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Is 24-hour energy intake greater during night shift compared to non-night shift patterns? A systematic review.

Elizabeth A CayananNadine A B EyreVickie LaoMaria ComasCamilla M HoyosNathaniel S MarshallCraig L PhillipsJudith Shu-Chu ShiaoYue-Liang Leon GuoChristopher James Gordon
Published in: Chronobiology international (2019)
Introduction: Epidemiological studies show that shift workers are at increased risk of cardiovascular diseases, metabolic dysfunction, diabetes, and obesity. Previous research has shown no difference in energy intake between night and day shifts only; however, it remains unclear whether other non-night shift patterns are different to night shift.Objectives: We investigated whether energy intake of night-shift workers differed from other shift patterns using calorimetry, food diary or food recall over 24-hour periods.Methods: A systematic review was conducted searching CINAHL, MEDLINE, Web of Science, Embase and PsycINFO databases for observational and interventional studies measuring energy intake in real or simulated shift work. Energy intake was extracted to compare night, day, afternoon/evening and rotating shift work cases.Results: After duplicate removal, we screened 1057 abstracts and 68 full-text articles were assessed for eligibility of which 15 studies met the inclusion criteria. All studies were cross-sectional and case-control designs in shift workers. Risk of bias assessment showed a low to moderate risk of bias in the majority of studies. There was no difference in energy intake between night-shift work and non-night shift patterns including early morning, day and afternoon/evening shifts. Night-shift workers did not favor particular macronutrients in comparison to other shift schedules.Conclusions: Energy and macronutrient intake were not detectably different in night shift compared to other shift patterns. Shift work patterns were heterogeneous which likely impacted on dietary assessment timings and computation of 24-h energy intake. Future studies should examine shift schedules with precise circadian timing of food consumption to determine if differences exist in energy and macronutrient intake between different shift patterns.
Keyphrases
  • case control
  • type diabetes
  • cross sectional
  • cardiovascular disease
  • oxidative stress
  • machine learning
  • weight loss
  • skeletal muscle
  • physical activity
  • cardiovascular risk factors
  • single molecule
  • human health