This study examined differences in food groups consumed at eating occasions by the level of adherence to dietary guidelines in Australian adults (≤19 years) and whether consumption differed with respect to age, sex and education levels. Secondary analysis of the 2011–2012 National Nutrition and Physical Activity Survey (n 9054) was performed, using one 24-h dietary recall with self-reported eating occasions. Dietary Guideline Index scores were used to assess adherence to the 2013 Australian Dietary Guidelines. Mean differences (95 % CI) in servings of the five food groups and discretionary foods at eating occasions were estimated for adults with higher and lower diet quality, stratified by sex, age group and education. Using survey-based t-tests, differences of at least half a serving with P values < 0·05 were considered meaningful. Compared with adults with lower diet quality, women and men aged 19–50 years with higher diet quality consumed more serves of vegetables at dinner (mean difference (95 % CI), women; 1·0; 95 % CI (0·7, 1·2); men: 0·9; 95 % CI (0·6, 1·3)) and fewer serves of discretionary foods at snacks (women: −0·7; 95 % CI (−0·9, −0·5); men: −1·0; 95 % CI (−1·4, −0·7). Other food groups, such as grains, dairy products and alternatives, meats and alternatives, were not significantly different between adults with lower and higher diet quality, across any eating occasions and age groups. Discretionary food intake at lunch, dinner and snacks was consistently greater among adults with lower diet quality, regardless of education level. Our findings identify dinner and snacks as opportunities to increase vegetable intake and reduce discretionary food intake, respectively.

Dietary behaviours in early childhood are understudied despite links with later health. Assessing the distribution of key food groups across the day could identify opportunities for improvements. This study aimed to describe the 24-hourly distribution of dietary intakes and frequency of eating occasions for weekdays and weekend days among children aged 18 months and assess associations of eating frequency with vegetable, fruit and discretionary intakes and zBMI. Using two parent-reported 24-h recalls of child dietary intakes from the Melbourne Infant Feeding Activity and Nutrition Trial (InFANT) Program, mean frequency of daily eating occasions and hourly intake distributions were calculated for vegetables, fruits, discretionary foods, and total foods and energy-containing beverages on weekdays (n 428) and weekend days (n 376). Multivariable regression analyses assessed associations between frequency of eating occasions, total intake of food groups and zBMI. Overall, children had 7·8 ± 1·8 (mean ± sd) eating occasions/d on weekdays, where 1·5 ± 0·8 contained vegetables, 2·2 ± 1·1 contained fruit and 2·5 ± 1·5 contained discretionary foods. Weekend day intakes were similar. Energy intakes were highest at dinner time. Intakes of total foods, fruits and discretionary foods were spread across the day (06.00–22.00). Vegetable consumption was mainly about 18.00 with minimal intake at other times. Eating frequency was associated with amount of food consumed but not consistently with zBMI. These 18-month-old children ate frequently throughout the day, with little distinction between weekdays and weekend days. Most eating occasions lacked vegetables, and frequency of discretionary foods was higher than of vegetables. Promoting vegetable consumption at occasions other than dinner could improve vegetable intake.

Changes between diet quality and health-related quality of life (HR-QoL) over 12 years were examined in men and women, in 2844 adults (46 % males; mean age 47·3 (sd 9·7) years) from the Australian Diabetes, Obesity and Lifestyle study with data at baseline, 5 and 12 years. Dietary intake was assessed with a seventy-four-item FFQ. Diet quality was estimated with the Dietary Guideline Index, Mediterranean-Dietary Approaches to Stop Hypertension Diet Intervention for Neurological Delay Index (MIND) and Dietary Inflammatory Index. HR-QoL in terms of global, physical component summary (PCS) and mental component summary (MCS) was assessed with the Short-Form Health Survey-36. Fixed effects regression models adjusted for confounders were performed. Mean MCS increased from baseline (49·0, sd 9·3) to year 12 (50·7, sd 9·1), whereas mean PCS decreased from baseline (51·7, sd 7·4) to year 12 (49·5, sd 8·6). For the total sample, an improvement in MIND was associated with an improvement in global QoL (β = 0·28, 95 % CI (0·007, 0·55)). In men, an improvement in MIND was associated with an improvement in global QoL (β = 0·28, 95 % CI (0·0004, 0·55)). In women, improvement in MIND was associated with improvements in global QoL (β = 0·62 95 % CI (0·38, 0·85)), MCS (β = 0·75, 95 % CI (0·29, 1·22)) and PCS (β = 0·75, 95 % CI (0·29, 1·22)). Positive changes in diet quality were associated with broad improvements in HR-QoL, and most benefits were observed in women when compared to men. These findings support the need for strategies to assist the population in consuming healthy dietary patterns to lead to improvements in HR-QoL.

The impact of change in socio-economic status (SES) from childhood to adulthood (SES mobility) on adult diet is not well understood. This study examined associations between three SES mobility variables (area disadvantage, education, occupation) and adult diet quality. 1482 Australian participants reported childhood area-level SES in 1985 (aged 10–15 years) and retrospectively reported highest parental education and main occupation (until participant age 12) and own area-level SES, education, occupation and dietary intake in 2004–2006 (aged 26–36 years). A Dietary Guidelines Index (DGI) was calculated from food frequency and habit questionnaires. A higher score (range 0–100) indicated better diet quality. Sex-stratified linear regression models adjusted for confounders. Area-level SES mobility was not associated with diet quality. Compared with stable high (university) education, stable low (school only) was associated with lower DGI scores (males: β = –5·5, 95 % CI: −8·9, –2·1; females: β = –6·3, 95 % CI: −9·3, –3·4), as was downward educational mobility (participant’s education lower than their parents) (males: β = –5·3, 95 % CI: −8·5, –2·0; females: β = –4·5, 95 % CI: −7·2, –1·7) and stable intermediate (vocational) education among males (β = –3·9, 95 % CI: −7·0, −0·7). Compared with stable high (professional/managerial) occupation, stable low (manual/out of workforce) males (β = –4·9, 95 % CI: −7·6, –2·2), and participants with downward occupation mobility (males: β = –3·2, 95 % CI: −5·3, –1·1; females: β = –2·8, 95 % CI: −4·8, –0·8) had lower DGI scores. In this cohort, intergenerational low education and occupation, and downward educational and occupational mobility, were associated with poor adult diet quality.

Dietary guidelines should be underpinned by the best available evidence on relationships between diet and health, including evidence from nutrient-based, food-based and dietary patterns research. The primary aim of the present study was to analyse the systematic reviews conducted to inform the 2013 Australian Dietary Guidelines according to dietary exposure. The secondary aim was to analyse the reviews by health outcome, and design of included studies. To identify the systematic reviews, the dietary guidelines report was used as a starting point and relevant references were retrieved. The evidence report contained the data used in this analysis. Descriptive statistics were used to analyse reviews according to exposure, outcome, and design of included studies. A total of 143 systematic reviews were included in this analysis. Foods were the most common exposure (86·7 % of reviews), followed by nutrients (10·5 %) and dietary patterns (2·8 %). Chronic disease morbidity and/or mortality was the most common outcome (80·4 %), followed by chronic disease risk factors (19·6 %). Most reviews included evidence from cohort or nested case–control studies (92·3 %), many included evidence from case–control studies (61·5 %) and some included evidence from randomised controlled trials (28·7 %). These results reflect the research questions that were asked, the systematic review methods that were used, and the evidence that was available. In developing future iterations of the Australian Dietary Guidelines, there is an opportunity to review the latest evidence from dietary patterns research.

Studies have examined the association between depressive symptoms and dietary patterns; however, only few studies focused on older adults. The present study examines the association between current and past dietary patterns and depression in a community-dwelling adult population aged 55 years and over. Adults (n 4082) were recruited into the Wellbeing, Eating and Exercise for a Long Life study in Victoria, Australia. In 2010 and 2014, data were collected using self-administered questionnaires including a 111-item FFQ, the RAND thirty-six-item Short Form Health Survey of health-related quality of life and the International Physical Activity Questionnaire. Depressive symptoms were assessed using the Geriatric Depression Scale in 2014. Current (2014) and past (2010) dietary patterns were determined using principal component analysis. Association between dietary patterns and depressive symptoms was assessed using a mixed model analysis with adjustment for covariates. Two similar dietary patterns were identified in men and women (n 2142). In women, a healthy dietary pattern (characterised by frequent intake of vegetables, fruits and fish) was associated with lower levels of depressive symptoms (current diet: β = −0·260, 95 % CI −0·451, −0·070; past diet: β = −0·201, 95 % CI −0·390, −0·013). A current unhealthy dietary pattern in women (characterised by frequent intake of red and processed meat, potatoes, hot chips, cakes, deserts and ice cream) was associated with higher levels of depressive symptoms (β = 1·367, 95 % CI 0·679, 2·056). No associations were identified in men. Further research is needed to confirm these findings and to understand the differences that may occur by sex.

This study aims to examine repeatability of reduced rank regression (RRR) methods in calculating dietary patterns (DP) and cross-sectional associations with overweight (OW)/obesity across European and Australian samples of adolescents. Data from two cross-sectional surveys in Europe (2006/2007 Healthy Lifestyle in Europe by Nutrition in Adolescence study, including 1954 adolescents, 12–17 years) and Australia (2007 National Children’s Nutrition and Physical Activity Survey, including 1498 adolescents, 12–16 years) were used. Dietary intake was measured using two non-consecutive, 24-h recalls. RRR was used to identify DP using dietary energy density, fibre density and percentage of energy intake from fat as the intermediate variables. Associations between DP scores and body mass/fat were examined using multivariable linear and logistic regression as appropriate, stratified by sex. The first DP extracted (labelled ‘energy dense, high fat, low fibre’) explained 47 and 31 % of the response variation in Australian and European adolescents, respectively. It was similar for European and Australian adolescents and characterised by higher consumption of biscuits/cakes, chocolate/confectionery, crisps/savoury snacks, sugar-sweetened beverages, and lower consumption of yogurt, high-fibre bread, vegetables and fresh fruit. DP scores were inversely associated with BMI z-scores in Australian adolescent boys and borderline inverse in European adolescent boys (so as with %BF). Similarly, a lower likelihood for OW in boys was observed with higher DP scores in both surveys. No such relationships were observed in adolescent girls. In conclusion, the DP identified in this cross-country study was comparable for European and Australian adolescents, demonstrating robustness of the RRR method in calculating DP among populations. However, longitudinal designs are more relevant when studying diet–obesity associations, to prevent reverse causality.

Evidence linking dietary patterns (DP) and obesity and hypertension prevalence is inconsistent. We aimed to identify DP derived from energy density, fibre and sugar intakes, as well as Na, K, fibre, SFA and PUFA, and investigate associations with obesity and hypertension. Adults (n 4908) were included from the cross-sectional Australian Health Survey 2011–2013. Two 24-h dietary recalls estimated food and nutrient intakes. Reduced rank regression derived DP with dietary energy density (DED), fibre density and total sugar intake as response variables for obesity and Na:K, SFA:PUFA and fibre density as variables for hypertension. Poisson regression investigated relationships between DP and prevalence ratios (PR) of overweight/obesity (BMI≥25 kg/m2) and hypertension (blood pressure≥140/90 mmHg). Obesity-DP1 was positively correlated with fibre density and sugars and inversely with DED. Obesity-DP2 was positively correlated with sugars and inversely with fibre density. Individuals in the highest tertile of Obesity-DP1 and Obesity-DP2, compared with the lowest, had lower (PR 0·88; 95 % CI 0·81, 0·95) and higher (PR 1·09; 95 % CI 1·01, 1·18) prevalence of obesity, respectively. Na:K and SFA:PUFA were positively correlated with Hypertension-DP1 and inversely correlated with Hypertension-DP2, respectively. There was a trend towards higher hypertension prevalence in the highest tertile of Hypertension-DP1 compared with the lowest (PR 1·18; 95 % CI 0·99, 1·41). Hypertension-DP2 was not associated with hypertension. Obesity prevalence was inversely associated with low-DED, high-fibre and high-sugar (natural sugars) diets and positively associated with low-fibre and high-sugar (added sugars) diets. Hypertension prevalence was higher on low-fibre and high-Na and SFA diets.

Fe deficiency remains the most common nutritional deficiency worldwide and young children are at particular risk. Preventative food-based strategies require knowledge of current intakes, sources of Fe, and factors associated with low Fe intakes; yet few data are available for Australian children under 2 years. This study’s objectives were to determine intakes and food sources of Fe for Australian infants and toddlers and identify non-dietary factors associated with Fe intake. Dietary, anthropometric and socio-demographic data from the Melbourne Infant Feeding, Activity and Nutrition Trial Program were analysed for 485 infants (mean age: 9·1 (sd 1·2) months) and 423 toddlers (mean age: 19·6 (sd 2·6) months) and their mothers. Dietary intakes were assessed via 24-h recalls over 3 non-consecutive days. Prevalence of inadequate Fe intake was estimated using the full probability approach. Associations between potential non-dietary predictors (sex, breast-feeding status, age when introduced to solid foods, maternal age, maternal education, maternal employment status and mother’s country of birth) and Fe intakes were assessed using linear regression. Mean Fe intakes were 9·1 (sd 4·3) mg/d for infants and 6·6 (sd 2·4) mg/d for toddlers. Our results showed that 32·6 % of infants and 18·6 % of toddlers had inadequate Fe intake. Main food sources of Fe were Fe-fortified infant formula and cereals for infants and toddlers, respectively. Female sex and current breast-feeding were negatively associated with infant Fe intakes. Introduction to solid foods at or later than 6 months was negatively associated with Fe intake in toddlers. These data may facilitate food-based interventions to improve Australian children’s Fe intake levels.

Traditionally, nutrition research has focused on individual nutrients, and more recently dietary patterns. However, there has been relatively little focus on dietary intake at the level of a ‘meal’. The purpose of the present paper was to review the literature on adults' meal patterns, including how meal patterns have previously been defined and their associations with nutrient intakes and diet quality. For this narrative literature review, a comprehensive search of electronic databases was undertaken to identify studies in adults aged ≥  19 years that have investigated meal patterns and their association with nutrient intakes and/or diet quality. To date, different approaches have been used to define meals with little investigation of how these definitions influence the characterisation of meal patterns. This review identified thirty-four and fourteen studies that have examined associations between adults' meals patterns, nutrient intakes and diet quality, respectively. Most studies defined meals using a participant-identified approach, but varied in the additional criteria used to determine individual meals, snacks and/or eating occasions. Studies also varied in the types of meal patterns, nutrients and diet quality indicators examined. The most consistent finding was an inverse association between skipping breakfast and diet quality. No consistent association was found for other meal patterns, and little research has examined how meal timing is associated with diet quality. In conclusion, an understanding of the influence of different meal definitions on the characterisation of meal patterns will facilitate the interpretation of the existing literature, and may provide guidance on the most appropriate definitions to use.

Findings from research that has assessed the influence of dietary factors on child obesity have been equivocal. In the present study, we aimed to test the hypothesis that a positive change in diet quality is associated with favourable changes in BMI z-scores (zBMI) in schoolchildren from low socio-economic backgrounds and to examine whether this effect is modified by BMI category at baseline. The present study utilised data from a subsample (n 216) of the Resilience for Eating and Activity Despite Inequality study, a longitudinal cohort study with data collected in 2007–8 (T1) and 2010–11 (T2) in socio-economically disadvantaged women and children (5–12 years at T1). Dietary data were collected using a FFQ and diet quality index (DQI) scores derived at both time points. The objective measures of weight, height and physical activity (accelerometers) were included. The other variables were reported in the questionnaires. We examined the association between change in DQI and change in zBMI, using linear regression analyses adjusted for physical activity, screen sedentary behaviour and maternal education level both in the whole sample and in the sample stratified by overweight status at baseline. After accounting for potential covariates, change in diet quality was found to be inversely associated with change in zBMI only in children who were overweight at baseline (P= 0·035), thus supporting the hypothesis that improvement in diet quality is associated with a concurrent improvement in zBMI among already overweight children, but not among those with a normal BMI status. The identification of modifiable behaviours such as diet quality that affect zBMI longitudinally is valuable to inform future weight gain prevention interventions in vulnerable groups.