The presence of a U-shaped association between sleep duration and mortality rate, such that those individuals who sleep more or less than 7 hours per day are at higher risk of death, was first described more than 40 years ago.29
Subsequently, many investigators have focused on the short-sleep portion of the curve, investigating the cognitive, psychological, metabolic, and immunologic effects of acute or chronic sleep deprivation. This research has bolstered the belief that adequate sleep is important for the maintenance of normal mental and physical function.
However, a growing number of population-based studies investigating the effects of sleep on health have found that a prolonged sleep duration is also associated with an increased mortality risk. These findings have been replicated in the Cancer Prevention Study I and II, NHS, the first National Health and Nutrition Examination Survey, Framingham Study, and the Japan Collaborative Cohort Study.4,6–8,10,30
In fact, in many of these studies, long sleep was associated with an even greater mortality risk than short sleep. Compared with those sleeping 5 hours, women reporting 9 hours of sleep had a 15% greater mortality risk in Cancer Prevention Study II, a 30% greater risk in NHS, and a 33% greater risk in the Japan Collaborative Cohort Study.4,7,8
Epidemiologic research also suggests that the association between long sleep and mortality is not limited to 1 cause of death. Both the Cancer Prevention Study II and NHS studies reported that prolonged sleep was associated with an across-the-board increase in risk over a wide range of specific causes of death.7,8
Prolonged sleep has been linked to an increased risk for obesity, diabetes, heart disease, and stroke, suggesting several pathways may be present.6,8,11,12,14
The reason for these strong associations is not clear. There have been virtually no experimental studies to assess the health effects of prolonged sleep, so hypotheses on the physiologic effects of long sleep are limited. Many have speculated that this association is due to residual confounding, despite the large number of covariates that many of these studies have incorporated in their multivariate modeling. Clearly, a confounder causing such a large effect must be prevalent, strongly associated with prolonged sleep duration, and strongly associated with poor health outcomes.
This study, by combining data on the first 2 prerequisites (prevalence and association with long sleep) with a reasonable range of estimates for the third requirement (association with mortality), attempts to identify the variables that could produce the greatest alterations in the long sleep–mortality association and, therefore, result in a substantial statistical association.
One caveat that should be made clear is that this analysis, because of its cross-sectional nature, does not distinguish between causal associations, in which the third factor is on the causal pathway, and noncausal associations, in which the third factor is a confounder. The statistical meaning of the CRR is identical in either circumstance: it is the ratio of the unadjusted long sleep–mortality RR to the adjusted RR and, therefore, determines to what degree a factor is responsible for the mathematical association between long sleep and mortality. If the factor is on a causal pathway between long sleep and death (ie, long sleep causes the factor), then the CRR assesses to what degree the association is mediated through that factor. On the other hand, if the factor is a cause of long sleep, then it is a confounder, and the CRR assesses the distortion in the true causal association produced by that factor. Further biologic research is required to differentiate whether variables that importantly affect estimates of the association between long sleep and mortality are causes or consequences of long sleep duration.
The association between long sleep and mortality in the literature is fairly strong. Compared with normal sleepers, those sleeping 9 hours had an adjusted mortality RR of 1.27 to 1.29 in Cancer Prevention Study I, 1.17 to 1.23 in Cancer Prevention Study II, 1.3 in National Health and Nutrition Examination Survey I, 1.40 in NHS, 1.27 to 1.57 in Japan Collaborative Cohort Study, and 1.5 to 1.8 in the Framingham Study, with even stronger associations in those sleeping 10 or more hours.4,6–8,10,30
Thus, if no causal association between long sleep and mortality exists, a confounder (or set of confounders) that was not adequately measured in these studies must have a CRR on the order of 1.3 to 1.5 to be responsible for the RRs that have been reported in the literature. While many factors are associated with long sleep, our analyses demonstrate that the list of factors capable of producing a CRR high enough to produce the reported long sleep–mortality associations is much more limited.
A broad range of factors was assessed for their association with long sleep, including demographic, lifestyle, socioeconomic, medical, psychological, and sleep-related variables. The a priori hypotheses were that prolonged sleep might be related to depression, social isolation, or chronic medical diseases, or a combination thereof. In each case, the relationship may be causal in either direction. Long sleep times may interfere with the development of interpersonal relationships and educational or job successes, but, alternatively, a lack of such activities would allow for more time sleeping. Chronic medical conditions may limit other activities, thereby facilitating longer sleep. In addition, inflammatory conditions might produce long sleep through the soporific effects of elevated cytokines.31,32
However, the opposite causal relationship may also be posited. Habitual long sleepers appear to have longer biologic nights, characterized by longer times with elevated serum melatonin and increasing levels of serum cortisol, as well as more time with a depressed body temperature.33
These differences may have adverse effects on immune function or other biologic processes. In addition, long sleep durations may lead to complications of immobility, such as a prothrombotic state.
A wide range of variables were associated with long sleep, including numerous medical conditions, pregnancy, sleep disorders, low levels of exercise, extremes of alcohol intake, and measures of social isolation. Although the magnitude of some of these associations was quite large, e.g., multiple sclerosis and lupus, the rarity of these disorders makes them unable to explain the strong association of sleep time to mortality reported in the literature. Depression-related measures, on the other hand, both because of their high prevalence and strong association with long sleep, appear to be the most likely to have a strong effect on the long sleep–mortality association, whether as a confounder or an intermediate variable in the causal pathway. Long sleep has also been strongly associated with depression and depressive symptoms in other cross-sectional analyses.4,7,34
In the Japan Collaborative Cohort Study, having 2 or more depressive symptoms was more than twice as likely among those sleeping 10 hours compared with 8 hours.4
The exact CRR for depression clearly depends on the strength of its association with mortality. Depression has been strongly associated with poor health outcomes, including an elevated risk for heart disease, cancer, and mortality, with rate ratios in the 1.5 to 2.0 range.17–19
Thus, the CRR for depression is likely in the range of 1.06 to 1.10. Therefore, poor control of differences in the prevalence of depression between long sleepers and normal sleepers may have an important effect on estimation of the long sleep–mortality association. These data strengthen the hypothesis that depression plays a fundamental role in linking long sleep with disease. Further research is still needed to understand whether depression is a confounder or causal intermediate in the association of long sleep with mortality. An abnormal sleep pattern, including prolonged sleep, is a diagnostic criterion for depression.35
Sleep restriction improves depression,16, 36,37
however, and self-report of “sleeping too much” has been reported as an independent predictor for the development of major depression 1 year later.38
These observations suggest that depression could be, to some degree, a causal intermediate between long sleep and mortality.
The use of antidepressant medications was also strongly associated with long sleep times. In this setting, antidepressant use may simply serve as a surrogate marker for more-severe depression. However, the strong association between antidepressant use and long sleep in analyses stratified for depression suggests that antidepressants may themselves have a soporific effect. Interestingly, the strength of association was similar for selective serotonin reuptake inhibitors and the older antidepressants, despite the clinical belief that older agents such as trazodone and tricyclic agents are much more sedating. Another explanation of this finding is that healthcare providers interpret long sleep as a sign of depression and try to treat this symptom with antidepressants. Clearly, more research into the relationships between sleep, depression, and antidepressant use is required.
Based on our results, measures of low socioeconomic status, such as lack of employment, low household income, or low perceived societal status, were also strongly associated with long sleep. The CRR for employment status was of similar magnitude to that of depression. Whereas the CRRs for income and perceived societal status were lower, this was in large part due to the low prevalence of the lowest classes of these variables in this relatively homogenous cohort of educated healthcare professionals. The CRRs for these factors in the general population would likely be much greater. Low socioeconomic status is also strongly associated with increased disease risk, due to both lack of access to health care as well as poorer quality of care. Thus, understanding the associations between long sleep and low socioeconomic status, as well as detailed measurements of socioeconomic status, in future epidemiologic studies of sleep duration will be vital in fully understanding whether long sleep time causes excess mortality.
Although medical disorders were associated with increased sleep time, these associations were relatively weak, except for certain rare disorders. Grouping together all medical disorders and assuming they double the mortality rate results in a CRR of only 1.05. Certain diseases such as cancer, heart disease, and stroke may be more strongly associated with death, but even assuming an RR of 4 results in CRRs of 1.03, 1.04, and 1.02, respectively, for these diseases. Although, in populations with a high prevalence of these conditions, their impact might be greater, these results make the hypothesis that the observed long sleep–mortality association is due to confounding by underlying medical disorders less likely.
There are several limitations to this study that should be noted. Sleep duration was obtained by subjective report to a questionnaire. We have previously demonstrated that questionnaire responses correlate well with sleep diaries.7
However, whether long sleep represents an increase in actual time spent sleeping or just increased time in bed is not clear because no objective measurement of sleep was attempted. Nonetheless, because all of the published epidemiologic associations of long sleep with adverse health outcomes have been based on subjective reports of sleep, we believe this work has direct relevance in explaining those findings. Further research is needed to validate questionnaire reports of prolonged sleep with objective measures such as actigraphy.
Second, information about sleep-duration predictors was also based on questionnaire reports and so is susceptible to misclassification. However, the fact that our cohort was a medically educated population should limit the extent of this misclassification. There is also no reason to believe that there were different levels of misclassification between average and long sleepers so that the effect of misclassification would only tend to weaken associations. Thus, the true association of the measures assessed in this work with long sleep may be underestimated.
Third, we did not have actual data on the mortality RRs for the various factors considered in this study. Instead, we were forced to use a range of RR estimates between 1.25 and 4.0 to generate the CRRs. This range, we believe, covers the plausible RRs for most factors associated with disease. For example, one of the best studied risk factors for disease, cigarette smoking, had an estimated mortality rate ratio of 1.68 in the original Surgeon General’s report warning on the dangers of smoking.39
More recently in the Cancer Prevention Study II cohort, the RR for smoking was estimated at 1.8 in women and 2.1 in men.40
Thus, although lack of knowledge on the exact mortality RR for each factor prevents us from precisely estimating the true CRR, we believe our ability to identify those variables with the capacity to importantly change the statistical association between long sleep and mortality is preserved.
Fourth, although the CRR is robust when utilized to assess the effect of a confounder, use of the CRR may provide a biased estimate of the degree to which the effect of long sleep is mediated through a causal intermediate.41
However, because the goal of our study was to provide insight into which factors are most likely to be responsible for the long sleep–mortality association, we believe a qualitative interpretation of our results is still possible and valid.
Finally, our analyses considered the possibility of only a single confounder or causal intermediate. It is likely that multiple confounding, intermediate factors, or a combination of confounding and intermediate factors exist and that they may interact in nonlinear ways to affect the long sleep–mortality association. Although estimating the magnitude of effect that multiple confounders would have is beyond the scope of this work, we believe that the factors with the greatest effect in univariate analyses are also the most likely to be involved in causing large changes in a scenario of multiple interacting factors.
Overall, this work suggests that there are many predictors of a prolonged sleep duration that could potentially explain the statistical association between long sleep and mortality. However, when considered from the standpoint of confounding RRs, which rely on the strength of association with long sleep, prevalence in the population, and the strength of association with mortality, the number of possible explanations is greatly narrowed. Chief among the candidates are depression and low socioeconomic status. Future research on the health effects of long sleep should focus on the relationships between sleep habits, depression, and socioeconomic status.