Inadequate dietary intakes are a key modifiable risk factor to reduce the risk of developing non-communicable diseases. To encourage healthy eating and behaviour change, innovative public health interventions are required. Social marketing, in particular segmentation, can be used to understand and target specific population groups. However, segmentation often uses demographic factors, ignoring the reasons behind why people behave the way they do. This review aims to explore the food and nutrition related research that has utilised psycho-behavioural segmentation. Six databases from were searched in June 2020. Inclusion criteria were: published 2010 onwards, segmentation by psycho-behavioural variables, outcome related to food or nutrition, and healthy adult population over 18 years. 30 studies were included; most were quantitative (n = 28) and all studies used post-hoc segmentation methods, with the tools used to segment the population varying. None of the segments generated were targeted in future research. Psycho-behavioural factors are key in understanding people’s behaviour. However, when used in post-hoc segmentation, do not allow for effective targeting as there is no prior understanding of behaviours that need to change within each segment. In future, we should move towards hybrid segmentation to assist with the design of interventions that target behaviours such as healthy eating.

Digital technologies provide a convenient and scalable approach to dietary assessment and personalised feedback, facilitating behaviour change. This is essential for reducing the prevalence of non-communicable diseases at a population level. However, the evaluation of the acceptability and feasibility of dietary feedback delivered via online platforms has not been thoroughly investigated. By utilising the term ‘system architecture’ to describe the essential components of the digital approach to capturing dietary feedback, this systematic review outlines the platform, dietary assessment methodology, reference values for assessing dietary intake and elements of personalised dietary feedback. When reported, the acceptability and feasibility of personalised feedback were captured. OVID Medline, OVID Embase, Scopus via Elsevier and Cinahl Plus via EBSCO identified 5839 studies. Search terms included dietary assessment, feedback and digital technologies. In total, twenty-eight studies involving 301 271 participants were included. Food frequency questionnaires were the most commonly used dietary assessment method, accessed via web-based platforms. Dietary intake was commonly assessed using a diet quality index, and feedback was provided on food groups, often combined with a diet quality score or macronutrient analysis. While participant acceptance of personalised dietary feedback was generally high, the overall completion rates for acceptability questionnaires were low, and feasibility was seldom reported. Methods used to measure acceptability and feasibility varied, preventing comparisons across studies. Study quality was high; however, future research would benefit from the involvement of stakeholders and end-users in designing feedback messages.

Background/Objectives: Diet quality indices (DQIs) are commonly used to evaluate the alignment of dietary intake with national dietary guidelines. However, few methods have been developed to apply DQIs to image-based dietary assessment tools. This study aimed to develop a novel food-based Diet Quality Score (DQS) and examine its construct validity in Australian adults aged 18–65 years living with a higher weight (BMI 30–45 kg/m²), using the validated Healthy Eating Index for Australians 2013 (HEIFA-2013) for comparison. Methods: This cross-sectional study assessed dietary intake in 260 adults (mean age 48 ± 12 years; BMI 35.0 ± 4.2) collected over 4 days using the mobile Food Record (mFR™) and scored using the HEIFA-2013 and DQS (both scored out of 100). Descriptive statistics summarised participant characteristics with DQS and HEIFA-2013 scores. Construct validity was examined by assessing the associations between sociodemographic, health behaviour, anthropometric, and clinical blood marker variables and DQS and HEIFA-2013 scores. Multivariable binary logistic regression identified variables associated with being in the lowest diet quality tertile for both DQIs. Results: The mean scores were 47.4 ± 8.7 for the DQS and 52.0 ± 8.6 for HEIFA-2013. Both scores were positively associated with age (DQS: r = 0.216, p < 0.001; HEIFA-2013: r = 0.265, p < 0.001) and attention paid to the health aspects of their diet (DQS: p < 0.023; HEIFA-2013: p < 0.010). Increasing age (OR = 0.958, 95% CI: 0.932–0.985, p = 0.003) and moderate versus low physical activity (OR = 0.40, 95% CI: 0.21–0.80, p = 0.009) decreased odds of being in the lowest diet quality tertile for both DQI scores. Conclusions: The DQS demonstrated acceptable construct validity, providing a valid method for using image-based dietary assessments to evaluate diet quality in Australian populations.

Engaging in sedentary activities and consuming energy-dense, nutrient-poor foods increases the risk of developing non-communicable diseases (1). | POSTER