Only half of middle aged and elderly Chinese in our study reported good sleep quality. Quality of sleep varied substantially by gender and geographical location and was affected by level of self-rated health, presence of depressive symptoms, sleep quantity, chronic diseases and social activity. Significant associations between levels of sleep quality and resistin, PAI-1, insulin, HOMA-IR, HOMA2-IR, LDL, TCH and TG were observed. These associations remained significant for insulin, HOMA-IR, HOMA2-IR, LDL and TCH among a subgroup of participants who were free of CVD and diabetes.
Sixteen percent of the elderly Chinese assessed their sleep quality as poor in this study. They showed higher prevalence of mental and physical diseases (depression, CVD, arthritis, prostatism (men only)) and shorter daily sleep time compared to participants that reported a good level of sleep quality. We also observed significant gender differences in those who reported poor sleep quality wherein Chinese women were almost 50% less likely to report good sleep quality than men. Similar to our findings, Li et al.[6
] found a 12% prevalence of insomnia among Hong Kong Chinese adults. Li et al.[6
] studied 9851 Hong Kong Chinese aged 18–65 years and found a 60% greater chance of insomnia being reported among women than men. Klink et al.[5
] also found there was a 50% higher chance of women reporting insomnia. Furthermore, in a study of 400 adults aged 20–70 years, Reyner et al.[7
] found women, reported more awakenings, more total time spent awake during the night and poorer sleep quality. This difference in sleep quality could be due to gender differences in the prevalence of psychiatric morbidities, socio-cultural factors and coping strategies. [5
We also found that sleep quality related to geographical location. Those living in the north were 40% less likely to report good sleep quality compared to those living in the south. This could be due to differences between the two cities wherein compared to Shanghai residents, Beijing residents show almost twice the prevalence of chronic diseases (depression, CVD, diabetes and metabolic syndrome), higher levels of alcohol consumption (37% Beijing vs. 20% Shanghai) and smoking (31% Beijing vs. 25% Shanghai), higher prevalence of nap taking (55% Beijing vs. 29% Shanghai) and less medical insurance coverage (53% Beijing vs. 86% Shanghai). To our knowledge this is the first study investigating differences in sleep quality as a function of geographical and residential status in China.
The effect of SRH (which is considered to be a valid representative of physical and mental health[38
]) was substantial on sleep quality for the total population and for gender and geographical subgroups. Those who reported good/excellent SRH had twice the chance of reporting good sleep quality compared to those that had fair, poor or very poor SRH.
In our study, we also found a strong association between sleep quality and presence of depressive symptoms, with the strongest effect among Shanghai men and Beijing women. The likelihood of reporting poor sleep quality was three times higher in the presence of depressive symptoms. Our findings are consistent with other studies that have shown significant associations between depression and insomnia[41
] and depression and sleep disturbances.[42
] The association between poor sleep and depression has also been observed in an elderly population.[2
Currently, the number of hours required for optimal functioning in an elderly population is not known. Although it is commonly believed that 8 hours of sleep per night is optimal for good health, recent studies suggest that mortality risk is lower among those sleeping 7 hours.[21
] The total hours of daily sleep includes night time sleep and nap time (nap taking is common mainly in north of China) and for that reason, 7–9 hours was categorized as normal daily sleep hours in our study. We found a substantial effect of sleep quantity on quality of sleep. Having a short sleep time significantly decreased the chance of reporting good quality of sleep. Even those reporting 7–9 hours of daily sleep were less likely to report good sleep quality than those reporting more than 9 hours of daily sleep. Presence of CVD also affected the levels of sleep quality among the total population but when we stratified by gender and residential location this effect remained significant only among Beijing men and Shanghai women.
We did not find any significant association between sleep quality and different lifestyle characteristics (smoking and alcohol drinking habits), age and BMI. This is consistent with Elwood et al. [38
] who studied 1986 welsh men aged 55–69 y and found no significant association between prevalence of insomnia and age, BMI, smoking and alcohol drinking habits. However, it is possible that the effect of smoking and alcohol drinking in our analysis might be confounded with gender as the majority of Chinese smokers and drinkers were men. Furthermore, this is affected by the limited number of participants in the extreme categories of alcohol consumption and BMI, which limits the statistical power to identify a potential association between these two variables and sleep patterns in our population.
Despite lower socio-economic status (lower average income, fewer years in full-time education and less medical insurance coverage) rural residents were more likely to report good levels of sleep quality compare to urban residents. This could be due to differences in lifestyle wherein those who live in rural areas tend to have higher levels of physical activity, higher age of retirement, less stressful lifestyles and a lower prevalence of both CVD (7.7% rural vs. 13% urban) and diabetes (11.4% rural vs. 17% urban).
In our analyses of the total sample and subgroups of the sample, we found no significant effects of income and education. This could be due to the fact that residence is a more comprehensive factor that might partially confound the effect of socio-economic factors (i.e. our sample of the Chinese population is skewed towards lower income and education and late retirement in rural areas).
Concentrations of inflammatory biomarkers, plasma insulin and index of insulin resistance were associated with sleep quality for the total population and among a subgroup of participants that were free from CVD and diabetes. However, no clear trends in the levels of biomarkers as a function of the different levels of sleep quality (good/common/poor) were observed. This is the first study to evaluate the associations between sleep quality and inflammatory markers and measures of insulin resistance in China and to evaluate the distribution and factors associated with sleep quality in such a vast country which has many geographical and residential differences
Although we considered the effect of comprehensive measurements of lifestyle, socio-demographic factors and presence of disease, our findings were limited. This could be due to the cross-sectional nature of our data and further longitudinal studies should be carried out to confirm our findings.
Furthermore, sleep patterns were measured using a questionnaire; a criticism of this type of measurement is that answers are provided by the participants and can be affected by recall bias. Furthermore, due to the size of the population and the extent of the general questionnaire, for logistic reasons the questions that we used are not as thorough as alternative validated questionnaires designed to evaluate sleep disturbances (e.g The Pittsburg Sleep Quality Inventory). Nevertheless, these factors could only generate a non-differential misclassification of the exposure (sleep patterns) that would attenuate the size of the effects hereby reported and the level of sleep disturbances in the population studied, but not necessarily our results and conclusions. Prospective evaluations and studies that provide more sensitive measures of sleep patterns in Chinese populations are required to clarify the determinants that affect sleep quality. This will also allow us to assess whether sleep disturbances are associated with early deviations from adequate cardio-metabolic health.