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Determinants of eating behaviours in Australian university students: A cross‐sectional analysis
Author(s) -
Whatnall Megan C.,
Patterson Amanda J.,
Chiu Simon,
Oldmeadow Christopher,
Hutchesson Melinda J.
Publication year - 2020
Publication title -
nutrition and dietetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.479
H-Index - 31
eISSN - 1747-0080
pISSN - 1446-6368
DOI - 10.1111/1747-0080.12584
Subject(s) - psychological intervention , cross sectional study , environmental health , healthy eating , medicine , food group , psychology , gerontology , demography , physical activity , pathology , psychiatry , sociology , physical medicine and rehabilitation
Aim This study aimed to explore clustering among individual eating behaviours in a sample of Australian university students, and explore associations between clustered eating behaviours and demographic characteristics. Methods A cross‐sectional analysis of data from the University of Newcastle (UON) Student Healthy Lifestyle Survey 2017 was conducted. Measures included eating behaviours (eg, vegetables, energy‐dense nutrient poor [EDNP] food intakes) assessed using short diet questions, and demographic characteristics (eg, age, undergraduate/postgraduate student). Factor analysis was used to explore clustering of individual eating behaviours (ie, identify factors). Linear regression models were used to explore associations between eating behaviour factors identified and demographic characteristics. Results A total of 3062 students (70% female; 56% aged 17‐24 years) were included in the analysis. The six eating behaviour factors identified (characterised by higher consumption of the named foods/drinks) were; EDNP snack foods, meat and takeaway foods, fruit and vegetables, sugary drinks, breakfast, and breads and cereals. A higher fruit and vegetable factor score was associated with being female ( P < .001), and a higher meat and takeaway foods factor score was associated with being male ( P < .001) and of younger age ( P < .001). Conclusions Nutrient‐rich foods clustered together and EDNP foods clustered together, that is, the identified factors represent either nutrient‐rich or EDNP foods. Interventions in the university setting should target students with the poorest eating behaviours, including males and younger students.