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From the Workroom to the Bedroom: Work-to-Home Spillover as a Mechanism Linking Work Characteristics to Sleep Health.

Kian HuangChristina MuClaire SmithSoomi Lee
Published in: Research square (2024)
Work may influence the home domain and subsequently impact employee sleep. Past work found that negative spillover mediated the relationship between perceived unfairness about work and insomnia symptoms across 20 years. As an extension of past work, this study investigated whether negative spillover and positive spillover mediate the relationship between job demands (perceived unfairness, job discrimination) and job resources (coworker and supervisor support) on multidimensional sleep health. Two waves of survey data from a subset of full-time workers were obtained from the Midlife in the United States Study approximately 10 years apart. A sleep health composite captured irregularity, dissatisfaction, nap frequency, inefficiency, and suboptimal sleep duration (higher=more sleep health problems). PROCESS Macro evaluated cross-sectional (T1) and sequential (T1 exposureàT1 mediatoràT2 outcome) mediation pathways, adjusting for sociodemographic characteristics, physical health, neuroticism, and work hours. Both cross-sectionally and prospectively, higher negative spillover mediated the association of higher unfairness with more sleep health problems, and the association between higher discrimination and more sleep health problems. There was no support for positive spillover as a mediator between job resources and sleep health cross-sectionally or prospectively. Findings suggest that organizations should reduce the amount of negative spillover by limiting instances of unfairness and discrimination at work to promote specific aspects of employee sleep health such as sleep irregularity, dissatisfaction, efficiency, and nap frequency.
Keyphrases
  • mental health
  • healthcare
  • public health
  • physical activity
  • sleep quality
  • health information
  • social support
  • depressive symptoms
  • human health
  • machine learning
  • climate change
  • mass spectrometry
  • data analysis