Objectives: To examine work characteristics in relation to body mass index (BMI) and risk of obesity.
Methods: We analyzed data from 1150 participants working 20+ h week-1 from the 2014 National NIOSH Quality of Work Life Survey, based on a representative sample of US workers. We used multiple linear regression for BMI and multiple logistic regression for obesity to estimate associations with 19 different work characteristics plus one set of occupational categories controlling for age, gender, race/ethnicity, education, marital status, job physical exertion, and television watching.
Results: We found significant positive linear associations between BMI and night shift (versus day shift) schedule (B = 2.28, P = 0.008) and blue-collar (versus management/professional) work (B = 1.75, P = 0.008). Night shift schedule [odds ratio (OR) = 2.19, P = 0.029], sales/office work (OR = 1.55, P = 0.040), and blue-collar work (OR = 2.63, P = 0.006) were associated with increased risk of obesity versus 'healthy weight'. No other statistically significant associations between work characteristics and BMI or obesity were observed.
Conclusions: Night shift schedule and blue-collar work were related to increased BMI and obesity risk in US workers in 2014. Identifying risk factors in blue-collar work and redesigning jobs to reduce those risk factors, and reducing night shift work, could play a role in reducing the prevalence of obesity in the USA.
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http://dx.doi.org/10.1093/annweh/wxaa098 | DOI Listing |
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Recently, deep latent variable models have made significant progress in dealing with missing data problems, benefiting from their ability to capture intricate and non-linear relationships within the data. In this work, we further investigate the potential of Variational Autoencoders (VAEs) in addressing the uncertainty associated with missing data via a multiple importance sampling strategy. We propose a Missing data Multiple Importance Sampling Variational Auto-Encoder (MMISVAE) method to effectively model incomplete data.
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Department of Computer Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran.
According to recent research, with the ever-increasing use of Internet of Things (IoT) devices, there has arisen an ever-growing need for high-performance yet low-power circuits that can efficiently process information. Quantum-dot Cellular Automata (QCA) has emerged as a promising alternative to conventional complementary metal-oxide-semiconductor (CMOS) technology due to its great potential in digital design at nanoscale levels on account of very low power consumption and very high processing speed. However, QCA circuits are inherently prone to faults due to variations in manufacturing processes and due to the influence of environmental factors.
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