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Blue collar workers often have a low educational level. Low SEP employees often work in demanding work conditions, such as shift work, which impose health risks. Shift work is associated with adverse health outcomes like cardiovascular disease. This may partly be due to poor sleep- and lifestyle behaviors, but evidence is inconclusive and mediation effects of sleep and lifestyle have hardly been studied. In addition, there is a need to identify subgroups of shift workers who are at particular increased risk for adverse health outcomes. Insight in the relationship between poor sleep, lifestyle and health among blue collar shift workers is needed to properly target prevention. Such insight may help to reduce socioeconomic health differences.



Our aim is to determine the mediating role of sleep quality and lifestyle on the adverse health effects (i.e. general perceived health, mental health and cardiometabolic health) of shift work among blue collar workers (AIM 1). We also aim to identify subgroups (moderators) of workers with a particular increased risk of adverse health effects of shift work based on sleep quality, lifestyle, shift work features and experienced psychological strain of shift work (AIM 2).



Based on large datasets of several companies with detailed information on shift work, this study will provide new insights into:

1. Adverse health effects of shift work in blue collar workers

2. Mediating and moderating role of sleep and lifestyle in the adverse health effects of shift work, taking relevant shift work features (e.g. schedule, duration of shift work) into account

3. Subgroups with an increased risk of adverse health effects due to shift work

4. Differences in adverse health effects of shift work by education, income, gender, and between workers with and without a chronic disease

We will facilitate inclusion of the obtained results into guidelines of occupational health care.




The study population exists of approximately 23,000 blue collar workers from production, industry, and service companies who have participated voluntarily in a Preventive Medical Examination (PME). About 40% of the population works in irregular shifts.



From PME data, shift work status, age, gender, educational level, physical activity, diet, smoking status, alcohol intake, sleep quality, perceived general health status, psychosocial health (4DSQ), presence of chronic disease and objectively measured body weight and height, blood pressure, random glucose and total cholesterol will be used. Self-perceived psychological strain variables related to shift work have also been measured, such as having control about work times and satisfaction with working in irregular shifts. Company records will be used to determine current and past shift work schedules and the number of years worked in irregular shifts.



The associations between shift work and health outcomes (general perceived health, mental health and cardiometabolic health), lifestyle and sleep will be analyzed using linear and logistic mixed models accounting for the clustering of observations of workers within the same company. The mediation effects of lifestyle and sleep in the relationship between shift work and health (AIM 1) will be examined by structural equation models. The moderating effects of lifestyle and sleep in the relationship between shift work and health outcomes (AIM 2) will be examined by adding interaction terms between shift work and sleep and lifestyle to identify subgroups. If significant, stratified analyses will be performed. The single subgroups with a high risk of adverse health effects of shift work based on the moderation analyses, will be combined in new variables and related to health outcomes to identify subgroups with the highest risk of adverse health outcomes. Differences by gender, education, chronic disease status (present/absent), shift work features (e.g. duration of shift work, frequency of night shifts), and experienced physiological strain due to shift work (e.g. satisfaction with shift schedule) will also be investigated. Power is sufficient to do this since the power analysis indicated that we can detect relevant differences in the primary outcome in 40 subgroups of 193 participants (3% of the shift working population).



Activities that contribute to knowledge dissemination include 1) presentations, workshops, fact sheets and news releases; 2) news releases via existing communication channels (e.g. social media) of involved stakeholders; 3) scientific articles; 4) presentations at national and international scientific congresses; and 5) relevant professional organizations will be approached to include the results in professional guidelines. An implementation plan of the results will be developed by a problem analysis, in which we determine barriers and facilitators to implement the results on a national scale.


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