Introduction The home and school environments play important roles in influencing children's health behaviours. However, their simultaneous influence on childhood obesity has not yet been examined. We explore the relationship of the home and school environments with childhood obesity, to determine whether this relationship is mediated by children's fruit and vegetable intake and physical behaviours. Methods This study uses baseline data from 9 to 11 year old children, their parents and school principals (matched data n = 2466) from the Obesity Prevention and Lifestyle Project. Child-reported behaviours, parent-reported home environment and principal-reported school environment data were collected via questionnaires. Trained researchers measured children's height and weight, and Body Mass Index (BMI, kg/m) was calculated. Structural equation modelling was used to assess the relationship of the home and school environments with children's fruit and vegetable intake, physical activity behaviours, and children's BMI. Result The home diet environment was positively associated with child diet (β = 0.18, p < 0.001). The home physical activity environment had the largest inverse association with BMI (β = - 0.11, p < 0.001), indirectly through child physical activity (β = 0.28 ,p < 0.001). Schools' healthy eating policy implementation was significantly associated with child diet (β = 0.52, p < 0.05), but physical activity policy was not associated with child activity (β = - 0.007, p > 0.05). The school environment was not associated with child BMI. Discussion The home environment had a stronger association with healthier child behaviours, compared to the school environment. These findings suggest that future childhood obesity interventions targeting healthier home environments and supporting parents can promote healthier child eating and physical activity behaviours.

Globally, population dietary intakes fall below the guideline recommendations and large-scale interventions have had modest success in improving diet quality. To inform the development of more targeted approaches, this study analysed the variations in self-reported data from an online survey of Australian adults collected between 2015 and 2020, to identify common combinations of low scoring components within a dietary guideline index. A low score was defined as meeting less than half the guideline recommendations (a score <50 out of 100). Among 230,575 adults, a single component analysis showed that 79.5% had a low score for discretionary choices, 72.2% for healthy fats and 70.8% for dairy. The combinations approach showed 83.0% of individuals had two to five low scoring components, with men, younger adults aged 18–30 years and individuals with obesity (BMI ≥ 30) more likely to have five or more. The most common dietary pattern combination included low scores for discretionary choices, dairy and healthy fats. There was a considerable but systematic variation in the low scoring components within the dietary patterns, suggesting that interventions with the flexibility to address particular combinations of key food groups across subgroups could be an effective and resource efficient way to improve diet quality in the population.

Objective To develop and evaluate the feasibility of a mobile application in Swiss households and assess its impact on dietary behavior and food acceptability between children who cooked with limited parental support (intervention group) with children who were not involved in cooking (control group). Methods A ten-week randomized controlled trial was conducted online in 2020. Parents were given access to a mobile-app with ten recipes. Each recipe emphasized one of two generally disliked foods (Brussels sprouts or whole-meal pasta). Parents photographed and weighed the food components from the child’s plate and reported whether their child liked the meal and target food. The main outcome measures were target food intake and acceptability analyzed through descriptive analysis for pre-post changes. Results Of 24 parents who completed the baseline questionnaires, 18 parents and their children (median age: 8 years) completed the evaluation phase. Mean child baseline Brussel sprouts and whole-meal pasta intakes were 19.0 ± 24.2 g and 86.0 ± 69.7 g per meal, respectively. No meaningful differences in intake were found post-intervention or between groups. More children reported a neutral or positive liking towards the whole-meal pasta in the intervention group compared to those in the control group. No change was found for liking of Brussel sprouts. Conclusions for practice The intervention was found to be feasible however more studies on larger samples are needed to validate feasibility. Integrating digital interventions in the home and promoting meal preparation may improve child reported acceptance of some healthy foods. Using such technology may save time for parents and engage families in consuming healthier meals.

Objectives Using technology, nutrition messages can be tailored to individuals, which may enhance the effectiveness of online dietary behavior interventions. The study objective was to test whether a brief, online intervention using tailored nutrition messaging, supported by behavior change techniques, is more effective than a brief, online intervention using generic messages, in reducing discretionary choices (energy dense, nutrient poor foods and beverages) intake in a sample of Australian adults. Methods A two armed randomized controlled trial was conducted from September to December 2019. The intervention group received two e-mails containing tailored nutrition messages, supported by behavior change techniques, over a 28 day period. The control group received similar e-mails using generic messages. Sociodemographic and dietary data were collected using a validated Short Food Survey at baseline and at follow-up. Statistical analysis used ANCOVA. Results Final analysis included 1441 Australian adults, of which 77.3% were female, with a mean age of 50.8 (SD = 16.0) and Body Mass Index of 28.2 (SD = 6.3). There was no significant difference between delivering tailored or generic messages via e-mail, on discretionary choice intake at follow-up, after controlling for baseline discretionary choice intake (P = .49). Regardless of intervention group allocation, there was a decrease in mean intake of discretionary choices from baseline to follow-up (M = 4.2, SD = 3.9 vs. M = 3.1, SD = 4.0, respectively, P &lt; .001), with a small to medium effect (Cohen's d = .28). Conclusions The primary outcome of the study was discretionary choice intake after a 28 day brief, online intervention using tailored or generic messaging. The intervention was able to significantly reduce discretionary choice intake, however, the impact was not significantly enhanced by tailoring the intervention message. Other forms of tailoring or the inclusion of additional intervention features should be investigated to further enhance intervention effectiveness in future research. Funding Sources The presented work had financial support from Flinders University through a research scholarship, and from CSIRO, Healthy Development Adelaide and the Commonwealth Scholarships Program through top-up scholarships.