What keeps low-SES children from sleeping well: the role of presleep worries and sleep environment

Abstract

Objectives: Children in families of low socioeconomic status (SES) have been found to have poor sleep, yet the reasons for this finding are unclear. Two possible mediators, presleep worries and home environment conditions, were investigated as indirect pathways between SES and children's sleep.

Participants/methods: The participants consisted of 271 children (M (age) = 11.33 years; standard deviation (SD) = 7.74 months) from families varying in SES as indexed by the income-to-needs ratio. Sleep was assessed with actigraphy (sleep minutes, night waking duration, and variability in sleep schedule) and child self-reported sleep/wake problems (e.g., oversleeping and trouble falling asleep) and sleepiness (e.g., sleeping in class and falling asleep while doing homework). Presleep worries and home environment conditions were assessed with questionnaires.

Results: Lower SES was associated with more subjective sleep/wake problems and daytime sleepiness, and increased exposure to disruptive sleep conditions and greater presleep worries were mediators of these associations. In addition, environmental conditions served as an intervening variable linking SES to variability in an actigraphy-derived sleep schedule, and, similarly, presleep worry was an intervening variable linking SES to actigraphy-based night waking duration. Across sleep parameters, the model explained 5-29% of variance.

Conclusions: Sleep environment and psychological factors are associated with socioeconomic disparities, which affect children's sleep.

Keywords: Children; Environment; Poverty; Sleep; Worry.

Conflict of interest statement

Conflict of interest

The ICMJE Uniform Disclosure Form for Potential Conflicts of Interest associated with this article can be viewed by clicking on the following link: http://dx.doi.org/10.1016/j.sleep.2014.10.008.

Copyright © 2014 Elsevier B.V. All rights reserved.

Figures

Fig. 1

Path model of environmental conditions and presleep worries as mediators of relations between the income-to-needs ratio and sleep. Model fit: χ2(4) = 4.85 ns; χ2/df = 0.82; CFI = 0.99; RMSEA = 0.03 ns, 95% CI (0.00–0.10). Residual variances among environmental conditions and presleep worries were allowed to correlate as were the residual variances among the sleep parameters. Statistically significant lines are solid and nonsignificant lines are dotted. The covariates were child gender, ethnicity, medication use, and body mass index. tp < 0.10; *p < 0.05; **p < 0.01; ***p < 0.001.