Metabolisable energy from nuts and patterns of nut consumption in the Australian population: a secondary analysis of the 2011-12 National Nutrition and Physical Activity Survey

BACKGROUND

Nut intake is not associated with increased body weight, which may be explained by their metabolisable energy, among other factors. Therefore, total energy intake may be overestimated among nut consumers. This study aimed to describe the metabolisable energy from nuts and nut consumption patterns in the Australian population.

METHODS

A nut-specific database was expanded to include metabolisable energy of nuts (based on nut type and form) and applied to the 2011-12 National Nutrition and Physical Activity Survey (NNPAS). Participants were Australians aged 2 years and older from the 2011-12 NNPAS (n = 12,153, with n = 4,765 nut consumers). Mean metabolisable energy intake was compared with mean energy intake using Atwater factors in nut consumers. Additionally, nut consumption patterns were explored, including the proportion of nuts consumed at meals and snacks.

RESULTS

Among nut consumers, mean metabolisable energy from nuts based only on nut type was 241.2 (95% confidence interval [CI]: 232.0, 250.5) kJ/day and mean metabolisable energy considering both nut type and form was 260.7 (95% CI: 250.2, 271.2) kJ/day. Energy intake from nuts using Atwater factors was 317.6 (95% CI: 304.8, 330.3) kJ/day. Nuts were more likely to be consumed at snack occasions, with approximately 63% of nut intake occurring as a snack.

CONCLUSION

Application of metabolisable energy to the 2011-12 NNPAS has a significant impact on calculation of energy intake from nuts. Nut consumption patterns identified a majority of nut consumption occurring as snacks. These findings may inform strategies to support nut consumption in Australia.

Snacking habits of Brazilian adolescents: Brazilian National Dietary Survey, 2017-2018

This study was aimed at describing the snack consumption among Brazilian adolescents. The Brazilian National Dietary Survey (2017-2018) is a nationwide representative cross-sectional study that collected food consumption data from 8264 adolescents (10 to 19 years old); data from one 24-h recall were used, in which all foods and beverages consumed throughout the day prior to the interview were described along with the amount, unit of measurement, time, place and occasion of consumption (breakfast, lunch, dinner or snack). Snacks were classified as follows: (a) Morning snack: "Snacks" consumed between 6 AM and 12 PM; (b) Afternoon snack: "Snacks" consumed between 1 PM and 5 PM; (c) Evening snack: "Snacks" consumed between 6 PM and 5 AM. Individuals were categorised according to the number of "Snacks" per day (zero, one, two or three or more). Among the evaluated adolescents, 52.8% were female and 23.0% were overweight. The consumption of at least one snack per day was mentioned by 88.2% of the adolescents, with an average of 2.33 snacks per day. An afternoon snack was consumed by 70.0% of the adolescents. Snack consumption was related to higher mean daily energy intake and a greater contribution of carbohydrates, added sugar and trans fat to the total daily energy intake. 'Cookies & Crackers' and 'Fast Foods' were the groups that contributed the most to the energy intake provided by snacks, showing, therefore, that less healthy foods were relevant components of the snacks consumed. For their important contribution to food intake, snacking habits deserve attention among the topics covered in dietary guidelines.

Young Australian Adults Prefer Video Posts for Dissemination of Nutritional Information over the Social Media Platform Instagram: A Pilot Cross-Sectional Survey

Growing social media use in young adults may have applications in health promotion. This study aimed to determine the acceptability and feasibility of using Instagram to disseminate nutritional information to young Australians and assess the most preferred post style. A cross-sectional web-based pilot survey was conducted in 18−30-year-olds residing in New South Wales. Eight sets of mock Instagram posts were generated comprising three formats: (i) text/icon, (ii) realistic image, or (iii) video. Respondents (n = 108) were asked to review and rank posts from highest to lowest according to likelihood of engagement, visual preference, motivation to change eating behaviors, and relevancy of information. The Friedman test (Wilcoxon signed-rank test post hoc analysis with Bonferroni correction) was conducted to determine differences between the three post styles. Video style posts were more likely to be engaged with (p < 0.001), visually preferred (p < 0.001), more motivating to change eating behaviors (p < 0.001), and presented the most relevant food and nutrition knowledge (p < 0.001) compared with the other post styles. Most participants reported that Instagram was a suitable platform to share food and nutrition information (96%). The findings of this pilot study can be used to inform a large study that investigates the use of Instagram among a more diverse population and with a greater number of video posts tailored for audience segmentation.

The energy density of meals and snacks consumed by young Australian adults (18-30 years old) are influenced by preparation location but not screen use nor social interactions: findings from the MYMeals wearable camera study

The present study examined the association of contextual factors (social and food preparation location) with the energy density of meals and snacks consumed in a sample of young Australian adults (18-30 years old) identified using wearable camera technology. Over three consecutive days, a subsample of young adults wore a wearable camera that captured images in 30 s intervals. Eating episodes from 133 participants were annotated for preparation location and social context (covering social interaction and screen use). Over the same period, participants completed daily 24 h recalls. The nutritional composition of meals and snacks was calculated by matching the items identified in the camera to the 24 h recall using time and date stamps. Self-reported data (weight and height) was used to calculate body mass index and (residential postcode) to assign socio-economic status. The association of context and demographic factors with energy density was determined using a mixed linear regression model employing the bootstrap method with bias-corrected and accelerated. In total, 1817 eating episodes were included in the analysis (n 8 preparation unclear and n 15 food components could not be identified excluded). Food prepared within the home was 1⋅1 kJ/g less energy-dense than other preparation locations. Lunches (CI -1⋅7 to -0⋅3) and dinners (CI -1⋅6 to -0⋅5) were both 1⋅0 kJ/g lower in energy density than breakfasts. Snacks were 3⋅5 kJ/g (CI 2⋅8-4⋅1) more energy-dense than breakfasts. Food prepared outside the home and food consumption during snacking appear to be adversely contributing to energy-dense food intake.