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Objective: This study investigates the associations between working from home and the presence of MSP during the COVID-19 pandemic. Working from home often involves a lot of sedentary computer screen work and the home working environment might not be optimally equipped, which can lead to health problems, including musculoskeletal pain (MSP). Methods: Longitudinal data from 16 questionnaire rounds of the Lifelines COVID-19 cohort during the first year of the COVID-19 pandemic (March 2020-February 2021) were used. In total, 40,702 Dutch workers were included. In every round, participants reported whether they worked on location, from home, or hybrid. Logistic Generalized Estimating Equations were used to study the association of work situation with the presence of MSP and the presence of severe MSP. Results: Working from home was associated with higher risks of having MSP in the lower back (OR: 1.05, 95% CI 1.02–1.08), in the upper back (OR: 1.24, 95% CI 1.18–1.31), and in the neck, shoulder(s) and/or arm(s) (OR: 1.18, 95% CI 1.13–1.22). Hybrid working was associated with higher risks of having pain in the upper back (OR: 1.09, 95% CI 1.02–1.17) and in the neck, shoulder(s) and/or arm(s) (OR: 1.14, 95% CI 1.09–1.20). Both home and hybrid workers had higher risks of severe MSP in the different body areas. Conclusion: Home workers, and to a smaller extent hybrid workers, had higher risks of having MSP than location workers during the first year of the COVID-19 pandemic. The results indicate the importance of measures to prevent MSP in future policies involving working from home.

Night-shift workers experience disturbances of their circadian rhythm and sleep, which may make them more susceptible to infectious diseases. Therefore, we studied whether night-shift workers are at higher risk of testing positive for SARS-CoV-2 infection than day workers. In this prospective study, data were used from 20 questionnaire rounds of the Dutch Lifelines COVID-19 cohort that was initiated in March 2020. In the different questionnaire rounds, 2285 night-shift workers and 23,766 day workers reported whether they had tested positive for SARS-CoV-2. Cox proportional hazards regression models adjusted for demographic, work, and health covariates were used to compare SARS-CoV-2 incidence between night-shift and day workers. From March 2020-January 2021, 3.4% of night-shift workers and 2.2% of day workers reported to have tested positive for SARS-CoV-2 (p < .001). After adjustment for covariates, night-shift workers had a 37% higher risk of testing positive for SARS-CoV-2 (hazard ratio: 1.37, 95% confidence interval: 1.05-1.77). In this study, we show that night-shift workers were more likely to test positive for SARS-CoV-2 than day workers, which adds to the growing evidence that night-shift work may influence the complex processes involved in infection susceptibility. Further mechanistic insight is needed to understand the relation between night-shift work and (SARS-CoV-2) infection susceptibility.

This study aimed to compare sickness absenteeism, work performance, and healthcare use due to respiratory infections, as well as general sickness absenteeism and work performance between shift and non-shift workers. In this study, 589 shift and non-shift workers employed in hospitals were included. For 6 months, participants kept a daily record of their influenza-like illness/acute respiratory infection (ILI/ARI) symptoms using a diary application. After an episode of ILI/ARI symptoms ended, participants (n = 531) were questioned about their sickness absenteeism (occurrence and duration in hours), work performance (on a 10 point scale), and healthcare use during the ILI/ARI episode. At the end of the 6 months follow-up, participants (n = 498) were also asked about general sickness absenteeism and work performance in the past 4 weeks. Mixed-model and regression analyses were used to compare absenteeism, work performance, and healthcare use between shift and non-shift workers. No differences were found in sickness absenteeism [Odds Ratio (OR) = 1.00 (95%‒Confidence Interval (CI): 0.61‒1.64)] and work performance [Regression coefficient (B) = −0.19 (95%‒CI: −0.65‒0.26)] due to ILI/ARI between shift and non-shift workers. In addition, healthcare use due to ILI/ARI was similar between shift and non-shift workers. Furthermore, similar general sickness absenteeism rates and work performance levels were found between shift and non-shift workers. As this is the first study that examined the associations with shift work due to ILI/ARI, further studies are needed to confirm our findings.

Objectives Shift work may be associated with an increased incidence of respiratory infections. However, underlying mechanisms are unclear. Therefore, our aim was to examine the mediating role of sleep, physical activity, and diet in the association between shift work and respiratory infections. Methods This prospective cohort study included 396 shift and non-shift workers employed in hospitals. At base-line, sleep duration and physical activity were measured using actigraphy and sleep/activity diaries, sleep quality was reported, and frequency of meal and snack consumption was measured using food diaries. In the following six months, participants used a smartphone application to report their influenza-like illness/acute respiratory infection (ILI/ARI) symptoms daily. Mediation analysis of sleep, physical activity, and diet as potential mediators of the effect of shift work on ILI/ARI incidence rate was performed using structural equation modeling with negative binomial and logistic regression. Results Shift workers had a 23% [incidence rate ratio (IRR) 1.23, 95% CI 1.01–1.49] higher incidence rate of ILI/ARI than non-shift workers. After adding the potential mediators to the model, this reduced to 15% (IRR 1.15, 95% CI 0.94–1.40). The largest mediating (ie, indirect) effect was found for poor sleep quality, with shift workers having 29% more ILI/ARI episodes via the pathway of poorer sleep quality (IRR 1.29, 95% CI 1.02–1.95). Conclusions Compared to non-shift workers, shift workers had a higher incidence rate of ILI/ARI that was partly mediated by poorer sleep quality. Therefore, it may be relevant for future research to focus on perceived sleep quality as an underlying mechanism in the relation between shift work and increased infection susceptibility.

Background Worldwide, socioeconomic differences in health and use of healthcare resources have been reported, even in countries providing universal healthcare coverage. However, it is unclear how large these socioeconomic differences are for different types of care and to what extent health status plays a role. Therefore, our aim was to examine to what extent healthcare expenditure and utilization differ according to educational level and income, and whether these differences can be explained by health inequalities. Methods Data from 18,936 participants aged 25–79 years of the Dutch Health Interview Survey were linked at the individual level to nationwide claims data that included healthcare expenditure covered in 2017. For healthcare utilization, participants reported use of different types of healthcare in the past 12 months. The association of education/income with healthcare expenditure/utilization was studied separately for different types of healthcare such as GP and hospital care. Subsequently, analyses were adjusted for general health, physical limitations, and mental health. Results For most types of healthcare, participants with lower educational and income levels had higher healthcare expenditure and used more healthcare compared to participants with the highest educational and income levels. Total healthcare expenditure was approximately between 50 and 150 % higher (depending on age group) among people in the lowest educational and income levels. These differences generally disappeared or decreased after including health covariates in the analyses. After adjustment for health, socioeconomic differences in total healthcare expenditure were reduced by 74–91 %. Conclusions In this study among Dutch adults, lower socioeconomic status was associated with increased healthcare expenditure and utilization. These socioeconomic differences largely disappeared after taking into account health status, which implies that, within the universal Dutch healthcare system, resources are being spent where they are most needed. Improving health among lower socioeconomic groups may contribute to decreasing health inequalities and healthcare spending.

Becoming divorced or widowed are stressful life events experienced by a substantial part of the population. While marital status is a significant predictor in many studies on healthcare expenditures, effects of a change in marital status, specifically becoming divorced or widowed, are less investigated. This study combines individual health claims data and registered sociodemographic characteristics from all Dutch inhabitants (about 17 million) to estimate the differences in healthcare expenditure for individuals whose marital status changed (n = 469,901) compared to individuals who remained married, using propensity score matching and generalized linear models. We found that individuals who were (long-term) divorced or widowed had 12–27% higher healthcare expenditures (RR = 1.12, 95% CI 1.11–1.14; RR = 1.27, 95% CI 1.26–1.29) than individuals who remained married. Foremost, this could be attributed to higher spending on mental healthcare and home care. Higher healthcare expenditures are observed for both divorced and widowed individuals, both recently and long-term divorced/widowed individuals, and across all age groups, income levels and educational levels.

Due to the wealth of exposome data from longitudinal cohort studies that is currently available, the need for methods to adequately analyze these data is growing. We propose an approach in which machine learning is used to identify longitudinal exposome-related predictors of health, and illustrate its potential through an application. Our application involves studying the relation between exposome and self-perceived health based on the 30-year running Doetinchem Cohort Study. Random Forest (RF) was used to identify the strongest predictors due to its favorable prediction performance in prior research. The relation between predictors and outcome was visualized with partial dependence and accumulated local effects plots. To facilitate interpretation, exposures were summarized by expressing them as the average exposure and average trend over time. The RF model’s ability to discriminate poor from good self-perceived health was acceptable (Area-Under-the-Curve = 0.707). Nine exposures from different exposome-related domains were largely responsible for the model’s performance, while 87 exposures seemed to contribute little to the performance. Our approach demonstrates that ML can be interpreted more than widely believed, and can be applied to identify important longitudinal predictors of health over the life course in studies with repeated measures of exposure. The approach is context-independent and broadly applicable.

Objectives The objective of this study is to examine the mediating role of unhealthy behaviors and body mass index (BMI) in the relationship between high job strain and self-rated poor health in workers with a low education. Methods A total of 8369 low educated workers, who participated in the Lifelines cohort study during the period 2012–2017, were included. Self-reported job strain, health behaviors (smoking, physical activity, and fruit and vegetable consumption), and BMI were assessed at baseline, and self-rated health after 2 years. To assess mediation by the health behaviors and BMI, structural equation modeling with logistic and multinomial regression analyses were performed. Results Workers with high job strain had a higher odds of poor health (OR 1.34; 95% CI 1.13–1.60) compared to those with low job strain. Workers with high job strain were more likely to have a lack of physical activity (OR 1.14; 95% CI 1.01–1.28), but were not more likely to smoke, to be overweight or obese, or to have a low fruit or vegetable consumption. Workers who smoke, have a lack of physical activity or are overweight or obese are more likely to report poor health (OR 1.37; 95% CI 1.16–1.60, OR 1.25; 95% CI 1.08–1.43, OR 1.37; 95% CI 1.16–1.61, OR 2.25; 95% CI 1.86–2.72). Indirect (mediating) effects of unhealthy behaviors and BMI in the relationship between high job strain and poor health were small and not statistically significant. Conclusions No mediating effects of unhealthy behaviors or BMI were found in the relationship between high job strain and self-rated poor health among workers with a low educational level.

Purpose Shift work has been related to obesity and diabetes, but the potential mediating role of lifestyle is yet unknown. Our aim was to investigate this mediating role of physical activity, diet, smoking, and sleep quality in the relationships between shift work, and obesity and diabetes. Methods In this cross-sectional study, 3188 shift workers and 6395 non-shift workers participated between 2013 and 2018 in periodical occupational health checks. Weight and height were objectively measured to calculate obesity (BMI ≥ 30 kg/m2). Diabetes status, physical activity, diet, smoking, and sleep quality were assessed using standardized questionnaires. Structural equation models adjusted for relevant confounders were used to analyze the mediating role of lifestyle in the relationships between shift work, and obesity and diabetes. Results Shift workers were more often obese (OR: 1.37, 95% CI 1.16–1.61) and reported more often to have diabetes (OR:1.35, 95% CI 1.003–1.11) than non-shift workers. Shift workers had lower physical activity levels, ate fruit and vegetables less often, smoked more often, and had poorer sleep quality (p p ≥ 0.05). Conclusion These results imply that interventions targeting diet, physical activity and in particular sleep problems specifically developed for shift workers could potentially reduce the adverse health effects of shift work.

The immune system potentially plays an important mechanistic role in the relation between shift work and adverse health effects. To better understand the immunological effects of shift work, we compared numbers and functionality of immune cells between night-shift and non-shift workers. Blood samples were collected from 254 night-shift and 57 non-shift workers employed in hospitals. Absolute numbers of monocytes, granulocytes, lymphocytes, and T cell subsets were assessed. As read out of immune function, monocyte cytokine production and proliferative capacity of CD4 and CD8 T cells in response to various stimuli were analysed. The mean number of monocytes was 1.15 (95%-CI = 1.05–1.26) times higher in night-shift than in non-shift workers. Furthermore, night-shift workers who worked night shifts in the past three days had a higher mean number of lymphocytes (B = 1.12 (95%-CI = 1.01–1.26)), T cells (B = 1.16 (95%-CI = 1.03–1.31)), and CD8 T cells (B = 1.23 (95%-CI = 1.05–1.45)) compared to non-shift workers. No differences in functional parameters of monocytes and lymphocytes were observed. The differences in numbers of monocytes and T cells suggest that chronic exposure to night-shift work as well as recent night-shift work may influence the immune status of healthcare workers. This knowledge could be relevant for preventive initiatives in night-shift workers, such as timing of vaccination.

Objectives Shift work may alter workers’ leisure-time and occupational physical activity (PA) levels, which might be one of the potential underlying mechanisms of the negative health effects of shift work. Therefore, we compared objectively measured PA levels between hospital shift and non-shift workers. Methods Data were used from Klokwerk+, a cohort study examining the health effects of shift work among healthcare workers employed in hospitals. In total, 401 shift workers and 78 non-shift workers were included, all of whom wore Actigraph GT3X accelerometers for up to seven days. Time spent sedentary, standing, walking, running, stairclimbing, and cycling during leisure time and at work was estimated using Acti4 software. Linear regression was used to compare proportions of time spent in these activities between hospital shift and non-shift workers. Results Average accelerometer wear-time was 105.9 [standard deviation (SD) 14.0] waking hours over an average of 6.9 (SD 0.6) days. No differences between hospital shift and non-shift workers were found in leisure-time PA (P>0.05). At work, shift workers were less sedentary [B=-10.6% (95% CI -14.3 -6.8)] and spent larger proportions of time standing [B=9.5% (95% CI 6.4-12.6)] and walking [B=1.2% (95% CI 0.1-2.2)] than non-shift workers. However, these differences in occupational PA became smaller when the number of night shifts during accelerometer wear-time increased. Conclusions Leisure-time PA levels of hospital shift workers were similar to those of non-shift workers, but shift workers were less sedentary and more physically active (ie, standing/walking) at work. Future research to the role of occupational activities in the health effects of shift work is recommended.

Objective Unfavorable eating patterns might contribute to the adverse health effects of shift work. Our objective was to examine differences in meal and snack frequency, as well as the quality of snacks, between shift and day workers and between different types of shifts. Methods Cross-sectional data from 485 healthcare workers aged 18–65 years of the Klokwerk+ cohort study was used. Dietary intake was assessed using 3-day food diaries, and meals and snacks were classified by the food-based classification of eating episodes method. Using multivariable-adjusted regression analyses, we esti-mated differences in meal and snack frequency and the quality of snacks between shift and day workers. Within the shift working group, eating frequency on day, evening, and night shifts were compared to work-free days. Results Meal and snack frequency as well as the quality of snacks showed no significant differences between shift and day workers (P≥0.05). Shift workers had a higher frequency of high-quality snacks [β 0.29, 95% confidence interval (CI) 0.12–0.46] and a lower frequency of low-quality snacks (β-0.29, 95% CI-0.49–-0.09) on evening shifts compared to their work-free days. Compared to work-free days, shift workers had a higher frequency of high-quality snacks on days shifts (β 0.24, 95% CI 0.10–0.38), and only those aged ≤40 years had a higher frequency of snacks on night shifts (β 0.53, 95% CI 0.06–1.00) (interaction by age P

The aim of this study was to compare chronotype- and age-dependent sleep disturbances and social jetlag between rotating shift workers and non-shift workers, and between different types of shifts. In the Klokwerk+ cohort study, we included 120 rotating shift workers and 74 non-shift workers who were recruited from six Dutch hospitals. Participants wore Actigraph GT3X accelerometers for 24 hr for 7 days. From the Actigraph data, we predicted the sleep duration and social jetlag (measure of circadian misalignment). Mixed models and generalized estimation equations were used to compare the sleep parameters between shift and non-shift workers. Within shift workers, sleep on different shifts was compared with sleep on work-free days. Differences by chronotype and age were investigated using interaction terms. On workdays, shift workers had 3.5 times (95% confidence interval: 2.2–5.4) more often a short (< 7 hr per day) and 4.1 times (95% confidence interval: 2.5–6.8) more often a long (≥ 9 hr per day) sleep duration compared with non-shift workers. This increased odds ratio was present in morning chronotypes, but not in evening chronotypes (interaction p-value

Objective Shift work has been linked to cardio-metabolic diseases, but insight into different shift work-related aspects and chronotype of shift workers and their relation with metabolic risk factors is limited. This study examined the association between current shift work status, frequency and duration of night shift work, chronotype, and metabolic risk factors in a population of health care workers. Methods Anthropometrics, questionnaires, and blood samples were collected from 503 shift working and 93 non-shift working health care workers employed in hospitals. Body mass index, waist circumference, cholesterol (total, HDL, LDL), triglycerides, and high-sensitivity C-reactive protein were measured. Associations of current shift work, frequency (non-night shift worker, 1–2, 3–4, ≥5 night shifts/month) and duration of night shift work (non-night shift workers

Background Night–shift work may cause severe disturbances in the worker’s circadian rhythm, which has been associated with the onset of health problems and diseases. As a substantial part of the workforce is exposed to night–shift work, harmful aspects of night–shift work should not be overlooked. The aim of the Klokwerk + study is to study the effects of night–shift work on body weight and infection susceptibility and the mechanisms underlying these health effects. First, we will study the relation between night–shift work exposure and body weight and between night–shift work exposure and infection susceptibility. Second, we will examine the mechanisms linking night–shift work exposure to body weight and infection susceptibility, with a specific focus on sleep, physical activity, diet, light exposure, vitamin D level, and immunological factors. Lastly, we will focus on the identification of biomarkers for chronic circadian disturbance associated with night–shift work. Methods/design The design of this study is a prospective observational cohort study consisting of 1,960 health care workers aged 18–65 years. The study population will consist of a group of night–shift workers and an equally sized group of non–night–shift workers. During the study, there will be two measurement periods. As one of the main outcomes of this study is infection susceptibility, the measurement periods will take place at approximately the first (September/October) (T0) and the last month (April/May) (T1, after 6 months) of the flu season. The measurements will consist of questionnaires, anthropometric measurements, a smartphone application to determine infection susceptibility, food diaries, actigraphy, light sensors, and blood sample analyses. Discussion The Klokwerk + study will contribute to the current need for high–quality data on the health effects of night–shift work and its underlying behavioral and physiological mechanisms. The findings can be the starting point for the development of interventions that prevent negative health effects caused by night–shift work. In addition, the identification of biomarkers indicative of loss of homeostasis due to circadian disturbance may be an important asset in monitoring the effects of such interventions.

Background Shift work may alter workers’ physical activity (PA) level, making PA a potential underlying mechanism of the negative health effects of shift work. As prior studies on shift work and PA have generally used self-reported, overall PA measures, the results may be susceptible to bias. Therefore, our aim was to compare objectively measured non-occupational and occupational PA levels between shift workers and non-shift workers. Methods Data were used from Klokwerk+, a prospective cohort study examining the health effects of shift work among health care workers. In total, 401 rotating and/or night shift workers and 78 non-shift workers were included, who wore Actigraph GT3X+ accelerometers for 7 consecutive days. Time spent sitting, standing, walking, running, stairclimbing, and cycling during leisure and at work was estimated using Acti4-software. Linear regression was used to compare proportions of time spent in these activities between shift and non-shift workers. Results Average accelerometer wear-time was 105.9 hours (SD=14.0) over an average of 6.9 days (SD=0.6). No differences between shift workers and non-shift workers were found in PA behaviours during leisure-time (p>0.05). At work, shift workers were less sedentary (B=−10.6 (95%-CI=−14.3- −6.8)) and spent larger proportions of the time standing (B=9.5 (95%-CI=6.4–12.6)) and walking (B=1.2 (95%-CI=0.1–2.2)) than non-shift workers. Conclusions Non-occupational PA levels of shift workers were similar to that of non-shift workers, but shift workers were more physically active (i.e. standing/walking) at work. Future research should focus on the role of this difference in occupational PA in the health effects of shift work.

Background Shift work is increasingly being suggested to be associated with an increased risk for overweight. Physical activity (PA) has been hypothesised to play a role in the health effects of shift work, but research on this role of PA is scarce. This study aimed to examine the association between night shift work and body mass index (BMI) and the moderating role of PA therein. Methods Data from 588 workers were used from the prospective cohort Klokwerk+ study, examining the health effects of night shift work in health care workers. BMI was calculated by measured body weight (in kg) divided by body height (in metre squared). PA was measured using the validated Short QUestionnaire to ASses Health-enhancing PA (SQUASH) questionnaire. Linear regression analyses were performed for the associations between shift work and BMI; interaction terms were added to determine the influence of PA. Results Mean BMI for shift workers was 25.3 (SD=4.2) versus 25.4 (SD=4.2) for non-shift workers (p>0.05). Shift workers were more moderately active than non-shift workers (beta 318 min/wk, 95% CI 141–496). After adjustment, there were no significant differences in the amount of vigorous intensity PA (beta-43 min/wk, 95% CI-115-26). There was no significant interaction for either moderate or vigorous PA in the shift work-BMI association. Conclusions Our study could not confirm the hypothesis that PA moderates the shift work-BMI relation. To confirm these findings and to get more insight into the moderating and mediating role of PA and other lifestyle behaviours, more longitudinal studies are recommended.

Recently, there has been interest in whether shift work may enhance susceptibility to infection. Our aim was to determine whether shift workers in the health-care field have a higher incidence, duration, and/or severity of influenza-like illness (ILI) and acute respiratory infection (ARI) than non–shift workers. From September 2016 to June 2017, 501 rotating and/or night-shift workers and 88 non–shift workers from the Klokwerk+ Study (the Netherlands, 2016–2017) registered the occurrence of ILI/ARI symptoms daily using a smartphone application. The incidence rate of ILI/ARI (defined as ≥2 symptoms on the same day/≥1 symptom on 2 consecutive days), the mean duration of each episode, and the incidence rate of severe episodes were compared between shift workers and non–shift workers using negative binomial regression and linear mixed-model analysis. In total, participants completed 110,347 diaries. Shift workers’ incidence rate of ILI/ARI was 1.20 (95% confidence interval (CI): 1.01, 1.43) times higher than that of non–shift workers, and for severe ILI/ARI episodes, shift workers’ incidence rate was 1.22 (95% CI: 1.01, 1.49) times higher. The mean duration of an ILI/ARI episode did not differ (ratio between means = 1.02, 95% CI: 0.87, 1.19). In conclusion, shift workers in health care had more ILI/ARI episodes and more severe ILI/ARI episodes than non–shift workers, but with a similar duration. Insight into underlying mechanisms connecting shift work and infection susceptibility will contribute to the design of preventive initiatives.

Objective To contribute to the development of measures that increase physical activity (PA) levels in workers with and without chronic diseases, insight into workers' PA level is needed. Therefore, this study examined the association between the number of chronic diseases and PA in a Dutch working population. Methods Data of 131,032 workers from the Dutch Public Health Monitor 2012 were used in this cross-sectional study conducted in 2015 in the Netherlands. PA was operationalized as adherence (yes/no) to three PA guidelines. One of these was the American College of Sports Medicine (ACSM) guideline (≥ 3 days/week, ≥ 20 min/day of vigorous-intensity activities). Also, the amount of moderate- and vigorous-intensity PA in min/week for those who were physically active for > 0 min/week was calculated. Associations between chronic diseases (0, 1, ≥ 2 chronic diseases) and PA were examined using logistic regression and Generalized Estimating Equations stratified for age (19–54 years/55–64 years). Results Workers aged 19–54 years with one (OR = 0.90 (99% CI = 0.84–0.95)) and multiple chronic diseases (OR = 0.76 (99% CI = 0.69–0.83)) had lower odds of adhering to the ACSM-guideline than workers without chronic diseases. Similar patterns were found for older workers. Younger workers with one (B = 24.44 (99% CI = 8.59–40.30)) and multiple chronic diseases (B = 49.11 (99% CI = 26.61–71.61)) had a higher amount of moderate PA than workers without chronic diseases. Conclusion Workers with chronic diseases adhered less often to the ACSM-guideline, but among workers aged 19–54 years who were physically active for > 0 min/week, those with chronic diseases spent more time in moderate-intensity PA than those without chronic diseases., Highlights • Associations between chronic diseases and physical activity (PA) in workers were studied. • Workers with chronic diseases adhered less often to the ACSM-guideline. • Physically active workers with chronic diseases spent more time in moderate PA. • In promoting PA, attention should be paid to chronically ill workers and their needs.