Shift work, defined as work occurring outside typical daytime working hours, is associated with an increased risk of various non‐communicable diseases, including diabetes and cardiovascular disease. Disruption of the internal circadian timing system and concomitant sleep disturbances is thought to play a critical role in the development of these health problems. Indeed, controlled laboratory studies have shown that short‐term circadian misalignment and sleep restriction independently impair physiological processes, including insulin sensitivity, energy expenditure, immune function, blood pressure and cardiac modulation by the autonomous nervous system. If allowed to persist, these acute effects may lead to the development of cardiometabolic diseases in the long term. Here, we discuss the evidence for the contributions of circadian disruption and associated sleep disturbances to the risk of metabolic and cardiovascular health problems in shift workers. Improving the understanding of the physiological mechanisms affected by circadian misalignment and sleep disturbance will contribute to the development and implementation of strategies that prevent or mitigate the cardiometabolic impact of shift work.

The various non-standard schedules required of shift workers force abrupt changes in the timing of sleep and light-dark exposure. These changes result in disturbances of the endogenous circadian system and its misalignment with the environment. Simulated night-shift experiments and field-based studies with shift workers both indicate that the circadian system is resistant to adaptation from a day- to a night-oriented schedule, as determined by a lack of substantial phase shifts over multiple days in centrally controlled rhythms, such as those of melatonin and cortisol. There is evidence that disruption of the circadian system caused by night-shift work results not only in a misalignment between the circadian system and the external light-dark cycle, but also in a state of internal desynchronization between various levels of the circadian system. This is the case between rhythms controlled by the central circadian pacemaker and clock genes expression in tissues such as peripheral blood mononuclear cells, hair follicle cells, and oral mucosa cells. The disruptive effects of atypical work schedules extend beyond the expression profile of canonical circadian clock genes and affects other transcripts of the human genome. In general, after several days of living at night, most rhythmic transcripts in the human genome remain adjusted to a day-oriented schedule, with dampened group amplitudes. In contrast to circadian clock genes and rhythmic transcripts, metabolomics studies revealed that most metabolites shift by several hours when working nights, thus leading to their misalignment with the circadian system. Altogether, these circadian and sleep-wake disturbances emphasize the all-encompassing impact of night-shift work, and can contribute to the increased risk of various medical conditions. Here, we review the latest scientific evidence regarding the effects of atypical work schedules on the circadian system, sleep and alertness of shift-working populations, and discuss their potential clinical impacts.

The objective of this study was to assess the validity of a sleep/wake activity monitor, an energy expenditure activity monitor, and a partial-polysomnography system at measuring sleep and wake under identical conditions. Secondary aims were to evaluate the sleep/wake thresholds for each activity monitor and to compare the three devices. To achieve these aims, two nights of sleep were recorded simultaneously with polysomnography (PSG), two activity monitors, and a partial-PSG system in a sleep laboratory. Agreement with PSG was evaluated epoch by epoch and with summary measures including total sleep time (TST) and wake after sleep onset (WASO). All of the devices had high agreement rates for identifying sleep and wake, but the partial-PSG system was the best, with an agreement of 91.6% ± 5.1%. At their best thresholds, the sleep/wake monitor (medium threshold, 87.7% ± 7.6%) and the energy expenditure monitor (very low threshold, 86.8% ± 8.6%) had similarly high rates of agreement. The summary measures were similar to those determined by PSG, but the partial-PSG system provided the most consistent estimates. Although the partial-PSG system was the most accurate device, both activity monitors were also valid for sleep estimation, provided that appropriate thresholds were selected. Each device has advantages, so the primary consideration for researchers will be to determine which best suits a given research design.