adults met the Suggested Dietary Target (SDT) to reduce the risk of chronic disease. Older children (aged 14⁻18 years), girls, young adults (19⁻30 years), males, and those of lower socio-economic status were less likely to meet the AI (p < 0.001). Those with a higher energy intake were more likely to meet the AI. Anthropometric measures were not associated with fibre intake or the likelihood of meeting the AI. Fibre is a nutrient of concern in Australian diets, with most children and adults falling short of recommendations. Adolescents, girls, young adults, men, and those of lower socio-economic status were less likely to meet the recommendations and may benefit most from public health interventions.

Dietary Fibre Intake in Australia. Paper II: Comparative Examination of Food Sources of Fibre among High and Low Fibre Consumers.

Intakes of dietary fibre in Australia are lower than recommended. An understanding of food choices associated with fibre intake can help to inform locally relevant dietary interventions that aim to increase its consumption. This study aimed to profile the relationship between dietary choices and fibre intake of Australians. Using Day 1 data from the 2011⁻2012 National Nutrition and Physical Activity Survey (n = 12,153, ≥2 years), dietary fibre intake was classified by quartiles for children (2⁻18 years) and adults (≥19 years). Intakes of the Australian Dietary Guidelines (ADG) food groups were calculated, as well as the major, sub-major, and minor food groups from the Australian Food Composition Database. Each of these food groups provide a progressively greater level of detail. Associations with ADG food groups and major food groups were determined, and the leading sub-major and minor food group sources of fibre for low (Quartile 1) and high (Quartile 4) fibre consumers were profiled. Energy-adjusted intakes of wholegrain and/or high fibre but not refined grain (cereal) foods, vegetables, and fruit were positively associated, and discretionary foods negatively associated, with quartile of fibre intake (p < 0.001). The top three sub-major food group sources of fibre were regular breads, cereal mixed dishes, and ready-to-eat breakfast cereals in high fibre consumers and regular breads, cereal mixed dishes, and potatoes in low fibre consumers. White breads was the leading minor food group contributor in low fibre consumers, and apples and lower sugar wheat based breakfast cereal were the leading fibre contributors in high fibre consumers in children and adults, respectively. Higher intakes of wholegrain, fruits, and vegetables, and a lower discretionary intake were associated with higher fibre intake. Encouraging these foods as part of any public health intervention is likely to be effective for increasing dietary fibre intakes.

Breakfast and Breakfast Cereal Choice and Its Impact on Nutrient and Sugar Intakes and Anthropometric Measures among a Nationally Representative Sample of Australian Children and Adolescents.

There is limited evidence in Australia that compares the nutritional impact of a breakfast cereal breakfast to a non-cereal breakfast, and includes the type of cereal. This study investigated the impact of breakfast choice and the total sugar content of breakfast cereal on nutrient intakes and anthropometric measures among Australian children and adolescents. Data from 2 to 18-year-old in the 2011-2012 National Nutrition and Physical Activity Survey were used (n = 2821). Participants were classified as breakfast cereal consumers (minimally pre-sweetened (MPS) or pre-sweetened (PS)), non-cereal breakfast consumers, or breakfast skippers. Foods consumed for breakfast, foods added to the cereal bowl, and the impact of breakfast choice on daily nutrient intakes and anthropometric measures were determined. Although only 9% of children skipped breakfast, 61% of skippers were aged 14-18 years. Among breakfast consumers, 49% had breakfast cereal, and 62% of these exclusively consumed MPS cereal. Breakfast skippers had a higher saturated fat intake than breakfast cereal consumers, and lower intakes of dietary fibre and most micronutrients (p < 0.001). Compared with non-cereal breakfast consumers, breakfast cereal consumers had additional free sugars intake, lower sodium, and higher total sugars, carbohydrate, dietary fibre, and almost all other micronutrients (p < 0.001). The only difference in nutrient intakes between MPS and PS cereal consumers was higher folate among PS consumers. No associations between anthropometric measures and breakfast or breakfast cereal choice were found. The highest prevalence of breakfast skipping was among 14-18-year old. Breakfast cereal consumers had higher intakes of dietary fibre and most micronutrients compared with non-cereal breakfast consumers and skippers, and almost no differences were found between MPS and PS cereal consumers.

Identifying Eating Occasion-Based Opportunities to Improve the Overall Diets of Australian Adolescents.

Adolescents in Australia have a poor dietary intake, leading to large numbers of them being at risk for inadequate intake of micronutrients, and excessive intake of less healthful dietary components. This study examined dietary intakes at multiple eating occasions to identify opportunities for more targeted recommendations and strategies to improve dietary intakes among adolescents. Data from the first 24-h recall of 14-18 years old in the 2011-2012 National Nutrition and Physical Activity Survey were analysed (n = 772). Participant-defined eating occasions were classified as breakfast, lunch, dinner or other eating occasions combined. The mean percent contribution to the total day intake of top shortfall nutrients (calcium, magnesium, vitamin A, iron), discretionary calories, saturated fat, free sugars and sodium, as well as nutrient density, the foods consumed and the percent of consumers at each eating occasion, were calculated. Breakfast had the lowest prevalence of consumers (81%), contributed the least to total daily energy (14.6%) and almost a quarter of daily calcium and iron. Other eating occasions combined contributed 47.5% of free sugars and were top contributors of daily calcium (34.6%) and magnesium (31.7%). Discretionary foods contributed 32.4% of the energy at lunch, and the sodium content at lunch was 415 mg/1000 kJ. Key opportunities identified for adolescents were to increase breakfast consumption, given the high nutrient densities of breakfasts consumed; improve overall lunch quality, particularly the sodium content; promote the intake of milk, fruit and a variety of vegetables at both lunch and dinner; maintain healthful choices at in-between meal eating occasions while focusing on decreasing the intake of discretionary foods.

The Cross-Sectional Association between Consumption of the Recommended Five Food Group "Grain (Cereal)", Dietary Fibre and Anthropometric Measures among Australian Adults.

The Australian Dietary Guidelines recommended "grain (cereal)" core food group includes both refined and whole grain foods, but excludes those that are discretionary (i.e., cakes). We investigated the association between daily serves from the "grain (cereal)" group and its effect on fibre and adiposity. Data from Australian adults in the 2011-2012 National Nutrition and Physical Activity Survey were used (n = 9341). Participants were categorised by serves of core grain foods and general linear models were used to investigate the effect of demographic, socioeconomic, and dietary covariates on waist circumference, body mass index (BMI) and fibre intake. Compared to core grain avoiders (0 serves), high consumers (6+ serves/day) were: more likely male and socially advantaged, had a healthier dietary pattern, less likely dieting, overweight or obese, and were at lower risk of metabolic complications. After adjustment for age, sex and energy intake, there was an inverse relationship between core grain serves intake and BMI (p < 0.001), waist circumference (p = 0.001) and a positive relationship with fibre (p < 0.001). Model adjustments for diet and lifestyle factors resulted in a smaller difference in waist circumference (p = 0.006) and BMI (p = 0.006). Core grain serves was significantly associated with higher fibre, but marginally clinically significant for lower adiposity.