Determinants of eating behaviours in Australian university students: A cross‐sectional analysis. Issue 3 (3rd November 2019)

• Record Type: Journal Article
• Authors: Whatnall, Megan C.; Patterson, Amanda J.; Chiu, Simon; Oldmeadow, Christopher; Hutchesson, Melinda J.
• Is Part Of: Nutrition & dietetics. Volume 77:Issue 3(2020)
• Page Start: 331
• Page End: 343
• Publication Date: 2019-11-03
• Subjects: college students -- cross‐sectional study -- determinants -- eating behaviours -- university students; Nutrition -- Periodicals; Dietetics -- Periodicals; 613.205
• DOI: 10.1111/1747-0080.12584
• Languages: English
• ISSNs: 1446-6368

Abstract

Abstract: 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.