Abstract
Introduction
Poor sleep quality has been reported in the unemployed compared with employed. How sleep varies by employment status has been rarely examined at a population level. Therefore, we investigated sleep-wake patterns among employed, unemployed but actively seeking a job, and not-in-the-labor-force participants by gender and race/ethnicity.
Methods
Methods We used data from the American Time Use Survey (ATUS), a nationally representative sample of US residents aged ≥15years, which records weekday/weekend activities in a 24-hour period (4:00am-4:00am). This sample was restricted to participants aged 25–60 years (n=130,062). This analysis utilized functional nonparametric regression based on dimension reduction and neighborhood matching. We modeled the relationship between participant-specific sleep-wake trajectories, coded by minute, and employment status. Implementing the counterfactual approach, we estimated the effects of each employment scenario on participant-level expected sleep trajectory. This approach allowed the examination of hypothetical sleep-wake trajectories for each participant if their employment status differed from the observed. We then marginalized these findings to gender and race/ethnic subpopulations, controlling for confounders and secular trends.
Results
Mean age was 42□0.01 years, nearly half (51%) of participants were women and 68% were Whites. The proportions of employed, unemployed, and not-in-the-labor-force were 79%, 16.5% and 4.5%, respectively. On average, unemployed and not-in-the-labor-force participants had a later bedtime and wake-time compared with employed. With the exception of Whites, each individual race/ethnicity group showed pronounced differences in sleep-wake patterns by employment status. Of note, the likelihood of still being asleep up to 9:00am was greater when unemployed in comparison to had they been employed. Compared with employed, differences in sleep-wake patterns were pronounced among Blacks and Hispanics had they been unemployed, but attenuated if they were out-of-the-labor-force. Gender alone was not a strong moderator of the relationship between sleep-wake patterns and employment status. Unemployed participants had bedtime after 11pm, regardless of gender or race/ethnicity.
Conclusion
Using the counterfactual approach, we predicted sleep-wake patterns among individuals had they been employed, unemployed, or out-of-the-labor-force by gender and race/ethnicity. Though cross-sectional, our data suggest that the sleep schedules of racial/ethnic minorities in comparison to Whites may be more affected by employment status.
Support (if any):
Gender, Marital Status and Life Satisfaction