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OBJECTIVES: This study aimed to evaluate the independent and interactive effects of changes in overtime and night shifts on burnout among nurses during the COVID-19 pandemic. METHODS: Nurses working in an Italian university hospital (N=317) completed the Maslach Burnout Inventory in September 2019 and again in December 2020. Based on hospital administrative data, changes in overtime and night shifts in the same years were categorized into three groups each. Linear regressions were used to estimate 2020 burnout differences between exposure groups, controlling for 2019 burnout levels, demographic and work-related characteristics, and to test the interaction between the two exposures. RESULTS: Nurses in the onset of high overtime group had higher emotional exhaustion [4.33, 95% confidence interval (CI) 1.74−6.92], depersonalization (2.10, 95% CI 0.49−3.71), and poor personal accomplishment (2.64, 95% CI 0.55−4.74) compared to stable low overtime nurses. Nurses in the increase in night shifts group had lower emotional exhaustion (-4.49, 95% CI -7.46− -1.52) compared to no night shift nurses. Interaction analyses revealed that this apparently paradoxical effect was limited to stable low overtime nurses only. Moreover, increases in night shifts were associated with higher depersonalization and poor personal accomplishment in nurses in the stable high overtime group. CONCLUSIONS: Increase in overtime is an independent risk factor for burnout among nurses, highlighting the need for specific regulations and actions to address it. Long-standing guidelines for the assignment of night shifts might have contributed to attenuate the impact of their increase on nurses’ mental health.

Worldwide, there are an estimated 374 million non-fatal work-related injuries with >4 days of absences from work (1) and more than 3000 fatal work injuries in European countries each year (2). The human cost of this is vast, and the economic burden of poor occupational safety and health practices is estimated to be about 4% of global gross domestic product (GDP) each year (1). There is a need to identify risk factors of work-related injuries as such knowledge may help decrease the number of injuries. A promising candidate for such a risk factor is extended working hours, which leave less time outside work and thereby less time for sleep and restitution. The resulting sleepiness can in turn cause poorer cognitive performance and thus constitutes a plausible mechanism linking long working hours to accidents. Being awake for 18 hours has been shown to impair performance corresponding to a blood alcohol content of 0.05% (3), and long daily working hours (12-hour shifts) have been associated with impairment of alertness and performance (4). This line of effects may link extended working hours to acute safety risks as well as possibly long-term health effects. In addition, reduced performance not only puts the person who works the long hours at risk, it could also have implications for the safety and health of others. Extended working hours could, for example, affect patient and traffic safety (5). In a study of nurses working 12-hour night shifts, almost all reported having at least one motor vehicle accident or near accident during the previous 12 months driving to or from work (6). Undoubtedly, there is an upper limit to how long it is possible to work as this is limited by the number of hours in the day and the need for recovery, and working hours has for long been a focus of regulation. Indeed regulation of working hours was part of the 1802 Health and Morals of Apprentices Act, which was the very first piece of factory legislation in the United Kingdom. It prevented apprentices in cotton mills from working at night and for >12 hours a day (7). Today, the Working Time Directive (2003/88/EC) regulates the duration of working hours in Europe (8). In this issue of Scandinavian Journal of Work, Environment and Health, Matre and colleagues (9) report the results of a highly welcomed systematic review and meta-analysis on extended working hours and safety incidents (accidents, near-accidents, safety incidents and injuries). They reviewed studies on extended working hours in terms of both daily and weekly working hours and found that both long daily (>12 hours) and weekly (>55 hours) working hours are associated with increased risk of safety incidents (9). The review adds to several other recent systematic reviews regarding extended weekly working hours performed by the World Health Organization, the International Labor Organization and the IPD-Work consortium, which have examined the associations between long working hours and primarily long-term health outcomes such as cardiovascular disease (10–14), cancer (15), type 2 diabetes (16), alcohol consumption (17) and depression (18, 19). From an epidemiological perspective, the analysis of extended working hours has advantages compared to analysis of many other work-related exposures. Working hours are relatively easy to measure. All employees can measure an hour objectively and with high precision. Duration of working hours can also be assessed with relatively high confidence by answering a single question. This makes the assessment of extended working hours attractive in general population surveys resulting in large cohorts with a standardized measure across jobs, gender, age, socioeconomic class and other contextual factors such as regional cultures. However, there are several methodological challenges that need to be solved in order to move the field forward. Importantly, as also pointed out by Matre and colleagues, extended work hours is highly intertwined with other measures of working hours such as short time between work shifts (quick returns) and working outside ordinary day work, eg, during the evening or night, which in itself has been associated with increased risks for health and safety. In addition, long work days do not necessarily imply long weekly working hours. The same weekly working hours can be achieved by having longer but fewer work days (compressed work weeks). Such compressed work weeks are advantageous in several ways. The lower number of work days gives employees more days without work. It decreases total commuting time – and the risks and environmental exposures associated thereof – and has been suggested as a means of reducing traffic congestion, although the effects are debated (20). Compressed work weeks are a well-established type of work schedule, eg, in the US Federal Government (21). It is therefore important to be able to disentangle the differences in risks by studying specific combinations of working hours. Another challenge is to determine whether the extended working hours are the actual cause of health safety outcomes. While there is a plausible link from extended working hours to accidents through lack of recovery and reduced performance (4), there may also be alternative mechanisms, in particular when it comes to more long-term effects. Extended weekly working hours, especially when related to overtime work, may be due to high workload. High workload may elicit feelings of being stressed at work, for example in the form of job strain that has been associated with increased risk of cardiovascular disease (22). In addition, long working hours imply longer exposure time to all other work environmental risk factors, such as chemical and physical exposures. Even if working time can be measured in a standardized way across different cultures and traditions, there are regional differences in what is considered the normal duration of working hours. For example, the proportion who worked >55 hours per week in 2016 was about 12% in South-East Asia and 3.5% in Europe (14). Furthermore, the reasons for extended working hours, may differ between countries and regions, and this may in turn modify the relationship between extended working hours and health. In countries with high social security and minimum wage, extended work hours is likely to be a more voluntary choice compared to countries or jobs where workers have extended working hours due to financial necessity. When it comes to measurement of working hours, Matre and colleagues (9) point to payroll-data as the golden standard. This is particularly true when the registration is linked to payment of wages, eg, in terms of overtime work. However, the validity of length of daily and weekly working hours is expected to be lower in jobs where unpaid extra working hours are more common (eg, among physicians and other academic personnel) and if hours from secondary jobs are not included (23). Even so, cohorts based on day-to-day registrations from payroll data have in recent years moved the field considerably forward due to highly detailed information on working hours for every day. The advantages for studies of associations between working hours and accidents include better information on temporality and decreased risk of misclassification. Thus, recent studies have analyzed associations of working hours the previous week(s) and risk of accidents on a given day (24, 25). The very detailed data are particularly important when analyzing the effect of extended working hours on accidents, since the effect must be assumed to be short term. Day-to-day registrations of payroll data also open the door to using more advanced designs, eg, case–crossover design (24, 26) that eliminates confounding by time-invariant variables. Finally, day-to-day payroll data allow detailed adjustment for other working hour characteristics and give the possibility to analyze duration of daily working hours in more detail. This will lead to more precise analyses on the association between working hours and the risk of work-related injuries and the potential for reducing the risk in the future. The author thanks Marie Aarrebo Jensen, Ann Dyreborg Larsen, and Helena Breth Nielsen for their thoughtful comments on the initial draft. References 1. ILO. Safety and health at work. Available from: https://www.ilo.org/global/topics/safety-and-health-at-work/lang--en/index.htm. Accessed June 15, 2021. 2. Eurostat Statistics Explained. Accidents at work statistics. Available from: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Accidents_at_work_statistics#Number_of_accidents. Accessed 16 June 16, 2021. 3. Dawson D, Reid K. Fatigue, alcohol and performance impairment. Nature. 1997;388(6639):235. https://doi.org/10.1038/40775 4. Axelsson J, Kecklund G, Akerstedt T, Lowden A. Effects of alternating 8- and 12-hour shifts on sleep, sleepiness, physical effort and performance. Scand J Work Environ Health. 1998;24 Suppl 3:62-8. 5. Rogers AE. The Effects of Fatigue and Sleepiness on Nurse Performance and Patient Safety. In: Hughes RG, editor. Patient Safety and Quality: An Evidence-Based Handbook for Nurses Rockville (MD): Agency for Healthcare Research and Quality (US); 2008. 6. Novak RD, Auvil-Novak SE. Focus group evaluation of night nurse shiftwork difficulties and coping strategies. Chronobiol Int. 1996;13(6):457-63. https://doi.org/10.3109/07420529609020916 7. UK Parliament. Early factory legislation. Available from: https://www.parliament.uk/about/living-heritage/transformingsociety/livinglearning/19thcentury/overview/earlyfactorylegislation. Accessed June 17, 2021 8. Directive 2003/88/EC of the European Parliament and of the Council of 4 November 2003 concerning certain aspects of the organisation of working time, (2003). Available from: https://eur-lex.europa.eu. 9. Matre D, Skogstad M, Sterud T, Nordy K-C, Knardahl S, Christensen JO, et al. Safety incidents associated with extended working hours. A systematic review and meta-analysis. Scand J Work Environ Health. 2021. Online first. https://doi.org/10.5271/sjweh.3958 10. Descatha A, Sembajwe G, Pega F, Ujita Y, Baer M, Boccuni F, et al. The effect of exposure to long working hours on stroke: A systematic review and meta-analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury. Environ Int. 2020;142:105746. https://doi.org/10.1016/j.envint.2020.105746 11. Li J, Pega F, Ujita Y, Brisson C, Clays E, Descatha A, et al. The effect of exposure to long working hours on ischaemic heart disease: A systematic review and meta-analysis from the WHO/ILO joint estimates of the work-related burden of disease and injury. Environ Int. 2020;142:105739. https://doi.org/10.1016/j.envint.2020.105739 12. Kivimaki M, Jokela M, Nyberg ST, Singh-Manoux A, Fransson EI, Alfredsson L, et al. Long working hours and risk of coronary heart disease and stroke: a systematic review and meta-analysis of published and unpublished data for 603,838 individuals. Lancet. 2015;386(10005):1739-46. https://doi.org/10.1016/S0140-6736(15)60295-1 13. Kivimaki M, Nyberg ST, Batty GD, Kawachi I, Jokela M, Alfredsson L, et al. Long working hours as a risk factor for atrial fibrillation: a multi-cohort study. Euro Heart J. 2017;38(34):2621-8. https://doi.org/10.1093/eurheartj/ehx324 14. Pega F, Náfrádi B, Momen NC, Ujita Y, Streicher KN, Prüss-Üstün AM, et al. Global, regional, and national burdens of ischemic heart disease and stroke attributable to exposure to long working hours for 194 countries, 2000-2016: A systematic analysis from the WHO/ILO Joint Estimates of the Work-related Burden of Disease and Injury. Environ Int. 2021;154:106595. https://doi.org/10.1016/j.envint.2021.106595 15. Heikkila K, Nyberg ST, Madsen IE, de Vroome E, Alfredsson L, Bjorner JJ, et al. Long working hours and cancer risk: a multi-cohort study. Brit J Cancer. 2016;114(7):813-8. https://doi.org/10.1038/bjc.2016.9 16. Kivimaki M, Virtanen M, Kawachi I, Nyberg ST, Alfredsson L, Batty GD, et al. Long working hours, socioeconomic status, and the risk of incident type 2 diabetes: a meta-analysis of published and unpublished data from 222 120 individuals. Lancet Diabetes Endocrinol. 2015;3(1):27-34. https://doi.org/10.1016/S2213-8587(14)70178-0 17. Pachito DV, Pega F, Bakusic J, Boonen E, Clays E, Descatha A, et al. The effect of exposure to long working hours on alcohol consumption, risky drinking and alcohol use disorder: A systematic review and meta-analysis from the WHO/ILO joint estimates of the work-related burden of disease and injury. Environ Int. 2021;146:106205. https://doi.org/10.1016/j.envint.2020.106205 18. Virtanen M, Jokela M, Madsen IE, Magnusson Hanson LL, Lallukka T, Nyberg ST, et al. Long working hours and depressive symptoms: systematic review and meta-analysis of published studies and unpublished individual participant data. Scand J Work Environ Health. 2018;44(3):239-50. https://doi.org/10.5271/sjweh.3712 19. Rugulies R, Sørensen K, Di Tecco C, Bonafede M, Rondinone BM, Ahn S, et al. The effect of exposure to long working hours on depression: A systematic review and meta-analysis from the WHO/ILO joint estimates of the work-related burden of disease and injury. Environ Int. 2021;155:106629. https://doi.org/10.1016/j.envint.2021.106629 20. Scott DM. Overcoming Traffic Congestion: A Discussion of Reduction Strategies and Behavioral Responses from a North American Perspective. Euro J Trans Infra Res. 2002;2(4). 21. POPM.gov [internet] Policy, Data, Oversight. Available from: https://www.opm.gov/policy-data-oversight/pay-leave/work-schedules/fact-sheets/alternative-work-schedules-compressed-work-schedules/. Accessed June 30, 2021. 22. Kivimäki M, Nyberg ST, Batty GD, Fransson EI, Heikkilä K, Alfredsson L, et al. Job strain as a risk factor for coronary heart disease: a collaborative meta-analysis of individual participant data. Lancet. 2012;380(9852):1491-7. https://doi.org/10.1016/S0140-6736(12)60994-5 23. Garde AH, Hansen J, Kolstad HA, Larsen AD, Pedersen J, Petersen JD, et al. Payroll data based description of working hours in the Danish regions. Chronobiol Int. 2018;35(6):795-800. https://doi.org/10.1080/07420528.2018.1466797 24. Harma M, Koskinen A, Sallinen M, Kubo T, Ropponen A, Lombardi DA. Characteristics of working hours and the risk of occupational injuries among hospital employees: a case-crossover study. Scand J Work Environ Health. 2020;46(6):570-8. https://doi.org/10.5271/sjweh.3905 25. Nielsen HB, Hansen AM, Conway SH, Dyreborg J, Hansen J, Kolstad HA, et al. Short time between shifts and risk of injury among Danish hospital workers: a register-based cohort study. 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OBJECTIVE: We performed a systematic review to assess potential consequences of extended working hours on accidents, near-accidents, safety incidents and injuries (incidents) by considering the overall certainty of evidence. METHODS: We searched five databases systematically (Medline, Embase, PsycINFO, Web of Science, and Proquest Health and safety Science Abstract) and identified 10072 studies published until December 2020. Twenty-two studies met the inclusion criteria. We followed a systematic approach to evaluate risk of bias and synthesize results in a meta-analysis. The certainty of evidence was determined by a modified version of The Grading of Recommendations Assessment, Development and Evaluation (GRADE). RESULTS: Our analyses indicated an association between working >12 hours/day (RR: 1.24, 95%CI: 1.11, 1.40), or working >55 hours/week (RR: 1.24, 95%CI: 0.98, 1.57), and elevated risk of incidents. The certainty of evidence evaluated as low. Weak or no associations were observed for other exposure contrasts: working >8 hours/day (RR: 0.93, 95%CI: 0.72, 1.19), or working overtime (RR: 1.08, 95%CI: 0.75, 1.55), working 41-48 hours/week (RR: 1.02, 95%CI: 0.92, 1.13) or 49-54 hours/week (RR: 1.02, 95%CI: 0.97, 1.07). The certainty of evidence was evaluated as low (very low for 41-48 hours/week). CONCLUSIONS: Daily working hours >12 hours and weekly working hours exceeding 55 hours was associated and increased risk of incidents. The level of evidence was low. Hence, further high-quality research is warranted to elucidate these associations.

Objective We performed a systematic review to assess potential consequences of extended working hours on accidents, near-accidents, safety incidents and injuries (hereafter 'incidents') by considering the overall certainty of evidence. Methods We searched five databases systematically (Medline, Embase, PsycINFO, Web of Science, and Proquest Health and Safety Science Abstracts) and identified 10 072 studies published up to December 2020, 22 of which met the inclusion criteria. We followed a systematic approach to evaluate risk of bias and synthesize results in a meta-analysis. The certainty of evidence was determined by a modified version of the Grading of Recommendations Assessment, Development and Evaluation (GRADE). Results Our analyses indicated an association between working >12 hours/day [relative risk (RR) 1.24, 95% confidence interval (CI) 1.11-1.40], or working >55 hours/week (RR 1.24, 95% I 0.98-1.57), and elevated risk of incidents. The certainty of evidence evaluated as low. Weak or no associations were observed for other exposure contrasts: working >8 hours/day (RR 0.93, 95% CI 0.72-1.19), or working overtime (RR 1.08, 95% CI 0.75-1.55), working 41-48 hours/week (RR 1.02, 95% CI 0.92-1.13) or 49-54 hours/week (RR 1.02, 95% CI 0.97-1.07). The certainty of evidence was evaluated as low (very low for 41-48 hours/week). Conclusions Daily working hours >12 hours and weekly working hours >55 hours was associated with an increased risk of incidents. The certainty of evidence was low. Hence, further high-quality research is warranted to elucidate these associations. [ABSTRACT FROM AUTHOR]

OBJECTIVES: This systematic review and meta-analysis combined published study-level data and unpublished individual-participant data with the aim of quantifying the relation between long working hours and the onset of depressive symptoms. METHODS: We searched PubMed and Embase for published prospective cohort studies and included available cohorts with unpublished individual-participant data. We used a random-effects meta-analysis to calculate summary estimates across studies. RESULTS: We identified ten published cohort studies and included unpublished individual-participant data from 18 studies. In the majority of cohorts, long working hours was defined as working ≥55 hours per week. In multivariable-adjusted meta-analyses of 189 729 participants from 35 countries [96 275 men, 93 454 women, follow-up ranging from 1–5 years, 21 747 new-onset cases), there was an overall association of 1.14 (95% confidence interval (CI) 1.03–1.25] between long working hours and the onset of depressive symptoms, with significant evidence of heterogeneity (I^2=45.1%, P=0.004). A moderate association between working hours and depressive symptoms was found in Asian countries (1.50, 95% CI 1.13–2.01), a weaker association in Europe (1.11, 95% CI 1.00–1.22), and no association in North America (0.97, 95% CI 0.70–1.34) or Australia (0.95, 95% CI 0.70–1.29). Differences by other characteristics were small. CONCLUSIONS: This observational evidence suggests a moderate association between long working hours and onset of depressive symptoms in Asia and a small association in Europe.

Objectives This systematic review and meta-analysis combined published study-level data and unpublished individual-participant data with the aim of quantifying the relation between long working hours and the onset of depressive symptoms. Methods We searched PubMed and Embase for published prospective cohort studies and included available cohorts with unpublished individual-participant data. We used a random-effects meta-analysis to calculate summary estimates across studies. Results We identified ten published cohort studies and included unpublished individual-participant data from 18 studies. In the majority of cohorts, long working hours was defined as working ≥55 hours per week. In multivariable-adjusted meta-analyses of 189 729 participants from 35 countries [96 275 men, 93 454 women, follow-up ranging from 1-5 years, 21 747 new-onset cases), there was an overall association of 1.14 (95% confidence interval (CI) 1.03-1.25] between long working hours and the onset of depressive symptoms, with significant evidence of heterogeneity (I2=45.1%, P=0.004). A strong association between working hours and depressive symptoms was found in Asian countries (1.50, 95% CI 1.13-2.01), a weaker association in Europe (1.11, 95% CI 1.00-1.22), and no association in North America (0.97, 95% CI 0.70-1.34) or Australia (0.95, 95% CI 0.70-1.29). Differences by other characteristics were small. Conclusions This observational evidence suggests a moderate association between long working hours and onset of depressive symptoms in Asia and a small association in Europe. [ABSTRACT FROM AUTHOR]

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 Medline 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.

OBJECTIVES: Associations between psychosocial work environment and excess weight have not been systematically addressed. The aim of this systematic review was to summarize the published evidence for the associations of psychosocial factors at work and long work hours with weight-related outcomes . Methods We conducted a search of Medline and Embase for all original articles published up to September 2012 using predefined keywords. After excluding studies with a definite selection bias, we included 39 articles. RESULTS: About 60% of the studies reported at least one positive association between psychosocial factors at work and a weight-related outcome. However, 76% of the tested associations were found to be non-significant. Furthermore, the associations were rather weak. Studies of higher quality tended to observe associations more often than those of lower quality. Positive associations were found more frequently (i) among women versus men, (ii) in cross-sectional versus longitudinal studies, and (iii) for overweight or obesity versus other outcomes. About 70% of the studies reported positive associations between long work hours and weight-related outcomes. All four studies that evaluated the association between working overtime and weight gain (three longitudinal and one cross-sectional), showed a positive association among men and two of them also observed associations among women. CONCLUSIONS: We found evidence for weak associations between psychosocial factors at work and excess weight. Associations were observed between long work hours, working overtime, and weight gain, especially among men. More cohort studies among non-obese baseline participants using appropriate analytical methods based on an elaborated hypothetical model are needed.

OBJECTIVE: Even though overtime work has been suspected to be a risk factor for ill health, little research has been done to determine the underlying immunological mechanisms. This study investigated the association between overtime work and cellular immunity among Japanese white-collar workers. METHODS: A total of 306 healthy, full-time, non-shift, daytime employees (165 men and 141 women), aged 22–69 (mean 36) years, provided a blood sample for the measurement of circulating immune [natural killer (NK), B, and T] cells and NK cell cytotoxicity (NKCC) and completed a questionnaire survey including overtime/month. Blood samples were collected between 09.00–11.00 hours during working days and participants completed the questionnaire within the two weeks prior to the blood sampling. Stepwise linear regression analyses controlling for confounders were carried out to examine the relationship between overtime work and immune markers. RESULTS: Overtime work was mainly related to short sleep duration, increased weight, and reduced job satisfaction, and it was more prevalent among men than women and among younger and married employees. Amount of overtime was inversely associated with NK (CD3-CD56+) cell counts (β=-0.145; P =0.032) but was not associated with NKCC, NKCC/NK cell ratio, or T or B cells. CONCLUSIONS: The NK cell is a lymphocyte that possesses killer activity against tumor and virus-infected cells and constitutes a major component of the innate immune system. A decrease of NK cell counts from overtime work suggests a dampened innate immune defense. However, the finding needs to be further validated with a well-designed study using objective overtime measures.

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 Medline and PsycINFO databases between 1995-2012. Resil Its 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. [ABSTRACT FROM AUTHOR]

Objective Even though overtime work has been suspected to be a risk factor for ill health, little research has been done to determine the underlying immunological mechanisms. This study investigated the association between overtime work and cellular immunity among Japanese white-collar workers. Methods A total of 306 healthy, full-time, non-shift, daytime employees (165 men and 141 women), aged 22-69 (mean 36) years, provided a blood sample for the measurement of circulating immune [natural killer (NK), B, and T] cells and NK cell cytotoxicity (NKCC) and completed a questionnaire survey including overtime/month. Blood samples were collected between 09.00-11.00 hours during working days and participants completed the questionnaire within the two weeks prior to the blood sampling. Stepwise linear regression analyses controlling for confounders were carried out to examine the relationship between overtime work and immune markers. Results Overtime work was mainly related to short sleep duration, increased weight, and reduced job satisfaction, and it was more prevalent among men than women and among younger and married employees. Amount of overtime was inversely associated with NK (CD3-CD56+) cell counts (β=-0.145; P =0.032) but was not associated with NKCC, NKCC/NK cell ratio, or T or B cells.Conclusions The NK cell is a lymphocyte that possesses killer activity against tumor and virus-infected cells and constitutes a major component of the innate immune system. A decrease of NK cell counts from overtime work suggests a dampened innate immune defense. However, the finding needs to be further validated with a well-designed study using objective overtime measures. [ABSTRACT FROM AUTHOR]

The article presents a diary study to open up the black box of overtime work among university faculty members. It aims at opening un the black box of overtime work among university faculty members. It provides information on when faculty members work overtime, what activities are undertaken during overtime, and how overtime is experienced.

The article presents a study on the distinction between overtime hours and long workhours on full-time and part-time workers. The results showed that there is no correlation between overtime and fatigue for both full-time and part-time employees. However, it was shown on the findings that overtime has a significant effect on higher work motivation for full-time workers but does not influence the group of part-time workers.