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.
Virginia ChanAlyse DaviesLyndal Wellard-ColeMargaret Allman-FarinelliPublished in: Journal of nutritional science (2022)
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.
Keyphrases
- healthcare
- mental health
- young adults
- physical activity
- molecularly imprinted
- convolutional neural network
- high speed
- weight loss
- body mass index
- high throughput
- human health
- heart rate
- air pollution
- deep learning
- blood pressure
- mass spectrometry
- optical coherence tomography
- single cell
- artificial intelligence
- big data
- solid phase extraction