Research Projects Using Growing Up Data

Growing up in New Zealand Cohort: Dietary patterns and associations with food security at 12 years

Publication Date:
2023
Lead Organisation:
University of Auckland
Lead Researcher:
Clare Wall
Access Type:
External
Primary Classification:
Health and Wellbeing
Secondary Classification:

About the project

Adolescents experience rapid physical, cognitive and psychological growth. During this phase, they are more likely to be exposed and attracted to unhealthy lifestyles, such as skipping meals, high exposure to ultra-processed foods, low preference for foods with higher nutritional quality, and sedentary habits. The establishment of unhealthy diets and the tracking of trajectories of low diet quality represent a major contributor to non-communicable diseases and impacts in higher morbidity rates and premature mortality. Therefore, periodic surveillance of populations diets and their determinants are imperative to guide and monitor policies aiming to improve diet quality and to narrow its inequities. However, in Aotearoa NZ, nationally representative or generalisable information about dietary patterns of young people and their main sociodemographic predictors and associations with food insecurity are currently lacking. This project aims to use the information collected at the 12-year data collection wave to: i) conduct the data cleaning/harmonisation of the food frequency questionnaire (FFQ), ii) identify and describe the cohort`s dietary patterns, (iii) examine the sociodemographic and health behaviour factors associated with the different dietary patterns  and, iv) examine the associations between the dietary patterns and indicators of food security. This project will use information from the FFQ administered  at the 12 year time-point to identify the dietary patterns by performing principal component analyses. Child and mother sociodemographic and health behaviour variables at 12 years will be also examined, as well as the validated scale on food security that was  previously derived/described by the GUINZ (Gerritsen et al. 2023).  Associations will be examined using of multivariate analyses. Analyses will be conducted  using SPSS software.

Start date: 1/11/2023