Long Oral Presentation Nutrition Society of Australia 50th Annual Scientific Meeting 2026

Cross-sectional associations between diet quality and work schedules among Australian night shift workers living with overweight and obesity. (147234)

Samara Legrand 1 , Angela B Clark 1 2 , Gloria K.W Leung 1 3 , Corinne Davis 1 , Michelle Rogers 4 , Yan Y Phoi 5 , Rochelle Davis 1 , Nicole J Kellow 1 , Alison M Coates 5 6 , Tracy A McCaffrey 1 , Maxine P Bonham 1
  1. Department of Nutrition, Dietetics and Food, Monash University, Notting Hill, Victoria, Australia
  2. School of Health Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
  3. Deakin Global Centre for Prevention Health and Nutrition (Institute for Health Transformation), School of Health and Social Development, Deakin University, Geelong, Victoria, Australia
  4. South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
  5. Alliance for Research in Exercise, Nutrition and Activity (ARENA), Adelaide University, Adelaide, South Australia, Australia
  6. Graduate Research School, Adelaide University, Adelaide, South Australia, Australia

For many night shift workers, unpredictable work schedules disrupt sleep patterns, leading to inconsistent meal timing and poorer eating behaviours(1). While previous research has primarily compared dietary intakes between day and night shifts, the transition from night shift to days off presents challenges due to misaligned sleep and fatigue, which may influence dietary choices and overall diet quality(2). Understanding dietary patterns across shift transitions may support the development of advice tailored to the unique scheduling demands of night shift workers. This cross-sectional analysis used data from the Shifting Weight using Intermittent Fasting (SWIFt) randomised controlled trial, which aimed to compare the effectiveness of three weight-loss dietary interventions on body weight and metabolic outcomes in night shift workers living with overweight and obesity(3). Participants completed 7-day food diaries that included night shifts (at least two) and days off, as well as a 14-day work diary. Diet quality was assessed using an adapted version of the Healthy Eating Index for Australian Adults (HEIFA-2013), which assesses diet quality across 11 components and provides a total score ranging from 0 to 100 (4). A linear mixed model adjusted for age and gender examined diet quality across five shift types: day shift (DS), first night shift (1NS), subsequent night shift (SNS), first day off after a night shift (1DONS), and other days off (DO). A total of 218 participants (55% female, mean ± SD age 46.7 ± 9.7 years) contributed 1150 days of dietary data. Total diet quality was low, with a mean HEIFA-2013 score of 48.4 ± 11.9 out of 100, indicating poor adherence to the Australian Dietary Guidelines. Shift type was associated with total diet quality (p < 0.001), with the highest scores observed on DS (EMM (SE) 50.2 (0.9)) and the lowest on 1DONS (46.4 (0.9); p = 0.006). Differences in HEIFA-2013 component scores across shift types were observed for alcohol, discretionary foods, and wholegrains, with the lowest scores on the 1DONS. Females had a higher overall diet quality than males (p = 0.001), and increasing age was associated with higher diet quality (p < 0.001). No significant interactions between shift type and age or gender were observed. In conclusion, this study found that diet quality among Australian night shift workers living with overweight or obesity was consistently low and varied across the work schedule. The 1DONS was associated with the lowest diet quality, suggesting that poorer dietary choices may occur during this period, possibly due to fatigue. These findings highlight a potential opportunity to improve the diet quality of night shift workers through interventions tailored to their work schedules.

  1. Clark AB, Coates AM, Davidson ZE et al. (2023) Adv. Nutr. 14, 295–316
  2. Kalkanis A, Demolder S, Papadopoulos D et al. (2023) Front. Neurol. 14, 1270043
  3. Bonham MP, Leung GKW, Rogers R et al. (2025) eBioMedicine 117, 105803
  4. Roy R, Hebden L, Rangan A. et al. (2016) Nutrition 32, 432–440