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Indigenous peoples and ethnic minority groups often experience poor diet quality and poor health outcomes. Such inequities may be partially due to nutrition interventions not meeting the unique cultural and linguistic needs of these population groups, which could be achieved using co-creation and/or personalised approaches. Cultural adaptation or tailoring of nutrition interventions has shown promise in improving some aspects of dietary intake, but this requires careful consideration to ensure it does not inadvertently exacerbate dietary inequities. The aim of this narrative review was to examine examples of cultural adaptations and/or tailoring of public health nutrition interventions that improved the dietary intake and to consider implications for the optimal design and implementation of personalised and precision nutrition interventions. This review identified six examples of cultural adaptation and/or tailoring of public health nutrition intervention in Indigenous peoples and ethnic minority groups across Australia, Canada and the US. All studies used deep socio-cultural adaptations, such as the use of Indigenous storytelling, and many included surface-level adaptations, such as the use of culturally appropriate imagery in intervention materials. However, it was not possible to attribute any improvements in dietary intake to cultural adaptation and/or tailoring per se , and the minimal reporting on the nature of adaptations limited our ability to determine whether the interventions used true co-creation to design content or were adapted from existing interventions. Findings from this review outline opportunities for personalised nutrition interventions to use co-creation practices to design, deliver and implement interventions in collaboration with Indigenous and ethnic minority groups. [ABSTRACT FROM AUTHOR]

Objective: Web-based dietary interventions could support healthy eating. The Advice, Ideas and Motivation for My Eating (Aim4Me) trial investigated the impact of three levels of personalised web-based dietary feedback on diet quality in young adults. Secondary aims were to investigate participant retention, engagement and satisfaction. Design: Randomised controlled trial. Setting: Web-based intervention for young adults living in Australia. Participants: 18–24-year-olds recruited across Australia were randomised to Group 1 (control: brief diet quality feedback), Group 2 (comprehensive feedback on nutritional adequacy + website nutrition resources) or Group 3 (30-min dietitian consultation + Group 2 elements). Australian Recommended Food Score (ARFS) was the primary outcome. The ARFS subscales and percentage energy from nutrient-rich foods (secondary outcomes) were analysed at 3, 6 and 12 months using generalised linear mixed models. Engagement was measured with usage statistics and satisfaction with a process evaluation questionnaire. Results: Participants (n 1005, 85 % female, mean age 21·7 ± 2·0 years) were randomised to Group 1 (n 343), Group 2 (n 325) and Group 3 (n 337). Overall, 32 (3 %), 88 (9 %) and 141 (14 %) participants were retained at 3, 6 and 12 months, respectively. Only fifty-two participants (15 % of Group 3) completed the dietitian consultation. No significant group-by-time interactions were observed (P > 0·05). The proportion of participants who visited the thirteen website pages ranged from 0·6 % to 75 %. Half (Group 2 = 53 %, Group 3 = 52 %) of participants who completed the process evaluation (Group 2, n 111; Group 3, n 90) were satisfied with the programme. Conclusion: Recruiting and retaining young adults in web-based dietary interventions are challenging. Future research should consider ways to optimise these interventions, including co-design methods. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to identify correlates of nutrition label awareness and use, particularly subgroup differences among consumers. Two label types were assessed: (1) nutrition facts tables (NFt) in Australia, Canada, Mexico, UK, and USA and (2) front-of-package (FOP) labels, including mandatory Guideline Daily Amounts (Mexico), voluntary Health Star Ratings (Australia) and voluntary Traffic Lights (UK).Design: Respondents were recruited using Nielsen Consumer Insights Global Panel (n 21 586) and completed online surveys in November-December 2018. Linear regression and generalised linear mixed models examined differences in label use and awareness between countries and label type based on sociodemographic, knowledge-related and dietary characteristics.Setting: Australia, Canada, Mexico, UK and USA.Participants: Adults (≥18 years).Results: Respondents from the USA, Canada and Australia reported significantly higher NFt use and awareness than those in Mexico and the UK. Mexican respondents reported the highest level of FOP label awareness, whereas UK respondents reported the highest FOP label use. NFt use was higher among females, 'minority' ethnic groups, those with higher nutrition knowledge and respondents with 'adequate literacy' compared with those with 'high likelihood of limited literacy'. FOP label use was higher among those with a 'high likelihood of limited literacy' compared with 'adequate literacy' across countries.Conclusions: Lower use of mandatory Guideline Daily Amount labels compared with voluntary FOP labelling systems provides support for Mexico's decision to switch to mandatory 'high-in' warning symbols. The patterns of consumer label use and awareness across sociodemographic and knowledge-related characteristics suggest that simple FOP labels may encourage broader use across countries. [ABSTRACT FROM AUTHOR]

Machine learning methods can help establish evidence-based dietary guidelines One approach to address the difficulties faced when converting complex dietary patterns into quantitative, interpretable summaries is unsupervised learning, which uses machine learning algorithms to identify clusters of individuals that share unique dietary patterns. The Dietary Guidelines for Americans provide dietary advice for health promotion and disease prevention and serve as the cornerstone to all other US Federal nutrition policy and education efforts(1). One machine learning approach to avoid this bias by accounting for heterogeneity is called stacked generalisation(35), which combines several machine learning algorithms into one. [Extracted from the article]