| dc.description.abstract | Background. Sleep duration, either short or long, has been associated with diseases such as obesity, type-2 diabetes and cardiovascular diseases. Characterizing the prevalence and patterns of sleep duration at the population-level, especially in resource-constrained settings, will provide informative evidence on a potentially modifiable risk factor. The aim of this study was to explore the patterns of sleep duration in the Peruvian adult and adolescent population, together with its socio-demographic profile. Material and Methods. A total of 12,424 subjects, mean age 35.8 years (SD +/-17.7), 50.6% males, were included in the analysis. This is a cross-sectional study, secondary analysis of the Use of Time National Survey conducted in 2010. We used weighted means and proportions to describe sleep duration according to socio-demographic variables (area and region; sex; age; education attainment; asset index; martial and job status). We used Poisson regressions, taking into account the multistage sampling design of the survey, to calculate crude and adjusted prevalence ratios (PR) and 95% confidence intervals (95% CI). Main outcomes were short- (<6 h) and long-sleep duration (>/= 9 h). Results. On average, Peruvians slept 7.7 h (95% CI [7.4-8.0]) on weekdays and 8.0 h (95% CI [7.8-8.1]) during weekends. The proportions of short- and long-sleep, during weekdays, were 4.3% (95% CI [2.9%-6.3%]) and 22.4% (95% CI [14.9%-32.1%]), respectively. Regarding urban and rural areas, a much higher proportion of short-sleep was observed in the former (92.0% vs. 8.0%); both for weekdays and weekends. On the multivariable analysis, compared to regular-sleepers (>/= 6 to <9 h), short-sleepers were twice more likely to be older and to have higher educational status, and 50% more likely to be currently employed. Similarly, relative to regular-sleep, long-sleepers were more likely to have a lower socioeconomic status as per educational attainment. Conclusions. In this nationally representative sample, the sociodemographic profile of short-sleep contrasts the long-sleep. These scenarios in Peru, as depicted by sleeping duration, differ from patterns reported in other high-income settings and could serve as the basis to inform and to improve sleep habits in the population. Moreover, it seems important to address the higher frequency of short-sleep duration found in urban versus rural settings. | |
