Short and long sleep are positively associated with obesity, diabetes,
hypertension, and cardiovascular disease among adults in the United States
Orfeu M. Buxtona,1,*, Enrico Marcellib,1
aBrigham and Women’s Hospital, Department of Medicine, Harvard Medical School, Division of Sleep Medicine, BLI-438, 221 Longwood Avenue, Boston, MA 02115, United States
bDepartment of Sociology and Center for Behavioral and Community Health Studies (BACH), San Diego State University, San Diego, CA, United States
a r t i c l e i n f o
Available online 16 June 2010
Social ecological model
a b s t r a c t
Research associates short (and to a lesser extent long) sleep duration with obesity, diabetes, and
cardiovascular disease; and although 7e8 h of sleep seems to confer the least health risk, these findings
are often based on non-representative data. We hypothesize that short sleep (<7 h) and long sleep (>8 h)
are positively associated with the risk of obesity, diabetes, hypertension, and cardiovascular disease; and
analyze 2004e2005 US National Health Interview Survey data (n ¼ 56,507 observations, adults 18e85)
to test this. We employ multilevel logistic regression, simultaneously controlling for individual charac-
teristics (e.g., ethnoracial group, gender, age, education), other health behaviors (e.g., exercise, smoking),
family environment (e.g., income, size, education) and geographic context (e.g., census region). Our
model correctly classified at least 76% of adults on each of the outcomes studied, and sleep duration was
frequently more strongly associated with these health risks than other covariates. These findings suggest
a 7e8 h sleep duration directly and indirectly reduces chronic disease risk.
? 2010 Elsevier Ltd. All rights reserved.
increasing for decades, with nearly two-thirds now overweight or
obese. Obesity predicts a broad range of health risks, including
& Flegal, 2007). As ‘actual causes’ of death, obesity, diabetes, hyper-
tension, and CVD (McGinnis & Foege, 1993) are critical federal
research and public health priorities (U.S. Department of Health and
Human Services, 2000a: pp. B18-1eB18-17; U.S. Department of
Health and Human Services, 2000b: pp. B19-1eB19-42).
In addition to individual characteristics (e.g., age), a variety of
risk factors affecting obesity, diabetes, hypertension, and CVD have
been identified, including individual behaviors (e.g., diet, exercise,
smoking); access to insurance and care; and physical, economic,
and social environment (e.g., physical context, demographic
composition). For example, lower socioeconomic status has been
estimated to be associated with lower levels of treatment and
Abbreviation: Body mass index, BMI; Cardiovascular disease, CVD; Coronary
heart disease, CHD; C-reactive protein, CRP; High blood pressure, HBP; National
Health Interview Survey, NHIS
* Corresponding author. Tel.: þ1 6175079177.
E-mail address: firstname.lastname@example.org (O.M. Buxton).
1Both authors contributed equally to this paper.
0277-9536/$ e see front matter ? 2010 Elsevier Ltd. All rights reserved.
a higher prevalence of chronic disease morbidity/mortality
(Uchino, Cacioppo, & Kiecolt-Glaser, 1996). Far more limited
information supports the influence of sociodemographic factors on
sleep duration (Hale, Peppard, & Young, 2007) and sleep disorders,
or on how these in turn are associated with or influence health
outcomes (Bliwise, King, Harris, & Haskell, 1992; Patel, 2007).
Sleep duration and quality may contribute to the increasing inci-
dence of chronic disease. The average sleep duration of US adults
appears to have fallen to nearly 7 h per night (Hale, 2005; Hale et al.,
2007; National Sleep Foundation, 2003; National Sleep Foundation,
2005; National Sleep Foundation, 2006; Roffwarg, Muzio, &
Dement, 1966). Insufficient sleep duration has been linked to
elevated body mass index (Hasler et al., 2004; Kohatsu et al., 2006),
weight gain (Patel et al., 2004; Patel & Hu, 2008), obesity (Cizza,
Skarulis, & Mignot, 2005; Gangwisch, Malaspina, Boden-Albala, &
Heymsfield, 2005; Taheri, Lin, Austin, Young, & Mignot, 2004;
Vioque, Torres, & Quiles, 2000), metabolic dysfunction (Karlsson,
Knutsson, Lindahl, & Alfredsson, 2003), and diabetes mellitus (Ayas
et al., 2003; Gottlieb et al., 2005; Hayashino et al., 2007; Knutson,
Ryden, Mander, & Van Cauter, 2006; Mallon, Broman, & Hetta,
2005; Yaggi, Araujo, & McKinlay, 2006). Modulations of cardiovas-
cular function by sleep duration have been demonstrated for eleva-
tions of blood pressure (Lusardi et al., 1999); (Meier-Ewert et al.,
2004), consistent with observed associations of sleep duration with
bloodpressure (Gottliebet al.,2006;Meier-Ewertet al.,2004; Sakata
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Social Science & Medicine 71 (2010) 1027e1036
et al., 2003) and CVD (Wingard & Berkman, 1983). And short sleep
duration is associated with premature mortality (Dew et al., 2003;
Kripke, Garfinkel, Wingard, Klauber, & Marler, 2002; Mallon,
Broman,&Hetta,2002;Patelet al.,2004; Wingard&Berkman,1983).
Although seven-to-eight-hour sleep duration is generally
associated with the least health risk, results typically are based on
non-representative samples. Epidemiologic studies associate short
sleep with obesity and diabetes [reviewed in (Knutson & Van
Cauter, 2008; Spiegel, Knutson, Leproult, Tasali, & Van Cauter,
2005)], consistent with the results of laboratory studies of sleep
restriction or disruption (Spiegel, Leproult, L’Hermite-Balériaux,
et al., 2004; Spiegel, Leproult, & Van Cauter, 1999; Tasali, Leproult,
Ehrmann, & Van Cauter, 2008). Datasets with a sufficient number
of observations of long-sleeping individuals generally find an
elevated risk of disease or mortality, with socioeconomic status as
a strong correlate, leading to the notion that ‘too much’ sleep is also
a health risk (Patel, 2007). The US National Health and Nutrition
Examination Survey (NHANES) I national cohort follow-up data
from the early 1990s revealed an association of long sleep duration
with stroke and coronary heart disease (Qureshi, Giles, Croft, &
Bliwise, 1997) and a cross-sectional association of short sleep
duration with increased obesity (Gangwisch et al., 2005). There is
a compelling need to identify the long-term risks to human health
involving sleep or sleep disorders (Institute of Medicine, 2006).
We test the hypothesis that both short (<¼6 h) and long (>¼9 h)
self-reported sleep duration are positively associated with risk of
chronic diseases, obesity, diabetes, hypertension, and CVD using
amultilevel logistic regressionapproachinfluenced by developments
in social epidemiology (Berkman, 2000; Committee on Assessing
Interactions among Social, Behavioral, and Genetic Factors in Health,
2006; Evans & Stoddart, 1990; Marcelli & Heer, 1997; Studenmund,
2006: pp. 449e451) that controls for other individual characteris-
tics, health behaviors other than sleep, health insurance coverage,
family environment, and geographic context.
Source of data
We merged the Person, Adult Sample, Household, and Family
files of the 2004e2005 US National Health Interview Survey (NHIS:
http://www.cdc.gov/nchs/nhis.htm) at the individual level to
estimate the association between short or long sleep and obesity,
diabetes (type 2), high blood pressure (HBP), or CVD among US
residents aged 18e85. Data were collected by approximately 400
interviewers using standard computer-assisted personal inter-
viewing (CAPI) procedures. Although the NHIS attempts to collect
data from all adult members of each randomly selected household
as part of the Family Core component, only one adult per family is
randomly selected for the Sample Adult questionnaire. This adult
responds directly unless he or she is physically or mentally
incapable, in which case a knowledgeable proxy is permitted to
answer (National Center for Health Statistics, 2006).
Of the 71,287 adults in the Person File in 2004, 31,263 remained
after merging with Adult Sample files. Of the 68,299 adults
included in the 2005 Person File, 31,383 remained, providing a total
initial sample size of 62,646. After dropping all observations for
those who did not provide a valid response for any variables in our
analysis (Table 1), our final sample (56,507) is weighted using
NHIS-provided person-level sample weights.
Consistent with a multifactorial, multilevel model of health
(Evans & Stoddart, 1990), and building on recent work on the
demography of sleep by Hale and colleagues (Hale, 2005; Hale
et al., 2007), we control for factors grouped into four analytical
categories e (1) individual sociodemographic and economic
characteristics (i.e., age, gender, ethnoracial group, nativity, marital
status, educational attainment, labor force participation, health
insurance coverage as a measure of medical care access); (2)
individual behaviors (i.e., smoking, alcohol consumption, vigorous
exercise); (3) individual health conditions (i.e., psychological
distress); and (4) extra-individual family and regional context (i.e.,
college-educated family member; census region). Inclusion of
a variable for whether a family member has been graduated from
college is included because research has shown that family envi-
ronment can profoundly influence health and socioeconomic
behavior, and consequently long-term health. While such an
approach does not investigate the within- and between-group
variable interactions likely to influence our four health outcomes, it
does permit analysis of a random sample of the entire non-insti-
tutionalized U.S. adult civilian population to estimate the marginal
association of sleep on important chronic diseases. Although we
estimated how current smoking and moderate exercise were
associated with our four health outcomes, these are not included in
our final models because they were not statistically significant at
the 90 percent confidence level, and alternative measures for these
health behaviors were significant and included (e.g., vigorous
leisure-time physical activity, being a former smoker). Table 1 lists
the definitions, means, and standard deviations of all variables
included in the models. Importantly, although climate and other
place-based factors vary widely by geography and influence the
four outcomes we study in this article, the NHIS data only provide
regional-level identifiers. We control for these rather large aggre-
gated areas in our models, but recognize that it would be better to
have lower-level geographic identifiers.
We employlogistic regression to estimate whether respondents’
answers to the following question e “On average, how many hours
of sleep doyou get in a 24-hperiod?” ewereassociated with having
been obese, diabetic, or having had HBP or CVD. We created three
categorical variables from the sleep variable, enabling us to
examine whether those who sleep less or more than is conven-
tionally considered healthy were likely to have one of the four
health outcomes. Obesity is defined as a body mass index (BMI)
?30 kg/m2. Our remaining three outcome variables are also
dichotomous and defined by respondents’ answers to the question
“Have you EVER been told by a doctor or other health professional
that you had diabetes; HBP; or CVD (including coronary heart
disease (CHD), angina, heart attack, stroke or “other” heart condi-
tion)?” The list of variables for each of our models included
comorbidity factors incrementally, such that the diabetes model
included obesity, the HBP model included obesityand diabetes, and
the CVD model included obesity, diabetes, and HBP.
We estimated how short sleep (<7 h/night) and long sleep (>8
h/night) were associated with each health outcome, employing
a sociogeographic model to incorporate potentially important
social and behavioral components often overlooked in more
traditional clinically-focused notions of cardiometabolic disorders.
Since responses were in integers only, comparisons of short and
long sleep are made to self-reported seven or 8 h of sleep per night.
Regression results are displayed in two ways. First, they are
reported together in four separate columns and in conventional
summary format as estimated parameter coefficients (Table 2).
Second, wetransformed each regression coefficient into a change in
the probability of an outcome equaling one as the result of a one-
unit change in an explanatory variable for each of our four
outcomes separately (Figs. 1e4). Each filled bar in these figures
represents an estimated relationship between an explanatory and
outcome variable that is statistically significant at the noted level
O.M. Buxton, E. Marcelli / Social Science & Medicine 71 (2010) 1027e1036
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