Short and long sleep are positively associated with obesity, diabetes, hypertension, and cardiovascular disease among adults in the United States

Brigham and Women's Hospital, Department of Medicine, Harvard Medical School, Division of Sleep Medicine, BLI-438, 221 Longwood Avenue, Boston, MA 02115, United States.
Social Science [?] Medicine (Impact Factor: 2.56). 09/2010; 71(5):1027-36. DOI: 10.1016/j.socscimed.2010.05.041
Source: PubMed

ABSTRACT Research associates short (and to a lesser extent long) sleep duration with obesity, diabetes, and cardiovascular disease; and although 7-8 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 2004-2005 US National Health Interview Survey data (n=56,507 observations, adults 18-85) to test this. We employ multilevel logistic regression, simultaneously controlling for individual characteristics (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 7-8 h sleep duration directly and indirectly reduces chronic disease risk.

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    ABSTRACT: Sleep disorders affect approximately 30% of the adult population , due to this fact, it is considered an important public health issue. Some medical conditions are correlated with sleep disturbances, including: obesity, diabetes, cardiovascu-lar disease, hyperactivity disorder and early mortality. The current mainstream sleep disorder detection and assessment method, the laboratory polysomnography, is very expensive and inconvenient for patients who are extracted from their own sleep-environment. Aiming to avoid the high costs and to perform an assessment in loco, we present in this paper a non-invasive sleep-environment monitoring system in order to aid the detection of environmental factors that may be contributing to poor sleep. The stand-alone device was designed in order to provide robustness, scalability and us-ability to a completely built-in sleep assessment system. We highlight that the main goal of this in-home device is to give more accurate information to physicians and technical staff, assisting in the screening process, reducing costs and helping to improve the well-being of people with sleep disorders. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit Keywords Sleep-environment monitoring, sleep assessment, non-invasive monitoring system, in-home sleep monitoring.
    PErvasive Technologies Related to Assistive Environments, Greece; 07/2015
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    ABSTRACT: • Socio-demographic, medical conditions, health risk, and social and behavioral factors contribute to insufficient sleep
    Sleep Medicine 03/2015; DOI:10.1016/j.sleep.2015.02.533 · 3.10 Impact Factor
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    ABSTRACT: Objective: This study tested a Self-Regulation Resource Model (SRRM) of self-compassion and health-promoting behavior intentions in emerging adults. The SRRM posits that positive and negative affect in conjunction with health self-efficacy serve as valuable self-regulation resources to promote health behaviors. Methods: An online survey was completed by 403 emerging adults recruited from the community and a Canadian University in late 2008. Multiple meditation analyses with bootstrapping controlling for demographics and current health behaviors tested the proposed explanatory role of the self-regulation resource variables (affect and self-efficacy) in linking self-compassion to health behavior intentions. Results: Self-compassion was positively associated with intentions to engage in health-promoting behaviours. The multiple mediation model explained 23 percent of the variance in health behavior intentions, with significant indirect effects through health self-efficacy and low negative affect. Conclusion: Interventions aimed at increasing self-compassion in emerging adults may help promote positive health behaviours, perhaps through increasing self-regulation resources.
    03/2015; 2:218-222. DOI:10.1016/j.pmedr.2015.03.006


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