The present study describes an analysis of the 2009 BRFSS data, investigating the associations between sleep duration and/or sleep insufficiency and cardiometabolic health outcomes. Further, the analysis attempts to specify whether cardiometabolic outcomes are more directly linked to sleep duration or insufficient sleep. Our hypotheses were partially confirmed. When sleep duration was examined alone, significant associations were found with all outcomes (BMI, obesity, diabetes, hypertension, hypercholesterolemia, heart attack, and stroke). When sleep insufficiency was examined separately, significant associations were found for BMI, obesity, hypertension, and hypercholesterolemia. When sleep duration and sleep insufficiency were examined together, the following were observed: (1) both sleep duration and sleep insufficiency contributed unique variance explanation of hypertension risk, (2) sleep insufficiency alone accounted for risk of hypercholesterolemia, and (3) sleep duration alone accounted for risk of BMI and obesity (short sleep duration), as well as heart attack and stroke (both short and long sleep duration).
Our results indicated that sleep duration, when examined alone, was significantly associated with all cardiometabolic health variables investigated. Similar results were obtained in several other studies attempting to link sleep duration to similar health outcomes. For example, a study [57
] found both short and long sleep duration (≤5 hours or ≥9 hours) to be at increased risk of higher BMI, diabetes, and hypertension. In a recent meta-analysis, short and long sleep duration (≤5–6 hours and >8–9 hours) were associated with a greater risk of developing or dying of stroke[58
]. Risk for hypercholesterolemia decreased with each additional hour of sleep for females [15
]. These findings also support others, from a number of research groups, who found that sleep duration is associated with obesity[12
], and cardiovascular disease[15
]. The current study extends these findings, exploring an alternative measurement for sleep (sleep insufficiency) and comparing it alongside and against sleep duration.
Sleep insufficiency effects, examined alone, demonstrated significant associations with increased BMI and risk for obesity, hypertension, and hypercholesterolemia. A study by Shankar, Syamala, and Kalidindi [18
] indicated similar results in their investigation of insufficient rest or sleep in relation to cardiovascular disease, diabetes, and obesity. They found an overall positive association in both age/sex-adjusted and multivariable-adjusted models. Further, they analyzed sleep insufficiency separately in relation to coronary heart disease, stroke, diabetes, and obesity, all of which demonstrated a significant positive association in both models. The question of sleep insufficiency attempts to indicate healthy sleep more directly than with a measure of sleep duration, which may exclude important individual differences in sleep need.
When weighed against each other, effects of sleep duration and insufficiency differed among cardiometabolic health outcomes. Sleep duration explained increased BMI and risk for obesity on its own, suggesting that sleep duration, rather than differences in perceived sleep need, regulates weight. Although the mechanism of this effect is unknown, short sleep has previously been associated with altered metabolic hormones (leptin and ghrelin)[60
], energy balance[63
], time available to eat[63
], and timing of meals [65
]. Previous studies have also shown that short sleep duration is associated with a higher consumption of high-calorie foods [65
Hypercholesterolemia was solely explained by sleep insufficiency, suggesting that short sleep duration may not be directly linked to higher cholesterol levels. This contradicts studies mentioned above. However, it may be explained by the results of a study by Mackiewicz et al. [69
] that found cholesterol metabolism to be an overrepresented category of genes that increased expression during sleep, and decreased representation during sleep deprivation. This may suggest that a more direct route for detecting sleep-related effects of hypercholesterolemia would be to measure sleep insufficiency rather than short sleep duration.
Both sleep duration and sleep insufficiency maintained significant effects on hypertension. This suggests that there are separate effects of sleep duration and perceived insufficiency. To more clearly understand this relationship, it should be explored in further studies. Perhaps the independent effects of short sleep duration reflect the body’s inability to perform basic processes, whereas the independent effects of perceived insufficiency reflect the psychophysiological stress of perceived unmet needs despite adequate duration.
Regarding model fit, across models, the combined effects of Sleep Insufficiency and Sleep Duration (Model 3) produce the greatest model fit. However, this is largely due to the predictive power of Sleep Duration. Within a given model, covariate adjustment dramatically increases the model fit, suggesting that the set of covariates investigated are greater risk factors for the outcomes than Sleep Duration and Insufficiency. The persistence of significant effects for both Sleep Duration and Sleep Insufficiency after covariate adjustment suggests that, while their contribution to the explanation of cardiometabolic disease is low, it is meaningful. As it pertains to model fitness, Cohen[70
] attributes R2
values <0.09 as low fit, 0.09–0.25 as medium fit, and >0.25 as high fit. For pseudo-R2
values, these limits are typically relaxed, with pseudo-R2
values >0.20 considered to be high fit. While our adjusted models for BMI (R2
=0.131), Obesity (pseudo-R2
=0.083), and Hypercholesteremia (pseudo-R2
=0.101) indicate a medium model fit, all of the other models show a high level of model fit (pseudo-R2
This study adds to the literature in several relevant ways. First, it is the first study to evaluate, at the population level, whether the elevated risk of adverse health outcomes associated with sleep duration is explained by perceived insufficient sleep. Put simply, this study explores the role of perceived unmet sleep need in the relationship between sleep duration and health. Although insufficient sleep was associated with adverse outcomes, our data show that the variance explained is not greater than or separate from that explained by sleep duration, in most cases. Second, this study documents a population-level association between sleep duration/insufficiency and cardiometabolic disease, using a population-weighted dataset, adjusting for a very wide array of potential confounders. The cardiometabolic endpoints investigated represent some of the leading causes of death (e.g., myocardial infarction and stroke), or major risk factors for those events (e.g., obesity, hypertension, hypercholesterolemia). Third, this study documents that among habitual short sleepers, two sub-groups emerge: the “short” sleepers who are more populous, and the rarer “very short” sleepers, who report <5 hours per night. We find large differences in risk profiles between these groups. Though other studies have found different risk profiles in different sleep durations, this has not been investigated before in this way.
Although this study has many qualities that strengthen its validity, some limitations should be noted. First, the sleep duration and sleep insufficiency questions may be problematic. Both of these sleep variables (though sleep insufficiency to a lesser degree) may not take sleep quality into account. Second, the feeling of obtaining insufficient “rest or sleep” may not necessarily be an accurate measurement of sleep insufficiency. This compound question conflates “rest” with “sleep,” such that responses may not completely represent a perceived lack of sleep, rather a perceived lack of rest. Also, perceived insufficient sleep may be biased by perceived normative standards, such that individuals may subjectively feel sufficiently rested, but, knowing that the normative amount of sleep is approximately 8 hours, may report “insufficient sleep” based on this knowledge rather than personal experience. This study is cross-sectional and therefore inferences regarding direction of relationships and/or causality are not possible. A further limitation includes not adjusting for sleep disorders: sleep apnea and insomnia have both been shown to be associated with cardiometabolic health risks, and the findings from the present study may partially represent these relationships. Adjusting for mental illness, body mass index may have captured a proportion of insomnia and sleep apnea respectively. Further, a measurement of sleep insufficiency cannot account for possible effects of excessive sleep.
These data are limited in that they are self-reported values obtained through surveys that have not been validated relative to objective and/or prospective methods of assessing sleep. These values could be biased by a number of factors, such as the desire to appear “normal” or the desire to express dissatisfaction, confusion as to whether estimates should include time in bed awake and/or napping during the day, recall biases, etc. This is a problem with all such surveys, and results should be interpreted with appropriate caution.
Telephone interviews are associated with limitations. Accuracy of reporting depends largely on the honesty and comprehension of the participant. The procedure is impersonal and it is more difficult to ascertain the accuracy and specificity of responses than during an in-person interview or with the use of a more specific set of questions. However, a study by Zallek et al. [71
] found that a single question about sleepiness, “Please measure your sleepiness on a typical day” (scale of 0–10), was just as accurate as the Epworth Sleepiness Scale of 8 questions in predicting the likelihood of falling asleep during a variety of activities. Also, although the BRFSS specific weighting procedures reduce bias in this sample, it is possible that non-response to the sleep duration and/or sleep insufficiency items is disproportionately represented by certain groups. When respondents included in analysis were compared to those that were not included, few (and mostly minor) differences were found. Since only 1.9% of the sample did not respond to the sleep-related items, even in the case that certain groups were more likely to be non-responders, this should minimally affect statistical outcomes, as there is still a sufficient number of responses for reliable inferences.
The main strength of asking for information over 30 days is that it allows for the assessment of a typical pattern, rather than assessing the past few nights, which may or may not be representative. In this way, it may better approximate long-term sleep curtailment. However, there is no empirically validated way of assessing this, and the item used in the present study has unknown validity as an indicator of insufficient sleep over the long term. Another issue is that this does not allow for a differentiation of workdays and weekends, nor does it differentiate those with stable sleep schedule from those with variable schedules that include sleep loss and subsequent recovery. In particular, recent data on the concept of Social Jetlag highlights the relevance of this, as does recent data showing that laboratory-based sleep curtailment in the presence of circadian misalignment is associated with elevated obesity and diabetes risk factors.
Because all of the data in the present study were collected at the same time, no inferences regarding causality can be drawn; it may be the case that sleep is a contributing factor to cardiometabolic disease, but it may also be the case that cardiometabolic disease impacts on sleep. Existing evidence supports both directions, so this may be a reciprocal relationship. Also, although the presence of a large array of potential confounders was adjusted for in the present analyses, it is possible that the relationships are sufficiently non-linear that the variance accounted for by these covariates is misestimated. Further, other unmeasured factors may have confounded the relationships between sleep and health outcomes.
The present study explored self-reported sleep duration alone, perceived sleep insufficiency alone, and the combined effects of these two variables on a number of cardiometabolic health outcomes, including BMI, obesity, hypertension, hypercholesterolemia, diabetes, heart attack, and stroke. Sleep duration was associated with all outcomes. Sleep insufficiency was associated with BMI, obesity, hypertension, and hypercholesterolemia. When sleep duration and sleep insufficiency were examined together, the sleep duration effects alone remained for BMI, obesity, heart attack and stroke; sleep insufficiency effects alone remained for hypercholesterolemia, and both variables contributed significant variance explanation of hypertension.