Effectiveness of workplace interventions with digital elements to reduce sedentary behaviours in office employees: a systematic review and meta-analysis.

BACKGROUND: Digital interventions are potential tools for reducing and limiting occupational sedentary behaviour (SB) in sedentary desk-based jobs. Given the harmful effects of sitting too much and sitting for too long while working, the aim of this systematic review and meta-analysis was to examine the effectiveness of workplace interventions, that incorporated digital elements, to reduce the time spent in SB in office workers. METHODS: Randomised control trials that evaluated the implementation of workplace interventions that incorporated digital elements for breaking and limiting SB among desk-based jobs were identified by literature searches in six electronic databases (PubMed, Web of Science, Scopus, CINAHL, PsycINFO and PEDro) published up to 2023. Studies were included if total and/or occupational SB were assessed. Only studies that reported pre- and postintervention mean differences and standard deviations or standard errors for both intervention arms were used for the meta-analysis. The meta-analysis was conducted using Review Manager 5 (RevMan 5; Cochrane Collaboration, Oxford, UK). Risk of bias was assessed using the Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields QUALSYST tool. RESULTS: Nineteen studies were included in the systematic review. The most employed digital elements were information delivery and mediated organisational support and social influences. Multicomponent, information, and counselling interventions measuring total and/or occupational/nonoccupational SB time by self-report or via device-based measures were reported. Multicomponent interventions were the most represented. Eleven studies were included in the meta-analysis, which presented a reduction of 29.9 (95% CI: -45.2, -14.5) min/8 h workday in SB (overall effect: Z = 3.81). CONCLUSIONS: Multicomponent interventions, using a wide range of digital features, have demonstrated effectiveness in reducing time spent in SB at the workplace among desk-based employees. However, due to hybrid work (i.e., work in the office and home) being a customary mode of work for many employees, it is important for future studies to assess the feasibility and effectiveness of these interventions in the evolving work landscape. TRIAL REGISTRATION: The review protocol was registered in the Prospero database (CRD42022377366).

Past or Present; Which Exposures Predict Metabolomic Aging Better? The Doetinchem Cohort Study.

People age differently. Differences in aging might be reflected by metabolites, also known as metabolomic aging. Predicting metabolomic aging is of interest in public health research. However, the added value of longitudinal over cross-sectional predictors of metabolomic aging is unknown. We studied exposome-related exposures as potential predictors of metabolomic aging, both cross-sectionally and longitudinally in men and women. We used data from 4 459 participants, aged 36-75 of Round 4 (2003-2008) of the long-running Doetinchem Cohort Study (DCS). Metabolomic age was calculated with the MetaboHealth algorithm. Cross-sectional exposures were demographic, biological, lifestyle, and environmental at Round 4. Longitudinal exposures were based on the average exposure over 15 years (Round 1 [1987-1991] to 4), and trend in these exposure over time. Random Forest was performed to identify model performance and important predictors. Prediction performances were similar for cross-sectional and longitudinal exposures in both men (R2 6.8 and 5.8, respectively) and women (R2 14.8 and 14.4, respectively). Biological and diet exposures were most predictive for metabolomic aging in both men and women. Other important predictors were smoking behavior for men and contraceptive use and menopausal status for women. Taking into account history of exposure levels (longitudinal) had no added value over cross-sectionally measured exposures in predicting metabolomic aging in the current study. However, the prediction performances of both models were rather low. The most important predictors for metabolomic aging were from the biological and lifestyle domain and differed slightly between men and women.

Sleep quality, sleep duration, and sleep disturbances among hospital night workers: a prospective cohort study.

PURPOSE: This study aimed to assess among hospital night workers (i) to what extent sleep quality, sleep duration and sleep disturbances overlap, and (ii) associations between sociodemographic factors, lifestyle factors and work characteristics and sleep components. METHODS: Data were used from 467 hospital night workers participating in the Klokwerk + study, a prospective cohort study with two measurements. Sleep quality was measured by the Pittsburgh Sleep Quality Index, sleep duration and sleep disturbances were measured by the Medical Outcomes Study Sleep Scale. The overlap between the three sleep measures was visualized with a Venn diagram and the proportions of overlap was calculated. Associations between independent variables (sociodemographic factors, lifestyle factors and work characteristics) and the three sleep outcomes were estimated using between-within Poisson regression models. RESULTS: About 50% of the hospital night workers had at least one poor sleep outcome. Overlap in poor sleep outcomes was apparent for 36.8% of these workers, while the majority had a poor outcome in one of the sleep components only (63.1%). Former smoking had a significant association with poor sleep quality. For most independent variables no associations with poor sleep outcomes were observed. CONCLUSION: Our findings suggest that sleep quality, sleep duration and sleep disturbances are separate entities and should be studied separately. Lifestyle factors and work characteristics were generally not associated with poor sleep. Since these factors can have an acute effect on sleep, future research should consider ecological momentary assessment to examine how exposure and outcomes (co)vary within-persons, over time, and across contexts. Trial registration Netherlands Trial Register trial number NL56022.041.16.

Mental health of healthcare workers during the first year of the COVID-19 pandemic in the Netherlands: a longitudinal study.

Purpose: In March 2020, the WHO declared COVID-19 a pandemic. Previous virus outbreaks, such as the SARS outbreak in 2003, appeared to have a great impact on the mental health of healthcare workers. The aim of this study is to examine to what extent mental health of healthcare workers differed from non-healthcare workers during the first year of the COVID-19 pandemic. Methods: We used data from a large-scale longitudinal online survey conducted by the Corona Behavioral Unit in the Netherlands. Eleven measurement rounds were analyzed, from April 2020 to March 2021 (N = 16,615; number of observations = 64,206). Mental health, as measured by the 5-item Mental Health Inventory, was compared between healthcare workers and non-healthcare workers over time, by performing linear GEE-analyses. Results: Mental health scores were higher among healthcare workers compared to non-healthcare workers during the first year of the pandemic (1.29 on a 0-100 scale, 95%-CI = 0.75-1.84). During peak periods of the pandemic, with over 100 hospital admissions or over 25 ICU admissions per day and subsequently more restrictive measures, mental health scores were observed to be lower in both healthcare workers and non-healthcare workers. Conclusion: During the first year of the COVID-19 pandemic, we observed no relevant difference in mental health between healthcare workers and non-healthcare workers in the Netherlands. To be better prepared for another pandemic, future research should investigate which factors hinder and which factors support healthcare workers to maintain a good mental health.

Perimenopause: Symptoms, work ability and health among 4010 Dutch workers.

OBJECTIVE: In this study we examined the associations between menopausal symptoms and work ability and health among a general population of Dutch female workers. STUDY DESIGN: This nationwide cross-sectional study was a follow-up of the Netherlands Working Conditions Survey 2020. In 2021, 4010 Dutch female employees aged 40-67 years completed an online survey on a variety of topics, including menopausal symptoms, work ability and health. METHODS: Linear and logistic regression analyses were performed to investigate the association between the degree of menopausal symptoms with work ability, self-rated health and emotional exhaustion, after adjustment for potential confounders. RESULTS: Almost one-fifth of participants were in the perimenopause (n = 743). Of these women, 80 % experienced menopausal symptoms: 27.5 % 'often' and 52.5 % 'sometimes'. Experiencing menopausal symptoms was associated with lower work ability, poorer self-rated health, and more emotional exhaustion. These associations were most pronounced among perimenopausal women 'often' experiencing symptoms. CONCLUSIONS: Menopausal symptoms threaten the sustainable employability of female workers. Interventions and guidelines are needed to support women, employers and (occupational) health professionals.

Predicting self-perceived general health status using machine learning: an external exposome study.

BACKGROUND: Self-perceived general health (SPGH) is a general health indicator commonly used in epidemiological research and is associated with a wide range of exposures from different domains. However, most studies on SPGH only investigated a limited set of exposures and did not take the entire external exposome into account. We aimed to develop predictive models for SPGH based on exposome datasets using machine learning techniques and identify the most important predictors of poor SPGH status. METHODS: Random forest (RF) was used on two datasets based on personal characteristics from the 2012 and 2016 editions of the Dutch national health survey, enriched with environmental and neighborhood characteristics. Model performance was determined using the area under the curve (AUC) score. The most important predictors were identified using a variable importance procedure and individual effects of exposures using partial dependence and accumulated local effect plots. The final 2012 dataset contained information on 199,840 individuals and 81 variables, whereas the final 2016 dataset had 244,557 individuals with 91 variables. RESULTS: Our RF models had overall good predictive performance (2012: AUC = 0.864 (CI: 0.852-0.876); 2016: AUC = 0.890 (CI: 0.883-0.896)) and the most important predictors were "Control of own life", "Physical activity", "Loneliness" and "Making ends meet". Subjects who felt insufficiently in control of their own life, scored high on the De Jong-Gierveld loneliness scale or had difficulty in making ends meet were more likely to have poor SPGH status, whereas increased physical activity per week reduced the probability of poor SPGH. We observed associations between some neighborhood and environmental characteristics, but these variables did not contribute to the overall predictive strength of the models. CONCLUSIONS: This study identified that within an external exposome dataset, the most important predictors for SPGH status are related to mental wellbeing, physical exercise, loneliness, and financial status.

Predictors of healthy physiological aging across generations in a 30-year population-based cohort study: the Doetinchem Cohort Study.

BACKGROUND: Predicting healthy physiological aging is of major interest within public health research. However, longitudinal studies into predictors of healthy physiological aging that include numerous exposures from different domains (i.e. the exposome) are scarce. Our aim is to identify the most important exposome-related predictors of healthy physiological aging over the life course and across generations. METHODS: Data were used from 2815 participants from four generations (generation 1960s/1950s/1940s/1930s aged respectively 20-29/30-39/40-49/50-59 years old at baseline, wave 1) of the Doetinchem Cohort Study who were measured every 5 years for 30 years. The Healthy Aging Index, a physiological aging index consisting of blood pressure, glucose, creatinine, lung function, and cognitive functioning, was measured at age 46-85 years (wave 6). The average exposure and trend of exposure over time of demographic, lifestyle, environmental, and biological exposures were included, resulting in 86 exposures. Random forest was used to identify important predictors. RESULTS: The most important predictors of healthy physiological aging were overweight-related (BMI, waist circumference, waist/hip ratio) and cholesterol-related (using cholesterol lowering medication, HDL and total cholesterol) measures. Diet and educational level also ranked in the top of important exposures. No substantial differences were observed in the predictors of healthy physiological aging across generations. The final prediction model's performance was modest with an R2 of 17%. CONCLUSIONS: Taken together, our findings suggest that longitudinal cardiometabolic exposures (i.e. overweight- and cholesterol-related measures) are most important in predicting healthy physiological aging. This finding was similar across generations. More work is needed to confirm our findings in other study populations.

The longitudinal association between working from home and musculoskeletal pain during the COVID-19 pandemic.

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.

Estimating healthcare expenditures after becoming divorced or widowed using propensity score matching.

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.

GlycA, a Biomarker of Low-Grade Inflammation, Is Increased in Male Night Shift Workers.

Sustained night shift work is associated with various adverse health risks, including an increased risk of cardiovascular disease, type II diabetes, and susceptibility to infectious respiratory diseases. The extent of these adverse health effects, however, seems to greatly vary between night shift workers, yet the underlying reasons and the mechanisms underlying these interindividual differences remain poorly understood. Metabolomics assays in the blood have recently gained much attention as a minimally invasive biomarker platform capturing information predictive of metabolic and cardiovascular diseases. In this cross-sectional study, we explored and compared the metabolic profiles of 1010 night shift workers and 1010 age- and sex-matched day workers (non-shift workers) from the Lifelines Cohort Study. The metabolic profiles were determined using the 1H-NMR Nightingale platform for the quantification of 250 parameters of metabolism, including routine lipids, extensive lipoprotein subclasses, fatty acid composition, and various low-molecular metabolites, including amino acids, ketone bodies, and gluconeogenesis-related metabolites. Night shift workers had an increased BMI (26.6 vs. 25.9 kg/m2) compared with day workers (non-shift workers) in both sexes, were slightly more likely to be ever smokers (only in males) (54% vs. 46%), worked on average 5.9 ± 3.7 night shifts per month, and had been working in night shifts for 18.3 ± 10.5 years on average. We observed changes in several metabolic markers in male night shift workers compared with non-shift workers, but no changes were observed in women. In men, we observed higher levels of glycoprotein acetyls (GlycA), triglycerides, and fatty acids compared with non-shift workers. The changes were seen in the ratio of triglycerides and cholesterol(esters) to total lipids in different sizes of VLDL particles. Glycoprotein acetyls (GlycAs) are of particular interest as markers since they are known as biomarkers for low-grade chronic inflammation. When the analyses were adjusted for BMI, no significant associations were observed. Further studies are needed to better understand the relationship between night shift work and metabolic profiles, particularly with respect to the role of sex and BMI in this relationship.

The mediating role of physical activity and sedentary behavior in the association between working from home and musculoskeletal pain during the COVID-19 pandemic.

Introduction: Working from home during the COVID-19 pandemic has been associated both with physical inactivity and musculoskeletal pain. However, it has not been examined whether physical activity and sedentary behavior are underlying mechanisms in the association between working from home and musculoskeletal pain. Therefore, we examined their mediating role in this association. Methods: Data were used from 24 questionnaire rounds of the Lifelines COVID-19 cohort (March 2020-January 2022). Longitudinal information on work situation (location, home, hybrid), physical activity, sedentary behavior, and musculoskeletal pain was collected among 28,586 workers. Analysis of physical activity/sedentary behavior as mediators of the association between working from home and musculoskeletal pain was performed using multilevel structural equation modeling. Results: Home workers more often had pain in the upper back [odds ratio (OR) = 1.17, 95%-confidence interval (CI) = 1.02-1.34] and arm, neck, and/or shoulder (ANS) (OR = 1.32, 95%-CI = 1.19-1.47) than location workers. Furthermore, home workers were more often sedentary for >9 h per work day than location workers (OR = 2.82, 95%-CI = 2.56-3.09), and being more sedentary was associated with musculoskeletal pain (upper back: OR = 1.17, 95%-CI = 1.06-1.30; ANS: OR = 1.25, 95%-CI = 1.16-1.34). Corresponding indirect effects were OR = 1.18 (95%-CI = 1.04-1.33) and OR = 1.26 (95%-CI = 1.12-1.35). No indirect effect was found for physical activity. Similar indirect effects were observed for hybrid workers. Conclusion: Home and hybrid workers were more likely to have pain in the upper musculoskeletal system during the COVID-19 pandemic than location workers, which was partly mediated by increased sedentary behavior, but not by reduced physical activity. Measures to reduce sedentary time in home workers may contribute to preventing musculoskeletal pain.

Using random forest to identify longitudinal predictors of health in a 30-year cohort study.

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.

Night-shift work is associated with increased susceptibility to SARS-CoV-2 infection.

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.

Working from home during the COVID-19 pandemic and its longitudinal association with physical activity and sedentary behavior.

OBJECTIVE: Working from home during the COVID-19 pandemic has affected many workers' daily life and possibly their physical activity behavior. We studied the longitudinal association of working from home during the pandemic with physical activity and sedentary behavior. METHODS: Longitudinal data from 17 questionnaire rounds of the Lifelines COVID-19 cohort (March 2020-February 2021) were used. In total, 33 325 workers were included. In every round, participants reported their current work situation: location, home, or hybrid (working on location and from home). Physical activity levels and sedentary behavior before and during the pandemic were asked. Logistic generalized estimating equations adjusted for demographic/work/health covariates were used to study the association of work situation with physical activity and sedentary behavior. RESULTS: Home workers were less likely to meet the recommended ≥150 minutes/week of moderate-to-vigorous-intensity activity during the pandemic than location workers [odds ratio (OR) 0.93, 95% confidence interval (CI) 0.90-0.96] and more likely to be less physically active than before the pandemic (OR 1.09, 95% CI 1.04-1.14). Furthermore, compared to location workers, home and hybrid workers were more likely to be more sedentary (sitting ≥8 hours/day) on workdays during than before the pandemic (OR 1.51, 95% CI 1.39-1.64/1.36-1.68, respectively). CONCLUSIONS: Compared to location workers, home workers (and to a lesser extent hybrid workers) were more often physically inactive and sedentary during than before the COVID-19 pandemic. As a substantial part of the working population may continue to work (partly) from home after the pandemic, workers should be supported to increase activity and reduce sitting while working from home.

Night shift work characteristics are associated with several elevated metabolic risk factors and immune cell counts in a cross-sectional study.

Night shift work is associated with increased health risks. Here we examined the association of metabolic risk factors and immune cell counts, with both night shift work and particular characteristics thereof: frequency, duration and consecutive night shifts. We performed a cross-sectional study using data from 10,201 non-shift workers and 1062 night shift workers of the Lifelines Cohort study. Linear regression analyses, adjusted for demographic, lifestyle and occupational factors, were used to study associations of night shift work characteristics with metabolic risk factors and immune cell counts. Night shift workers had an increased BMI, waist circumference and immune cell counts compared to non-shift workers. This was especially seen in night shift workers who had a higher frequency of night shifts per month (≥ 5: BMI: B = 0.81 kg/m2 (95%-CI = 0.43-1.10); waist circumference: B = 1.58 cm (95%-Cl = 0.34-1.71; leukocytes: B = 0.19 × 109 cells/L (95%-CI = 0.04-0.34 × 109)) and worked more consecutive night shifts (> 3: BMI: B = 0.92 kg/m2 (95%-CI = 0.41-1.43); waist circumference: B = 1.85 cm (95%-Cl = 0.45-3.24); leukocytes: B = 0.32 × 109 cells/L (95%-CI = 0.09-0.55 × 109)). This association was less pronounced in long-term night shift workers (≥ 20 years). Our findings provide evidence for the association between night shift work characteristics and BMI, waist circumference and leukocytes (including, monocytes, lymphocytes, and basophil granulocytes).

Socioeconomic differences in healthcare expenditure and utilization in The Netherlands.

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.

The mediating role of lifestyle in the relationship between shift work, obesity and diabetes.

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 < 0.05). Mediation analysis revealed that shift workers had a higher odds of obesity (OR: 1.07, 95% CI 1.01-1.15) and diabetes (OR: 1.13, 95% CI 1.02-1.27) mediated by poorer sleep quality. Lower physical activity levels (OR: 1.11, 95% CI 1.05-1.19) and lower intake of fruit and vegetables (OR: 1.04, 95% CI 1.01-1.15) were also mediators in the relationship between shift work and obesity, but not in the relationship between shift work and diabetes (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.

Sickness absenteeism, work performance, and healthcare use due to respiratory infections for shift and non-shift workers.

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.

The mediating role of sleep, physical activity, and diet in the association between shift work and respiratory infections.

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 baseline, 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.

Shift work and its relation with meal and snack patterns among healthcare workers.

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 estimated 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 [ß 0.29, 95% confidence interval (CI) 0.12-0.46] and a lower frequency of low-quality snacks (ß -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 (ß 0.24, 95% CI 0.10-0.38), and only those aged ≤40 years had a higher frequency of snacks on night shifts (ß 0.53, 95% CI 0.06-1.00) (interaction by age P<0.05). Conclusion This study observed no differences between day and shift workers either in meal and snack frequency or in the quality of snacks. However, snacking patterns differed across shifts. Future research should investigate whether these snacking patterns contribute to the adverse health effects of shift work.