Original Research published: 05 June 2015 doi: 10.3389/fneur.2015.00112
S Michael A. Grandner 1, 2*, Nicholas J. Jackson 3, Bilgay Izci-Balserak 1, 4, 5, Rebecca A. Gallagher 1, 4, Renee Murray-Bachmann 6, Natasha J. Williams 7, Nirav P. Patel 8† and Girardin Jean-Louis 7 Center for Sleep and Circadian Neurobiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA, 2 Behavioral Sleep Medicine Program, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA, 3 Quantitative Psychology Program, Department of Psychology, University of Southern California, Columbia, SC, USA, 4 Division of Sleep Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA, 5 School of Nursing, University of Pennsylvania, Philadelphia, PA, USA, 6 Department of Medicine, State University of New York Downstate Medical Center, Brooklyn, NY, USA, 7 Division of Internal Medicine, Center for Healthful Behavior Change, New York University Medical Center, New York, NY, USA, 8 Department of Medicine, Reading Hospital and Medical Center, Reading, PA, USA 1
Edited by: Mehmet Y. Agargün, Yuzuncu Yil University School of Medicine, Turkey Reviewed by: David G. Harper, McLean Hospital, USA Timo Partonen, National Institute for Health and Welfare, Finland Jeanetta C. Rains, Elliot Hospital, USA *Correspondence: Michael A. Grandner, Center for Sleep and Circadian Neurobiology, University of Pennsylvania, 3624 Market Street, Suite 205, Philadelphia, PA 19104, USA
[email protected] Deceased. Dr. Patel contributed to the study design, data analysis, interpretation, and manuscript preparation, and he knew the paper was being submitted and agreed to submission. †
Specialty section: This article was submitted to Sleep and Chronobiology, a section of the journal Frontiers in Neurology Received: 25 April 2014 Accepted: 04 May 2015 Published: 05 June 2015 Citation: Grandner MA, Jackson NJ, Izci-Balserak B, Gallagher RA, Murray-Bachmann R, Williams NJ, Patel NP and Jean-Louis G (2015) Social and behavioral determinants of perceived insufficient sleep. Front. Neurol. 6:112. doi: 10.3389/fneur.2015.00112
Frontiers in Neurology | www.frontiersin.org
Insufficient sleep is associated with cardiometabolic disease and poor health. However, few studies have assessed its determinants in a nationally representative sample. Data from the 2009 behavioral risk factor surveillance system were used (N = 323,047 adults). Insufficient sleep was assessed as insufficient rest/sleep over 30 days. This was evaluated relative to sociodemographics (age, sex, race/ethnicity, marital status, region), socioeconomics (education, income, employment, insurance), health behaviors (diet, exercise, smoking, alcohol), and health/functioning (emotional support, BMI, mental/physical health). Overall, insufficient sleep was associated with being female, White or Black/African-American, unemployed, without health insurance, and not married; decreased age, income, education, physical activity; worse diet and overall health; and increased household size, alcohol, and smoking. These factors should be considered as risk factors for insufficient sleep. Keywords: insufficient sleep, behavioral, social determinants, sleep duration, cardiometabolic disease, poor health
Introduction Sleep is an important public health issue, with chronic sleep insufficiency linked to many societal problems including motor vehicle accidents, industrial disasters, and medical and other occupational errors. Poor sleep is associated with chronic diseases including hypertension, depression, diabetes, and obesity, as well as higher mortality rates (1–3). Increased awareness of the importance of healthy sleep is needed (1). Previous research in other domains of healthy behavior have shown that in order to meet these demands, a more complete understanding of social and behavioral “determinants” is needed (4, 5). Social determinants of sleep have been previously explored; the very same social characteristics associated with good health such as a high level of education, being married, and being employed are also associated with higher sleep quality and healthy sleep duration (6, 7). Income plays a role in perceived sleep quality – as income decreases, sleep complaints increase (6, 8). Individuals with less education are more likely to experience insomnia (9) and report sleep disturbance (6). Living in an inner city environment has a negative effect on sleep duration – persons who live in unhealthy neighborhoods have an increased risk of unhealthy sleep (10–12). Blacks are at an increased risk for short sleep and/or poor sleep as compared to whites (13–15), and insufficient sleep among blacks is associated with greater cardiometabolic risk (7, 16). Employment also has an effect on sleep. The
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unemployed report poorer sleep than those who are employed (6). It is clear that socioeconomic variables are related to sleep complaints in general, such that increased socioeconomic status is associated with fewer sleep complaints. Although the preponderance of studies assessing the societal implications of sleep loss have focused on short sleep duration, rather than perceived insufficient sleep, these likely represent overlapping, though separate, constructs (2, 17). Very few studies have specifically addressed health effects of unmet sleep need independent of sleep duration. Shankar et al. (18) and Altman and colleagues (19) found that insufficient sleep was associated with cardiometabolic health. Earlier studies also studied perceived sleep insufficiency, finding associations with and reported poor general health, functional deficits (2005), and increased cortisol secretion and abnormal growth hormone metabolism (20). For our purposes, insufficient sleep is defined as reduction in sleep time of a magnitude associated with a negative health outcome (2). Although many associations have been documented, few studies have evaluated a broad array of social, behavioral, and health predictors in a nationally representative sample. Accordingly, the present study evaluated the social and behavioral determinants of insufficient sleep in a nationally representative sample. In this secondary data analysis, we utilized the 2009 behavioral risk factor surveillance system (BRFSS), which collected data on insufficient sleep from >300,000 individuals from all 50 states. Our hypotheses were that rates of insufficient sleep would be related to sociodemographic factors, socioeconomic factors, health behaviors, and health status. Further, we hypothesize that in fully adjusted analyses, effects will be attenuated but will remain significant, indicating that all of these factors contribute unique variance to insufficient sleep.
as “Non-Hispanic White,” “Black/African-American,” “Hispanic/ Latino,” “Asian/Other,” or “Multiracial.” Marital status was assessed as “Married,” “Never Married,” “Part of an Unmarried Couple,” “Divorced,” “Widowed,” or “Separated.” Household size was assessed as a continuous variable. Census regions were determined based on state.
Socioeconomics
Household income level was assessed categorically. Education level was assessed as either “Less Than High School,” “High School Graduate,” “Some College,” or “College Graduate.” Employment status was assessed as “Employed,” “Retired,” “Homemaker,” “Student,” “Unemployed,” or “Unable to Work.” Access to health insurance was assessed binomially (yes/no).
Health Behaviors
Alcohol use was assessed categorically (heavy drinking: >1 drink/ day in women, >2 drinks/day in men) and continuously (drinks/ month). Smoking was assessed as “Never,” “Former,” “Current Some Days,” or “Current Every Day.” Diet was assessed as daily servings of fruits/vegetables. Physical activity was assessed several ways. Categorical assessments evaluated the presence of any exercise in the past 30 days, and also assessed physical activity at work. Physical activity was assessed continuously as computed hours/day of moderate/vigorous activity.
Health and Functioning
Availability of emotional support was categorized as “Never,” “Rarely,” “Sometimes,” “Usually,” or “Always.” Body mass index was categorized as Underweight (30). Although self-reported height and weight may be unreliable due to reporting bias, these data have demonstrated their utility and have been accepted in other studies (23–27). Overall health was assessed categorically (“Very Poor,” “Poor,” “Good,” “Very Good,” or “Excellent”) and continuously as number of days in the past 30 days of subjective poor physical and mental health.
Materials and Methods Data from the 2009 BRFSS were used (21). The BRFSS is an annual, state based, random-digit-dialed telephone survey of adults in the United States. It is conducted by the Centers for Disease Control and Prevention and is designed to monitor health-related behaviors in the general population. For the present analyses, data from all 50 states and multiple territories were included. Response rate varied by state, with a median of 53.86% and a range of 37.90% (Oregon) to 72.78% (Guam). All analyses are weighted to account for differences in selection probability, lack of coverage or response, number of residential telephone lines in the home of each participant, and number of adults in the household. This increases generalizability of results by accounting for factors of location and accessibility, and characteristics including age, sex, and ethnicity (22).
Statistical Analyses
These variables were chosen a priori, as previous studies have found that many of these variables were related to sleep (6, 28). Complete case analysis was implemented. The general analytic strategy was to build a parsimonious model for a subsample of the data and then evaluate the model performance on the remaining sample. To facilitate this, the data were randomly split in half with one partition being treated as a training dataset (for model development) and the second partition being treated as a testing dataset (for evaluating model fit). Model development was conducted using stochastic gradient boosted modeling (GBM). GBM is a statistical (or machine) learning technique that combines the simplicity of decision trees with a high level of predictive accuracy, which is achieved through iteratively growing a large number of decision trees with each subsequent tree fit to the regularized (i.e., shrinkage) residuals from the previous tree. The number of trees, the shrinkage rate (learning rate), and number of terminal nodes in each tree (interaction depth) can be controlled and set empirically via cross-validation to maximize predictive accuracy. In these analyses, 10-fold cross-validation was used to determine the optimal [minimized root mean square error (RMSE)] interaction depth and number of decision trees. A shrinkage rate of 0.01 was used per. In the training dataset, 3000 gradient boosted trees were fit to
Sleep
The outcome of interest was “perceived insufficient rest or sleep” (insufficient sleep). This was measured using the item, “During the past 30 days, for about how many days have you felt you did not get enough rest or sleep?” Responses were divided by 4.28 to convert to days/week in order to facilitate interpretation.
Sociodemographics
Age was assessed as a continuous variable and categorized into groups. Sex was assessed categorically. Race/ethnicity was categorized Frontiers in Neurology | www.frontiersin.org
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the sleep insufficiency outcome using all 23 predictors. The primary measure from this analysis was relative influence. The relative influence of each predictor was calculated as the average across all trees of the empirical improvement (via RMSE) of the model from splitting a decision tree on a predictor. Model fit was assessed by examining the R2 (proportion of variance explained; R2) in the testing dataset from predictions using the coefficients from the training dataset. A non-parametric percentile bootstrap of 1000 replications was used to compare R2 between models. Stability of the selection of influential predictors was assessed using Pearson correlations of the relative influences between models. The GBM analyses were conducted using the gbm package in R version 2.14.0. All other analyses were conducted using STATA/SE version 12 (STATA Corp, College Station, TX, USA).
Table 1 | Characteristics of the total sample.
Results Sample Characteristics
The sample included a total of N = 323,047 individuals, representing all 50 states, Washington, DC, Puerto Rico, US Virgin Islands, and Guam. Characteristics of the sample can be found in Table 1. Response to the insufficient sleep item was very high, with 1.8% not responding. Analysis of characteristics of non-responders showed no notable differences to responders (Table S1 in Supplementary Material). Insufficient sleep for at least one night/week was reported in over half the sample, with >10% reporting insufficient sleep seven nights/week.
Variable
Category
Values
Insufficient sleep (days/week)
Mean (SD)
2.00 (2.33)
Age
18–24 (%) 25–29 (%) 30–34 (%) 35–39 (%) 40–44 (%) 45–49 (%) 50–54 (%) 55–59 (%) 60–64 (%) 65–69 (%) 70–74 (%) 75–79 (%) 80+ (%)
9.76 8.07 11.21 9.48 11.26 9.59 10.64 7.92 7.07 4.80 3.65 3.17 3.36
Sex
Female (%) Male (%)
50.57 49.43
Race/ethnicity
White (%) Black/African-American (%) Hispanic/Latino (%) Asian/other (%) Multiracial (%)
69.34 9.72 14.34 5.06 1.54
Household size
Mean (SD)
Education
Less than high school (%) High school (%) Some college (%) College graduate (%)
9.23 26.48 27.13 37.16
Income
