Dr Erin Clarke

Diet quality is influenced by demographics and can change over time. This study aimed to (1) compare diet quality among adolescents/adults who completed the online Healthy Eating Quiz (HEQ) by demographic characteristics, and (2) to evaluate change in score over time for repeat completers. HEQ data collected between July 2016 and May 2022 were analysed, including demographics (age, gender, vegetarian status, socio-economic status, number of people main meals are shared with, country), and diet quality calculated using the Australian Recommended Food Score (ARFS) (range 0¿73) for respondents aged = 16 years. Differences in ARFS by demographic characteristics and change in score over time, adjusted for age, gender and vegetarian status, were tested by linear regression. The participants (n = 176,075) were predominantly female (70.4%), Australian (62.8%), and aged 18¿24 years (27.7%), with 4.0% (n = 7087) repeat completers. Mean ± SD ARFS was 33.9 ± 9.4/73. Results indicate that ARFS was significantly lower among males and significantly higher with increasing age group, higher socio-economic status, in vegetarians, those who shared main meals with others, and those living in Australia (p-values < 0.001). Mean change in ARFS over time (2.3 ± 6.9) was significantly higher for those with lower baseline scores (p < 0.001). Publicly available, brief dietary assessment tools have the potential to improve diet quality at the population level.

Objective: To investigate associations between eating behavior constructs (social eating, perceived competence, habit automaticity, self-determined motivation) and diet quality among young adults. Design: Cross-sectional analysis. Participants: Young adults (n = 1,005; mean age, 21.7 ± 2.0 years; 85% female) enrolled in the Advice, Ideas, and Motivation for My Eating (Aim4Me) study. Main outcome measures: Four eating behavior measures collected via online surveys: Social Eating Scale, Perceived Competence in Healthy Eating Scale, Self-Report Behavioral Automaticity Index, and Regulation of Eating Behaviors scales. Diet quality was assessed using the Australian Recommended Food Score (ARFS) and percentage energy from energy-dense, nutrient-poor (EDNP) foods. Analysis: Multivariate linear regression investigating associations between eating behavior measures (independent variables) and ARFS and EDNP foods (dependent variables), adjusting for sociodemographic and lifestyle confounders. Results: Greater perceived competence in healthy eating and behavioral automaticity for consuming healthy foods, limiting EDNP food intake, and higher intrinsic motivation, integrated regulation, and identified regulation of eating behaviors were associated with higher ARFS and lower percentage energy EDNP foods (P < 0.001). Greater self-reported social influence on eating behaviors was associated with higher ARFS (P = 0.01). Higher amotivation was associated with greater % energy from EDNP foods (P < 0.001). Conclusions and Implications: Perceived competence, habit automaticity, and self-determined motivation are determinants of diet quality in young adults. These findings support the development of interventions that promote healthy eating habits by focusing on eating behavior constructs and evaluating their use in improving diet quality.

Aim: To investigate associations between changes in vegetable and fruit (V&F) intakes and anthropometric indices (weight, BMI, % body fat, waist circumference), including differences by sex, during a dietary weight-loss intervention. Methods: Adults (18-45 years) with overweight/obesity (BMI 25-35 kg/m2) entered a 10-week pre-post study, receiving individualised consults with an Accredited Practising Dietitian targeting increased V&F intakes. Dietary intake was assessed using 24-hour recalls and food frequency questionnaires. Linear mixed models were used to examine how much of the changes in anthropometric indices were explained by changes in V&F intakes. Sex differences were assessed by Wilcoxon rank sum tests. Results: Of the 43 participants enrolled, 34 completed the study (53% female). Significant differences in energy intake and anthropometric indices were observed between males and females at baseline. After 10 weeks, females significantly reduced their weight (-2.9%, P <.01), BMI (-0.82 kg/m2, P <.01), waist circumference (-1.70 cm, P <.01), energy intake (-824 kJ/day, P =.01) and improved diet quality (-14.0% energy-dense, nutrient-poor foods, P <.01). Males significantly reduced weight (-2.5%, P =.04), BMI (-0.76 kg/m2, P =.03), waist circumference (-2.40 cm, P =.02), energy intake (-2875 kJ/day, P <.01), increased fruit intake (+0.89 serves/day, P =.02) and improved diet quality (-6% energy-dense, nutrient-poor foods, P <.01). Compared to the other sex, greater reductions were observed in energy intake in males and energy-dense, nutrient-poor foods in females. Linear mixed models identified that changes in V&F intakes did not explain the variation in anthropometric measures. Conclusion: Future interventions may benefit from trialling sex tailored messages to enhance effects on anthropometric changes.

Dietary intakes are commonly assessed by established methods including food frequency questionnaires, food records, or recalls. These self-report methods have limitations impacting validity and reliability. Dietary biomarkers provide objective verification of self-reported food intakes, and represent a rapidly evolving area. This review aims to summarize the urinary biomarkers of individual foods, food groups, dietary patterns, or nutritional supplements that have been evaluated to date. Six electronic databases were searched. Included studies involved healthy populations, were published from 2000, and compared measured dietary intake with urinary markers. The initial search identified 9985 studies; of these, 616 full texts were retrieved and 109 full texts were included. Of the included studies, 67 foods and food components were studied, and 347 unique urinary biomarkers were identified. The most reliable biomarkers identified were whole grains (alkylresorcinols), soy (isoflavones), and sugar (sucrose and fructose). While numerous novel urinary biomarkers have been identified, further validation studies are warranted to verify the accuracy of self-reported intakes and utility within practice.

Background: This systematic review aimed to evaluate the impact of nutrition interventions on participant reported pain severity and intensity in populations with chronic pain. Methods: Eight databases were systematically searched for studies that included adult populations with a chronic pain condition, a nutrition intervention and a measure of pain. Where possible, data were pooled using meta-analysis. Seventy-one studies were included, with 23 being eligible for meta-analysis. Results: Studies were categorised into four groups: (i) altered overall diet with 12 of 16 studies finding a significant reduction in participant reported pain; (ii) altered specific nutrients with two of five studies reporting a significant reduction in participant reported pain; (iii) supplement-based interventions with 11 of 46 studies showing a significant reduction in pain; and (iv) fasting therapy with one of four studies reporting a significant reduction in pain. The meta-analysis found that, overall, nutrition interventions had a significant effect on pain reduction with studies testing an altered overall diet or just one nutrient having the greatest effect. Conclusions: This review highlights the importance and effectiveness of nutrition interventions for people who experience chronic pain.