Objectives: Explore the level of general nutrition knowledge and demographic influences of knowledge levels in a community sample. Design and setting: A sample of volunteers, recruited from community centres in two suburbs of differing socio-economic status, in Adelaide, South Australia. Subjects: Two hundred and one people, aged 18 years and older, completed a modified and validated version of the General Nutrition Knowledge Questionnaire (113 items). The questionnaire was self-administered and completed under supervision. Results: Basic messages about eating more fruit, vegetables and fibre, and less fatty and salty foods were best understood. Confusion was evident with more detailed nutrition information. For example, 90 % of the people were aware of the recommendations to eat more fruit and vegetables, but 56 % and 62 % knew the recommended number of servings of fruit and vegetables, respectively. Descriptive statistics showed significant demographic variation in nutrition knowledge levels; multiple regression analysis confirmed the significant independent effects of gender, age, highest level of education and employment status on nutrition knowledge level (P , 0?01 level). The model accounted for 40 % of the variance in nutrition knowledge scores. Conclusions: There is demographic variation in nutrition knowledge levels and a broad lack of awareness of some public health nutrition recommendations. Having a detailed understanding of the deficiencies in community knowledge should allow for future nutrition education programmes to target subgroups of the population or particular areas of nutrition education, to more efficiently improve knowledge and influence dietary behaviour.

Parent involvement is an important component of obesity prevention interventions. However, the best way to support parents remains unclear. This review identifies interventions targeting parents to improve children's weight status, dietary and/or activity patterns, examines whether intervention content and behaviour change techniques employed are associated with effectiveness. Seventeen studies, in English, 1998-2008, were included. Studies were evaluated by two reviewers for study quality, nutrition/activity content and behaviour change techniques using a validated quality assessment tool and behaviour change technique taxonomy. Study findings favoured intervention effectiveness in 11 of 17 studies. Interventions that were considered effective had similar features: better study quality, parents responsible for participation and implementation, greater parental involvement and inclusion of prompt barrier identification, restructure the home environment, prompt self-monitoring, prompt specific goal setting behaviour change techniques. Energy intake/density and food choices were more likely to be targeted in effective interventions. The number of lifestyle behaviours targeted did not appear to be associated with effectiveness. Intervention effectiveness was favoured when behaviour change techniques spanned the spectrum of behaviour change process. The review provides guidance for researchers to make informed decisions on how best to utilize resources in interventions to support and engage parents, and highlights a need for improvement in intervention content reporting practices.

Diet quality indices reflect overall dietary patterns better than single nutrients or food groups. The study aims were to develop a measure of adherence with dietary guidelines applicable to child and adolescent populations in Australia and determine the association between index scores and food and nutrient intake, socio-demographic characteristics, and measures of adiposity. Data were analyzed from 4- to 16-y-old participants of the 2007 Australian Children's Nutrition and Physical Activity Survey (n = 3416). The Dietary Guideline Index for Children and Adolescents (DGI-CA) comprises 11 components: 5 core food groups, wholegrain bread, reduced-fat dairy foods, extra foods (nutrient poor and high in fat, salt, and added sugar), healthy fats/oils, water, and diet variety (possible score of 100). The index criteria were age specific. The mean DGI-CA score was low (53.6 ± 0.4), similar between boys and girls, and differed by age; the youngest children scored higher than the oldest children (P < 0.0001). Higher DGI-CA scores were associated with lower energy intake, energy density, total and saturated fat, and sugar intake; higher protein, carbohydrate, fiber, calcium, iron, vitamin C, vitamin A, folate, phosphorous, magnesium, zinc, and iodine intakes; and a higher polyunsaturated:saturated fat ratio (P < 0.0001). DGI-CA scores were associated with socio-economic characteristics and measures of family circumstance. Weak positive associations were observed between DGI-CA score and BMI or waist circumference Z-scores in the 4- to 10-y and 12- to 16-y age groups only. This index is the first validated index in Australia and one of the few international indices to describe the diet quality of children and adolescents.

We developed and tested a mobile phone application (app) to support individuals embarking on a partial meal replacement programme (MRP). Overweight or obese women were randomly allocated to one of two study groups. The intervention group received an MRP Support app. The control group received a static app based on the information available with the MRP. A total of 58 adult women (Support n = 28; Control n = 30) participated in the 8-week trial. Their BMI was 26-43 kg/m Usage data suggested that the intervention group were more engaged with using the app throughout the study period. Mixed modelling revealed that the difference in weight loss between the intervention and control groups (estimated mean, EM = 3.2% and 2.2% respectively) was not significant (P = 0.08). Objective data suggested that users of the Support app were more engaged than those using the control app. A total of 1098 prompts (54%) asking people in the intervention group to enter their meals were completed prior to the evening prompt. Women in the intervention group reported a greater increase in positive affect (i.e. mood) than those in the control group (EM = 0.48 and -0.01, respectively) (P = 0.012). At Week 8, those in the control group reported a greater decrease in the effort they were willing to put into staying on the diet than those who received the Support app (EM = -2.8 and -1.4, respectively) (P = 0.024). The Support app could be a useful adjunct to existing MRPs for psychological outcomes.