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Energy intake and the risk of obesity: are dietary patterns significant ?

Published online by Cambridge University Press:  07 March 2019

P. Mc Govern  and
Dr J. O Reilly
P. Mc Govern
Affiliation:
School of Clinical Sciences and Nutrition, University of Chester, Parkgate Road, Chester CH1 4BJ.
Dr J. O Reilly
Affiliation:
Department of Clinical Sciences and Nutrition, University of Chester, Parkgate Road, Chester CH1 4BJ.
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Abstract

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Proceedings of the Nutrition Society , Volume 78 , Issue OCE1: Winter Meeting, 4–5 December 2018, Optimal diet and lifestyle strategies for the management of cardio-metabolic risk , 2019 , E38
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Copyright © The Authors 2019 

Global predictions indicate that Ireland will become the second most obese population in Europe by 2025(Reference Di Cesare, Bentham and Stevens1). The Irish Universities Nutrition Alliance (IUNA) 2011 National Adult Nutrition Survey (n= 1,500, age = 18–90 years) highlighted that 52 % of Irish females were classified overweight (Body Mass Index (BMI) ≥ 25 kg / m2) and 21 % were classed obese (BMI) ≥ 30 kg / m2).

Dietary patterns have evolved with increased dietary variety(Reference McCrory, Burke and Roberts2), snacking occasions(Reference Kant and Graubard3), out of home eating and the prevalence of obseogenic communities(Reference Smith and Cummins4). The synergic relationship between energy intake and day of week is complex and dependent on gender(Reference An5), age(Reference An5), education, socioeconomic status, work environment(Reference Geaney, Kelly and Di Marrazzo6) and seasonal effects(Reference An5). Food and drinks consumed at the weekend tend to be more energy dense with less nutritional value(Reference An5).

This study investigated if energy intake increased over the weekend for females (20–45 years of age) inclusive of all BMI (kg / mReference McCrory, Burke and Roberts2) classifications, resident in Ireland and working outside the home environment. Opportunistic sampling was employed to recruit participants predominantly from nominated companies (n= 32) with a small number of individual participants (n= 3). A cross sectional survey captured dietary intake across a four day diary of two week days (Friday and Monday) and two weekend days (Saturday and Sunday). Participants were requested to record all food and drink consumed, with no alteration to their normal routine. A quantitative study of energy intake was completed based on food diary entry into the Nutritics Nutrition Analysis (Version 4.25 University Edition) Software. A parametric paired t-test determined if there was a significant difference in energy intake over the weekend days compared to week days. Differences in mean energy intake across all four diary days were analysed using a one way (repeated measures) analysis of variance (ANOVA).

Mean energy intake on weekend days (Saturday and Sunday, 3458±916 kcal) was not significantly different (p = .088) to week days (Monday and Friday, 3175±946 kcal). Mean daily energy intake on Saturday (1965±770 kcal) was significantly higher (p = .007) than Sunday (1493±538 kcal) and Monday (p = .001) (1443±493 kcal). Mean energy intake was significantly lower (p = 0.0005) than the estimated average requirement (EAR) (2175 kcal) on all diary days.

In conclusion, mean energy intake was highest on Saturday (1965±770 kcal) compared to other diary days which was attributed to elevated mean daily intake of fat (80±38 g), alcohol (22±3g) and carbohydrate (210±82 g). Taking into consideration the mean participant age (32±7years) and BMI (22.97±3.2 kg / m2), future work could focus on the significance of sustained elevated energy intake over the weekend (without an equivalent energy expenditure) linked to weight gain and BMI classification.

References

1.Di Cesare, M., Bentham, J., Stevens, et al. (2016). Lancet, 387(10026), 1377–1396Google Scholar
2.McCrory, M.A., Burke, A. & Roberts, S.B. (2012). Physiol Behav, 107(4), 576–58310.1016/j.physbeh.2012.06.012Google Scholar
3.Kant, A.K. & Graubard, B.I. (2014). J Acad Nutr Diet, 115(1), 50–6310.1016/j.jand.2014.06.354Google Scholar
4.Smith, D.M. & Cummins, S. (2009) Geog Comp, 3(1), 518–53510.1111/j.1749-8198.2008.00198.xGoogle Scholar
5.An, R. (2016). Ann Epidemiol, 26(1), 57–6510.1016/j.annepidem.2015.10.010Google Scholar
6.Geaney, F., Kelly, C., Di Marrazzo, F. et al. (2016). Prev Med, 89, 76–8310.1016/j.ypmed.2016.05.005Google Scholar