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.

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.

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.

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.

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.

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.

Shift work schedule and night work load: Effects on body mass index - a four-year longitudinal study.

Objectives The aim of this study was to investigate changes in body mass index (BMI) between different work schedules and different average number of yearly night shifts over a four-year follow-up period. Methods A prospective study of Norwegian nurses (N=2965) with different work schedules was conducted: day only, two-shift rotation (day and evening shifts), three-shift rotation (day, evening and night shifts), night only, those who changed towards night shifts, and those who changed away from schedules containing night shifts. Paired student's t-tests were used to evaluate within subgroup changes in BMI. Multiple linear regression analysis was used to evaluate between groups effects on BMI when adjusting for BMI at baseline, sex, age, marital status, children living at home, and years since graduation. The same regression model was used to evaluate the effect of average number of yearly night shifts on BMI change. Results We found that night workers [mean difference (MD) 1.30 (95% CI 0.70-1.90)], two shift workers [MD 0.48 (95% CI 0.20-0.75)], three shift workers [MD 0.46 (95% CI 0.30-0.62)], and those who changed work schedule away from [MD 0.57 (95% CI 0.17-0.84)] or towards night work [MD 0.63 (95% CI 0.20-1.05)] all had significant BMI gain (P<0.01) during the follow-up period. However, day workers had a non-significant BMI gain. Using adjusted multiple linear regressions, we found that night workers had significantly larger BMI gain compared to day workers [B=0.89 (95% CI 0.06-1.72), P<0.05]. We did not find any significant association between average number of yearly night shifts and BMI change using our multiple linear regression model. Conclusions After adjusting for possible confounders, we found that BMI increased significantly more among night workers compared to day workers.