Shift Work and Lifestyle factors: A 6-year follow-Up Study Among Nurses

Objectives: To evaluate different work schedules, short rest time between shifts (quick returns), and night shift exposure for their possible adverse effects on different lifestyle factors in a 6-year follow-up study. Methods: Data stemmed from “The Survey of Shiftwork, Sleep and Health,” a cohort study of Norwegian nurses started in 2008/9. The data analyzed in this sub-cohort of SUSSH were from 2008/9 to 2015 and consisted of 1,371 nurses. The lifestyle factors were: Exercise (≥1 h/week, <1 h/week), caffeine consumption (units/day), smoking (prevalence and cigarettes/day), and alcohol consumption (AUDIT-C score). We divided the nurses into four groups: (1) day workers, (2) night workers, (3) nurses who changed toward, and (4) nurses who changed away from a schedule containing night shifts. Furthermore, average number of yearly night shifts (NN), and average number of quick returns (QR) were calculated. Paired t-tests, McNemar tests, and logistic regression analyses were used in the analyses. Results: We found a significant increase in caffeine consumption across all work schedule groups and a decline in smoking prevalence for day workers and night workers at follow-up. Analyses did not show any significant differences between groups when analyzing (1) different work schedules, (2) different exposures to QR, (3) different exposures to NN on the respective lifestyle factor trajectories. Conclusion: We found no significant differences between the different work schedule groups or concerning different exposures to QR or NN when evaluating these lifestyle factor trajectories. This challenges the notion that shift work has an adverse impact on lifestyle factors.publishedVersio

Shift work: Weight change and lifestyle factors

Our society is dependent on 24hour services: healthcare, transportation, law enforcement, and fire services, as well as an ever-expanding service sector. The employees who contribute to these 24hour services, collectively called shift workers, suffer the cost of working outside regular daytime hours. Beyond acute health effects such as disturbed sleep and disrupted circadian rhythms, research has unveiled adverse long-term health consequences of shift work, for example, in terms of cardiometabolic health. In this thesis, we examine possible effects of shift work on body-weight related outcomes and lifestyle factors: smoking habits, alcohol consumption, caffeine consumption, and exercise habits. The data used in all three papers stems from The SUrvey of Shift work, Sleep and Health (SUSSH). SUSSH is a large cohort of Norwegian nurses that was initiated in late 2008. The overall aim of SUSSH was to examine possible adverse health consequences of shift work. In paper 1, we investigated possible associations between cumulative night shift exposure and adverse consequences to body weight and lifestyle factors. The cross-sectional data, consisting of 2059 nurses, was extracted from the first wave of SUSSH. The number of self-reported night shifts worked last year (NNL) was used as an operationalization of night work load. Body Mass Index (BMI), obesity (BMI>30), smoking habits, alcohol consumption (Alcohol Use Disorders Identification Test Consumption (AUDIT-C)), caffeine consumption and exercise habits were used as outcome variables and analyzed separately. NNL was found to be significantly and positively associated with BMI, both when evaluated against BMI as a continuous parameter (β =0.055, p<0.05), and when evaluated against obesity (OR=1.01(1.00-1.01)). The AUDIT-C score was found to be significantly and positively associated with hours worked per week, but not with NNL. In paper 2, we build on the main finding from paper 1 and investigated prospective changes in BMI between nurses in different work schedules and changes in BMI and differences in cumulative night shift exposure over a four-year follow-up period. Nurses (n=1244) who reported their work schedules at both baseline and follow-up were included; pregnant nurses at baseline or follow-up were excluded. The shift schedules included were: day-only, two-shift rotation (day and evening shifts), three-shift rotation (day, evening and night shifts), night-only, those who changed to schedule containing night shifts, and those who changed away from schedules containing night shifts. We found that night-only workers, two-shift workers, three-shift workers, and those who changed work schedule away from- or towards night work all had significant BMI gain during the follow-up period. Day-only workers had a non-significant BMI gain. In our multiple linear regression model, we found that night-only workers had significantly larger BMI gain compared to day-only workers (β=0.89 (0.06-1.72)), p<0.05). We did not find any significant association between average yearly number of night shifts (NNs) and BMI using our regression model. Overall, we concluded that night-only workers had significantly larger weight gain than day-only workers. Paper 3 builds on paper 1 in terms of lifestyle factors. In paper 3, we addressed the relationship between shift work and lifestyle factors using a prospective design with six-year follow-up. This subcohort of nurses consisted of 1371 nurses. Different work schedule groups (day workers, night workers, workers starting- and stopping working night shifts), quick returns (≤11h between consecutive shifts; QRs), and NNs were evaluated for their possible effects on changes in caffeine consumption, smoking habits, alcohol (AUDIT-C), and exercise habits. Day workers and the groups with the lowest exposure to QRs (<5) and NNs (<1) were used as contrasts in the respective analyses. A significant increase in caffeine consumption was found across all work schedule groups. Furthermore, declines in smoking prevalence were found among all groups, although they were not significant for those who changed work schedules towards- or away from night shifts. Day workers had a significant increase in the AUDIT-C score. No work schedules were associated with changes in exercise habits. However, our main finding was negative: we did not find any significant between-group differences regarding work schedules, QRs, or NNs on any of the lifestyle factor trajectories. In conclusion, we investigated different characteristics of shift work and changes to body-weight in paper 1 and paper 2. Our findings suggest that night work contributes to weight gain. However, paper 2 failed to replicate paper 1 in terms of a clear dose-response relationship between cumulative night shift exposure and weight gain. The overall conclusion from paper 1 and paper 3, contrary to our a priori hypothesis, is that we did not find any large differences between different lifestyle factors and particular shift work characteristics. Our findings challenge the hypothesis often supported by models trying to elucidate causal pathways between shift work and adverse health consequences. These models often incorporate lifestyle and behavioral factors along a potential causal pathway in conjunction with circadian disruption and insufficient sleep

Shift Work and Lifestyle factors: A 6-year follow-Up Study Among Nurses

Objectives: To evaluate different work schedules, short rest time between shifts (quick returns), and night shift exposure for their possible adverse effects on different lifestyle factors in a 6-year follow-up study. Methods: Data stemmed from “The Survey of Shiftwork, Sleep and Health,” a cohort study of Norwegian nurses started in 2008/9. The data analyzed in this sub-cohort of SUSSH were from 2008/9 to 2015 and consisted of 1,371 nurses. The lifestyle factors were: Exercise (≥1 h/week, <1 h/week), caffeine consumption (units/day), smoking (prevalence and cigarettes/day), and alcohol consumption (AUDIT-C score). We divided the nurses into four groups: (1) day workers, (2) night workers, (3) nurses who changed toward, and (4) nurses who changed away from a schedule containing night shifts. Furthermore, average number of yearly night shifts (NN), and average number of quick returns (QR) were calculated. Paired t-tests, McNemar tests, and logistic regression analyses were used in the analyses. Results: We found a significant increase in caffeine consumption across all work schedule groups and a decline in smoking prevalence for day workers and night workers at follow-up. Analyses did not show any significant differences between groups when analyzing (1) different work schedules, (2) different exposures to QR, (3) different exposures to NN on the respective lifestyle factor trajectories. Conclusion: We found no significant differences between the different work schedule groups or concerning different exposures to QR or NN when evaluating these lifestyle factor trajectories. This challenges the notion that shift work has an adverse impact on lifestyle factors

Associations between night work and BMI, alcohol, smoking, caffeine and exercise - A cross-sectional study

Background: Shift work is associated with negative health effects. Increased prevalence of several cardiovascular risk factors among shift workers/night workers compared with day workers have been shown resulting in increased risk of cardiovascular events among shift workers and night workers. Previous studies have taken a dichotomous approach to the comparison between day and night workers. The present study uses a continuous approach and provides such a new perspective to the negative effects of night work load as a possible risk factor for undesirable health effects. Methods: This cross sectional study (The SUrvey of Shift work, Sleep and Health (SUSSH)) uses data collected from December 2008 to March 2009. The study population consists of Norwegian nurses. The study collected information about demographic and lifestyle factors: Body Mass Index (BMI), smoking habits, alcohol consumption, caffeine consumption and exercise habits. The lifestyle parameters were evaluated using multiple hierarchical regression and binary logistic regression. Number of night shifts worked last year (NNL) was used as operationalization of night work load. Adjustment for possible confounders were made. Obesity was defined as BMI > 30. Alcohol Consumption was evaluated using the short form of the Alcohol Use Disorders Identification Test Consumption (AUDIT-C). Data were analyzed using SPSS version 22. Results: We had data from 2059 nurses. NNL was significantly and positively associated with BMI, both when evaluated against BMI as a continuous parameter (Beta = .055, p < .05), and against obesity (OR = 1.01, 95 % CI = 1.00-1.01). The AUDIT-C score was significantly and positively associated with hours worked per week (OR = 1.03, 95 % CI = 1.01-1.05). Conclusions: We found a positive significant association between night work load and BMI. This suggests that workers with a heavy night work load might need special attention and frequent health checks due to higher risk of undesirable health effects

Sleep patterns among Norwegian nurses between the first and second wave of the COVID-19 pandemic

Background Nurses are in the frontline and play an important role in the battle against the COrona VIrus Disease-2019 (COVID-19) pandemic. Sleep problems among health care workers are likely to increase due to the pandemic. However, it is conceivable that negative health outcomes related to the pandemic fluctuate with the infection rate waves of the pandemic. The present study aimed to investigate sleep patterns among Norwegian nurses, after the first wave, during a period with very low rates of COVID-19. Methods Data stemmed from the cohort study “SUrvey of Shift work, Sleep and Health (SUSSH)” among Norwegian nurses. A total of 1532 nurses responded one time to a questionnaire between June and September in 2020 including items about demographics and work, information about COVID-19 and quarantine, sleep patterns and changes in sleep patterns due to the pandemic. Descriptive statistics for all relevant variables were calculated and McNemar tests were used to compare categorical variables. Results The majority of nurses (84.2%) reported no change in sleep duration after the first wave of the COVID-19 pandemic compared to before, 11.9% reported less sleep, and 3.9% reported more sleep. Similarly, 82.4% of the nurses reported no change in their sleep quality, whereas 16.2% of the nurses reported poorer sleep quality after the first wave of the pandemic compared to before. The majority of nurses reported no change in their sleep schedule due to the pandemic, although 9.6% of the nurses reported to go to bed later and 9.0% woke up earlier than before the pandemic. Conclusions Most existing literature exploring sleep among health care workers during the COVID-19 pandemic has been carried out during periods with high infection rates. In this study we aimed to investigate sleep patterns among Norwegian nurses following the first wave, during a period of low COVID-19 rates in Norway. Most of the nurses reported no change in neither sleep duration, sleep quality, bedtime, nor wake-up times compared to before the pandemic. Still, nearly 12% reported shorter sleep duration, and about 16% reported poorer sleep quality indicating that some nurses experienced worsening of their sleep following the pandemic

A longitudinal study on the association between quick returns and occupational accidents

Objective: This study aimed to investigate how change in the number of quick returns [(QR) <11 hours between consecutive shifts] longitudinally is associated with risk of occupational accidents among nurses. Methods: Two-year follow-up data from 1692 nurses participating in the Survey of Shiftwork, Sleep and Health among Norwegian nurses (SUSSH) (mean age 40.2, standard deviation 8.3 years, 91% female) were used. Negative binomial regression analyses were conducted to investigate the association between changes in the number of QR after two years and occupational accidents, controlling for demographics, work factors, and occupational accidents at baseline. Results: An increase from having no or a moderate number of QR (1–34 per year) from baseline to the two-year follow-up assessment was associated with an increased risk of occupational accidents, compared to experiencing no change in the number of QR. Those with a moderate number of QR at baseline who experienced an increase after two years had an increased risk of causing harm to patients/others [incident rate ratio (IRR) 8.49, 95% confidence interval (CI) 2.79–25.87] and equipment at work (IRR 2.89, 95% CI 1.13–7.42). Those who had many QR (>34 per year) at baseline but experienced a reduction after two years had a reduced risk of causing harm to themselves (IRR 0.35, 95% CI 0.16–0.73) and patients/others (IRR 0.27, 95% CI 0.12–0.59). Conclusion: A fairly consistent pattern was demonstrated in which changes in the number of QR over the two-year follow-up period was associated with a corresponding change in the risk of occupational accidents

The association between shift work and immunological biomarkers in nurses

Objectives: Shift work is associated with several negative health effects. The underlying pathophysiological mechanisms are unclear, but low-grade inflammation has been suggested to play a role. This project aimed to determine whether levels of immunological biomarkers differ depending on work schedule, self-reported sleep duration, self-reported sleep quality, and presence of shift work disorder (study 1). Furthermore, we aimed to determine whether these biomarkers differ after a night of sleep vs. at the end of a night or a day shift (study 2). Methods: In study 1, 390 nurses provided blood samples after a night of sleep with the dried blood spot method. In study 2, a subset of 55 nurses also provided blood samples after a day shift and after a night shift. The following biomarkers were measured: interleukin-1alpha, interleukin-1beta, interleukin-4, interleukin-6, interleukin-8, interleukin-10, interleukin-13, monocyte chemoattractant protein-1, interferon-gamma, and tumor necrosis factor-alpha. Multiple linear regressions with adjustment for age, sex and body mass index (study 1) and ANOVAs with repeated measures (study 2) were conducted. Results: In study 1, neither work schedule, number of night shifts, number of quick returns (<11 h between consecutive shifts), sleep duration, poor sleep quality, nor shift work disorder were systematically associated with most of these biomarkers. Compared with day only work, day-evening work was associated with higher levels of IL-1alpha and IL-13, quick returns were associated with higher levels of IL-1beta and MCP-1, short sleep duration (<6 h) was associated with lower levels of IL-1beta and higher levels of TNF-alpha, and long sleep duration (8+ h) was associated with higher levels of IL-13. In study 2, IL-1beta levels were higher (large effect size) both after a day shift (14% increase) and a night shift (75% increase) compared with levels after a night of sleep. Similarly, TNF-alpha levels were higher (moderate-large effect size) after a day shift (50% increase) compared to after a night of sleep. In contrast, MCP-1 levels were lower (large effect size) both after a day shift (22% decrease) and a night shift (12% decrease) compared with after a night of sleep. Conclusions: We found some indications that shift work influenced immunological biomarkers. The results should be interpreted with caution due to limitations, e.g., related to the sampling procedure and to low levels of biomarkers in the blood samples

Changes in work schedule affect the prevalence of shift work disorder among Norwegian nurses–a two year follow-up study

This study aimed to explore how changes in the work schedule would affect the prevalence of Shift Work Disorder (SWD) over time. Two-year follow-up data from 1076 nurses participating in the longitudinal SUrvey of Shift work, Sleep and Health among Norwegian nurses (SUSSH) were included in the study. The questionnaires included measures of work-related factors, i.e., work schedule and numbers of night shifts and quick returns (QRs) worked the last year, as well as questions related to SWD according to the ICSD-3 diagnostic criteria at both baseline and at 2-year follow-up. Data were analyzed with paired samples t-tests, chi-square tests, and logistic regression analyses adjusting for sex and age. Terminating night work was the strongest predictor for recovering from SWD from baseline to follow-up (OR 10.91, 95% CI 6.11–19.46). Additionally, changing the work schedule from day work to night work from baseline to follow-up was the strongest predictor for developing SWD in the same period (OR 4.75, 95% CI 2.39–9.47). Reductions in number of nights (more than 10) and QRs (more than 10) worked the last year were associated with recovering from SWD between baseline and follow-up. Nurses who recovered from SWD had significantly reduced the mean number of night shifts worked the last year from 32.3 at baseline to 20.4 at follow-up (p = .001). Furthermore, an increase of more than 10 nights or more than 10 QRs worked the last year between baseline and follow-up predicted developing SWD. Nurses developing SWD between baseline and follow-up had significantly increased the mean number of nights worked the last year from 25.8 at baseline to 31.0 at follow-up (p =-.043). Changes in night work exposure were the strongest predictors for both recovering from or developing SWD from baseline to follow-up. Reducing exposure to night work and QRs were associated with recovering from SWD and increasing exposure to night work and QRs were associated with developing SWD. The results imply that unfavorable work schedules play a role in the development of sleep problems among nurses. These results may be useful when designing healthy working schedules