Background: Optimal nutritional choices are linked with better health, but many current interventions to improve diet have limited effect. We tested the hypothesis that providing personalized nutrition (PN) advice based on information on individual diet and lifestyle, phenotype and/or genotype would promote larger, more appropriate, and sustained changes in dietary behaviour. Methods: Adults from seven European countries were recruited to an internet-delivered intervention (Food4Me) and randomized to: (i) conventional dietary advice (control) or to PN advice based on: (ii) individual baseline diet; (iii) individual baseline diet plus phenotype (anthropometry and blood biomarkers); or (iv) individual baseline diet plus phenotype plus genotype (five diet-responsive genetic variants). Outcomes were dietary intake, anthropometry and blood biomarkers measured at baseline and after 3 and 6 months’ intervention. Results: At baseline, mean age of participants was 39.8 years (range 18–79), 59% of participants were female and mean body mass index (BMI) was 25.5 kg/m². From the enrolled participants, 1269 completed the study. Following a 6-month intervention, participants randomized to PN consumed less red meat [-5.48 g, (95% confidence interval:-10.8,-0.09), P = 0.046], salt [-0.65 g, (−1.1,-0.25), P = 0.002] and saturated fat [-1.14 % of energy, (−1.6,-0.67), P < 0.0001], increased folate [29.6 µg, (0.21,59.0), P = 0.048] intake and had higher Healthy Eating Index scores [1.27, (0.30, 2.25), P = 0.010) than those randomized to the control arm. There was no evidence that including phenotypic and phenotypic plus genotypic information enhanced the effectiveness of the PN advice. Conclusions: Among European adults, PN advice via internet-delivered intervention produced larger and more appropriate changes in dietary behaviour than a conventional approach.

Meal skipping rates may be highest during young adulthood, a period of transition and development. Although these dietary behaviours may increase future risk of chronic disease, limited research has investigated correlates of meal skipping in young adults. A systematic literature search was conducted to identify studies that investigated correlates of meal skipping behaviours in young adults (aged 18–30 years). EBSCO host, MEDLINE Complete, Global Health, Scopus, EMBASE, Web of Science and Informit platforms were searched for eligible articles. Correlates were defined as any factor that was either associated with meal skipping or was self-reported by the participant to have an influence on meal skipping. Randomised controlled trials, prospective cohort studies, case-control studies, nested case-control studies, cross-sectional studies, and longitudinal studies were eligible for inclusion. Three-hundred and thirty-one articles were identified, 141 full-text articles assessed for eligibility, resulting in 35 included studies. Multiple methodological and reporting weaknesses were apparent in the reviewed studies with 28 of the 35 studies scoring a negative rating in the risk of bias assessment. Meal skipping (any meal), defined as the skipping of any meal throughout the day, was reported in 12 studies with prevalence ranging between 5 and 83%. The remaining 25 studies identified specific meals and their skipping rates, with breakfast the most frequently skipped meal 14–88% compared to lunch 8–57% and dinner 4–57%. Lack of time was consistently reported as an important correlate of meal skipping, compared with correlates such as cost and weight control, while sex was the most commonly reported associated correlate. Breakfast skipping was more common among men while lunch or dinner skipping being more common among women. This review is the first to examine potential correlates of meal skipping in young adults. Future research would benefit from stronger design and reporting strategies, using a standardised approach for measuring and defining meal skipping.

Improving lifestyle behaviours has considerable potential for reducing the global burden of non-communicable diseases, promoting better health across the life-course and increasing well-being. However, realising this potential will require the development, testing and implementation of much more effective behaviour change interventions than are used conventionally. Therefore, the aim of this study was to conduct a multi-centre, web-based, proof-of-principle study of personalised nutrition (PN) to determine whether providing more personalised dietary advice leads to greater improvements in eating patterns and health outcomes compared to conventional population-based advice. A total of 5,562 volunteers were screened across seven European countries; the first 1,607 participants who fulfilled the inclusion criteria were recruited into the trial. Participants were randomly assigned to one of the following intervention groups for a 6-month period: Level 0—control group—receiving conventional, non-PN advice; Level 1—receiving PN advice based on dietary intake data alone; Level 2—receiving PN advice based on dietary intake and phenotypic data; and Level 3—receiving PN advice based on dietary intake, phenotypic and genotypic data. A total of 1,607 participants had a mean age of 39.8 years (ranging from 18 to 79 years). Of these participants, 60.9 % were women and 96.7 % were from white-European background. The mean BMI for all randomised participants was 25.5 kg m⁻², and 44.8 % of the participants had a BMI ≥ 25.0 kg m⁻². Food4Me is the first large multi-centre RCT of web-based PN. The main outcomes from the Food4Me study will be submitted for publication during 2015.

Personalized nutrition (PN) behavior-change interventions are being used increasingly in attempts to improve dietary intake; however, the impact of PN advice on improvements in dietary intake has not been reviewed systematically. The aim of this systematic review was to evaluate the effect of PN advice on changes in dietary intake compared with generalized advice in healthy adults. Three databases (EMBASE, PubMed, and CINAHL) were searched between 2009 and 2020 for randomized controlled trials (RCTs) that tested the effect of PN and tailored advice based on diet, phenotype, or genetic information. The Evidence Analysis Library Quality Criteria checklist was used to conduct a risk-of-bias assessment. Information on intervention design and changes in nutrients, foods, and dietary patterns was extracted from the 11 studies meeting the inclusion criteria. Studies were conducted in the United States, Canada, or Europe; reported outcomes on 57 to 1488 participants; and varied in follow-up duration from 1 to 12 mo. Five studies incorporated behavior-change techniques. The risk of bias for included studies was low. Overall, the available evidence suggests that dietary intake is improved to a greater extent in participants randomly assigned to receive PN advice compared with generalized dietary advice. Additional well-designed PN RCTs are needed that incorporate behavior-change techniques, a broader range of dietary outcomes, and comparisons between personalization based on dietary, biological, and/or lifestyle information.

Objective: To examine how socio-demographic characteristics and diet quality vary with consumption of ultra-processed foods (UPF) in a cross-sectional nationally representative survey of Australian adults.Design: Using a 24-h recall, this cross-sectional analysis of dietary and socio-demographic data classified food items using the NOVA system, estimated the percentage of total energy contributed by UPFs and assessed diet quality using the Dietary Guideline Index (DGI–2013 total and components). Linear regression models examined associations between socio-demographic characteristics and diet quality with percentage of energy from UPF.Setting: Australian Health Survey 2011–2013.Participants: Australian adults aged ≥ 19 years (n 8209).Results: Consumption of UPF was higher among younger adults (aged 19–30 years), adults born in Australia, those experiencing greatest area-level disadvantage, lower levels of education and the second lowest household income quintile. No significant association was found for sex or rurality. A higher percentage of energy from UPF was inversely associated with diet quality and with lower DGI scores related to the variety of nutritious foods, fruits, vegetables, total cereals, meat and poultry, fish, eggs, nuts and seeds, legumes/beans, water and limits on discretionary foods, saturated fat and added sugar.Conclusions: This research adds to the evidence on dietary inequalities across Australia and how UPF are detrimental to diet quality. The findings can be used to inform interventions to reduce UPF consumption and improve diet quality.