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Background. Shift work is characterized by employees working outside the standard hours of 7:00 am to 6:00 pm. Because shift work includes night work, the normal sleep–wake cycle (circadian rhythm) is disrupted, with potential consequences for shift workers’ physical and mental health. Objectives. To assess the pooled effects of shift work on mental health and to evaluate whether these differ in men and women. Search Methods. We searched PubMed, Scopus, and Web of Science databases for peer-reviewed or government reports published up to August 2018 Selection Criteria. To be included, studies had to be longitudinal or case–control studies of shift work exposure associated with adverse mental health outcomes. For subanalyses, we grouped these outcomes as anxiety symptoms, depressive symptoms, or general poor mental health symptoms. Data Collection and Analysis. We followed the Meta-Analysis of Observational Studies in Epidemiology Group guidelines. We extracted adjusted risk estimates for each study to calculate pooled effect sizes (ESs) using random effect models and metaregression analysis to explore sources of heterogeneity. Main Results. We included 7 longitudinal studies, with 28 431 unique participants. Shift work was associated with increased overall risk of adverse mental health outcomes combined (ES = 1.28; 95% confidence interval [CI] = 1.02, 1.62; I 2  = 70.6%) and specifically for depressive symptoms (ES = 1.33; 95% CI = 1.02, 1.74; I 2  = 31.5%). Gender differences explained more than 90% of heterogeneity, with female shift workers more likely to experience depressive symptoms than female non–shift workers (odds ratio = 1.73; 95% CI = 1.39, 2.14). Authors’ Conclusions. To our knowledge, this is the first meta-analysis to investigate the pooled effects of shift work on the risk of poor mental health, including subanalyses by type of poor mental health and gender. Shift workers, particularly women, are at increased risk for poor mental health, particularly depressive symptoms. Public Health Implications. Depression accounts for 4.3% of the global burden of disease and incidence, with mental disorders worldwide predicted to cost US $16.3 million by 2030. With 1 in 5 people in the United States and Europe doing shift work, and the increased risk of poor mental health among shift workers, shift work industries are a priority context for reducing this burden. Workplace health promotion programs and policies are needed to minimize shift workers’ risk of poor mental health.

Objectives The aim of this review was to assess the risk of cardiovascular disease (CVD) events associated with shift work and determine if there is a dose-response relationship in this association. Method Electronic databases (PubMed, Scopus, and Web of Science) were searched for cohort or case-control control study designs in any population, reporting exposure to shift work as the main contributing factor to estimate CVD risk. For each study, adjusted relative risk (RR) ratios and 95% confidence intervals (CI) were extracted, and used to calculate the pooled RR using random-effect models. Meta-regression analysis was conducted to explore potential heterogeneity sources. Potential non-linear dose-response relationships were examined using fractional polynomial models. Results We included 21 studies with a total of 173 010 unique participants. The majority of the studies were ranked low-to-moderate risk of bias. The risk of any CVD event was 17% higher among shift workers than day workers. The risk of coronary heart disease (CHD) morbidity was 26% higher (1.26, 95% CI 1.10-1.43, I^2= 48.0%). Sub-group analysis showed an almost 20% higher risk of CVD and CHD mortality among shift workers than those who did not work shifts (1.22, 95% CI 1.09-1.37, I^2= 0% and 1.18, 95% CI 1.06-1.32 I^2=0%; respectively). After the first five years of shift work, there was a 7.1% increase in risk of CVD events for every additional five years of exposure (95% CI 1.05-1.10). Heterogeneity of the pooled effect size (ES) estimates was high (I^2=67%), and meta-regression analysis showed that sample size explained 7.7% of this. Conclusions The association between shift work and CVD risk is non-linear and seems to appear only after the first five years of exposure. As shift work remains crucial for meeting production and service demands across many industries, policies and initiatives are needed to reduce shift workers' CVD risk.

Objectives. To review the contribution of the Nurses’ Health Studies (NHS and NHS II) in addressing hypotheses regarding risk factors for and consequences of obesity. Methods. Narrative review of the publications of the NHS and NHS II between 1976 and 2016. Results. Long-term NHS research has shown that weight gain and being overweight or obese are important risk factors for type 2 diabetes, cardiovascular diseases, certain types of cancers, and premature death. The cohorts have elucidated the role of dietary and lifestyle factors in obesity, especially sugar-sweetened beverages, poor diet quality, physical inactivity, prolonged screen time, short sleep duration or shift work, and built environment characteristics. Genome-wide association and gene–lifestyle interaction studies have shown that genetic factors predispose individuals to obesity but that such susceptibility can be attenuated by healthy lifestyle choices. This research has contributed to evolving clinical and public health guidelines on the importance of limiting weight gain through healthy dietary and lifestyle behaviors. Conclusions. The NHS cohorts have contributed to our understanding of the risk factors for and consequences of obesity and made a lasting impact on clinical and public health guidelines on obesity prevention.

Even though epidemiological evidence links specific workplace stressors to health outcomes, the aggregate contribution of these factors to overall mortality and health spending in the United States is not known. In this paper, we build a model to estimate the excess mortality and incremental health expenditures associated with exposure to the following 10 workplace stressors: unemployment, lack of health insurance, exposure to shift work, long working hours, job insecurity, work–family conflict, low job control, high job demands, low social support at work, and low organizational justice. Our model uses input parameters obtained from publicly accessible data sources. We estimated health spending from the Medical Expenditure Panel Survey and joint probabilities of workplace exposures from the General Social Survey, and we conducted a meta-analysis of the epidemiological literature to estimate the relative risks of poor health outcomes associated with exposure to these stressors. The model was designed to overcome limitations with using inputs from multiple data sources. Specifically, the model separately derives optimistic and conservative estimates of the effect of multiple workplace exposures on health, and uses optimization to calculate upper and lower bounds around each estimate, which accounts for the correlation between exposures. We find that more than 120,000 deaths per year and approximately 5%–8% of annual healthcare costs are associated with and may be attributable to how U.S. companies manage their work forces. Our results suggest that more attention should be paid to management practices as important contributors to health outcomes and costs in the United States. This paper was accepted by Dimitris Bertsimas, optimization .

Objective To synthesise the association of shift work with major vascular events as reported in the literature.Data sources Systematic searches of major bibliographic databases, contact with experts in the field, and review of reference lists of primary articles, review papers, and guidelines.Study selection Observational studies that reported risk ratios for vascular morbidity, vascular mortality, or all cause mortality in relation to shift work were included; control groups could be non-shift (“day”) workers or the general population.Data extraction Study quality was assessed with the Downs and Black scale for observational studies. The three primary outcomes were myocardial infarction, ischaemic stroke, and any coronary event. Heterogeneity was measured with the I2 statistic and computed random effects models.Results 34 studies in 2 011 935 people were identified. Shift work was associated with myocardial infarction (risk ratio 1.23, 95% confidence interval 1.15 to 1.31; I2=0) and ischaemic stroke (1.05, 1.01 to 1.09; I2=0). Coronary events were also increased (risk ratio 1.24, 1.10 to 1.39), albeit with significant heterogeneity across studies (I2=85%). Pooled risk ratios were significant for both unadjusted analyses and analyses adjusted for risk factors. All shift work schedules with the exception of evening shifts were associated with a statistically higher risk of coronary events. Shift work was not associated with increased rates of mortality (whether vascular cause specific or overall). Presence or absence of adjustment for smoking and socioeconomic status was not a source of heterogeneity in the primary studies. 6598 myocardial infarctions, 17 359 coronary events, and 1854 ischaemic strokes occurred. On the basis of the Canadian prevalence of shift work of 32.8%, the population attributable risks related to shift work were 7.0% for myocardial infarction, 7.3% for all coronary events, and 1.6% for ischaemic stroke.Conclusions Shift work is associated with vascular events, which may have implications for public policy and occupational medicine.

Sleep loss may trigger mood episodes in people with bipolar disorder but individual differences could influence vulnerability to this trigger. To determine whether bipolar subtype (bipolar disorder type I (BP-I) or II (BD-II)) and gender were associated with vulnerability to the sleep loss trigger. During a semi-structured interview, 3140 individuals (68% women) with bipolar disorder (66% BD-I) reported whether sleep loss had triggered episodes of high or low mood. DSM-IV diagnosis of bipolar subtype was derived from case notes and interview data. Sleep loss triggering episodes of high mood was associated with female gender (odds ratio (OR) = 1.43, 95% CI 1.17-1.75, < 0.001) and BD-I subtype (OR = 2.81, 95% CI 2.26-3.50, < 0.001). Analyses on sleep loss triggering low mood were not significant following adjustment for confounders. Gender and bipolar subtype may increase vulnerability to high mood following sleep deprivation. This should be considered in situations where patients encounter sleep disruption, such as shift work and international travel.

Objectives Many studies have investigated the association between long working hours and health. By focusing on differences in the definition of long working hours and the influence of shift work, we attempt to explain why the results of these studies remain inconclusive. Methods We defined long working hours as working time greater than around 40 hours per week or 8 hours per day. Since previous studies have indicated that shift work is detrimental to health, we minimized the influence of shift work in the studies. We also placed importance on the existence of reference groups since this made the results clearer. Based on these points, we analyzed previous studies to clarify the epidemiological evidence regarding the association between long working hours and health. We established inclusion criteria and carried out a systematic search for articles published in the Mediine and PsycINFO databases between 1995-2012. Results We identified a total of 17 articles and 19 studies (12 prospective cohort and 7 cross-sectional studies). The outcomes were all-cause mortality, circulatory disease, diabetes mellitus, metabolic syndrome, depressive state, anxiety, other psychological disorders, sleep condition, cognitive function, and health-related behavior. Long working hours had significant adverse effects on most health outcomes. Conclusions We concluded that working long hours is associated with depressive state, anxiety, sleep condition, and coronary heart disease. However, further studies that appropriately deal with the definition of long working hours and shift work are needed.

Background: Multisensor fitness trackers offer the ability to longitudinally estimate sleep quality in a home environment with the potential to outperform traditional actigraphy. To benefit from these new tools for objectively assessing sleep for clinical and research purposes, multisensor wearable devices require careful validation against the gold standard of sleep polysomnography (PSG). Naturalistic studies favor validation. Objective: This study aims to validate the Fitbit Charge 2 against portable home PSG in a shift-work population composed of 59 first responder police officers and paramedics undergoing shift work. Methods: A reliable comparison between the two measurements was ensured through the data-driven alignment of a PSG and Fitbit time series that was recorded at night. Epoch-by-epoch analyses and Bland-Altman plots were used to assess sensitivity, specificity, accuracy, the Matthews correlation coefficient, bias, and limits of agreement. Results: Sleep onset and offset, total sleep time, and the durations of rapid eye movement (REM) sleep and non–rapid-eye movement sleep stages N1+N2 and N3 displayed unbiased estimates with nonnegligible limits of agreement. In contrast, the proprietary Fitbit algorithm overestimated REM sleep latency by 29.4 minutes and wakefulness after sleep onset (WASO) by 37.1 minutes. Epoch-by-epoch analyses indicated better specificity than sensitivity, with higher accuracies for WASO (0.82) and REM sleep (0.86) than those for N1+N2 (0.55) and N3 (0.78) sleep. Fitbit heart rate (HR) displayed a small underestimation of 0.9 beats per minute (bpm) and a limited capability to capture sudden HR changes because of the lower time resolution compared to that of PSG. The underestimation was smaller in N2, N3, and REM sleep (0.6-0.7 bpm) than in N1 sleep (1.2 bpm) and wakefulness (1.9 bpm), indicating a state-specific bias. Finally, Fitbit suggested a distribution of all sleep episode durations that was different from that derived from PSG and showed nonbiological discontinuities, indicating the potential limitations of the staging algorithm. Conclusions: We conclude that by following careful data processing processes, the Fitbit Charge 2 can provide reasonably accurate mean values of sleep and HR estimates in shift workers under naturalistic conditions. Nevertheless, the generally wide limits of agreement hamper the precision of quantifying individual sleep episodes. The value of this consumer-grade multisensor wearable in terms of tackling clinical and research questions could be enhanced with open-source algorithms, raw data access, and the ability to blind participants to their own sleep data.

Objectives This discussion paper aims to provide scientifically based recommendations on night shift schedules, including consecutive shifts, shift intervals and duration of shifts, which may reduce health and safety risks. Short-term physiological effects in terms of circadian disruption, inadequate sleep duration and quality, and fatigue were considered as possible links between night shift work and selected health and safety risks, namely, cancer, cardio-metabolic disease, injuries, and pregnancy-related outcomes. Method In early 2020, 15 experienced shift work researchers participated in a workshop where they identified relevant scientific literature within their main research area. Results Knowledge gaps and possible recommendations were discussed based on the current evidence. The consensus was that schedules which reduce circadian disruption may reduce cancer risk, particularly for breast cancer, and schedules that optimize sleep and reduce fatigue may reduce the occurrence of injuries. This is generally achieved with fewer consecutive night shifts, sufficient shift intervals, and shorter night shift duration. Conclusions Based on the limited, existing literature, we recommend that in order to reduce the risk of injuries and possibly breast cancer, night shift schedules have: (i) ≤3 consecutive night shifts; (ii) shift intervals of ≥11 hours; and (iii) ≤9 hours shift duration. In special cases - eg, oil rigs and other isolated workplaces with better possibilities to adapt to daytime sleep - additional or other recommendations may apply. Finally, to reduce risk of miscarriage, pregnant women should not work more than one night shift in a week.

Shift work is considered necessary to ensure continuity of care in hospitals and residential facilities. In particular, the night shift is one of the most frequent reasons for the disruption of circadian rhythms, causing significant alterations of sleep and biological functions that can affect physical and psychological well-being and negatively impact work performance. The aim of this study was to highlight if shift work with nights, as compared with day work only, is associated with risk factors predisposing nurses to poorer health conditions and lower job satisfaction. This cross-sectional study was conducted from June 1, 2015 to July 31, 2015 in 17 wards of a general hospital and a residential facility of a northern Italian city. This study involved 213 nurses working in rotating night shifts and 65 in day shifts. The instrument used for data collection was the \"Standard Shift Work Index,\" validated in Italian. Data were statistically analyzed. The response rate was 86%. The nurses engaged in rotating night shifts were statistically significantly younger, more frequently single, and had Bachelors and Masters degrees in nursing. They reported the lowest mean score in the items of job satisfaction, quality and quantity of sleep, with more frequent chronic fatigue, psychological, and cardiovascular symptoms in comparison with the day shift workers, in a statistically significant way. Our results suggest that nurses with rotating night schedule need special attention due to the higher risk for both job dissatisfaction and undesirable health effects.