POSTER TEMPLATE BY: Modeling The Relationship Between Sleep Characteristics and Pediatric Obesity Student: Andrew Althouse, Advisor: Rebecca Nugent Carnegie Mellon University, Pittsburgh, Pennsylvania Introduction Age, Sleep Duration, and BMI Discussion Other Models Data PSQ 2 and Feel Upon Waking Profile of Subjects The Multivariate Model Future Work Acknowledgements & References  Obesity is on the rise in all age groups in the United States. An NHANES survey conducted in 1980 found 15.0% of adults to be obese (defined as having a Body Mass Index over 30); two decades later, that percentage had increased to 32.9%.  The same NHANES surveys found obesity to be on the rise in children as well. Obesity in children is measured as having a BMI over the 95 th percentile for their respective age and gender. Three age groups were examined:  Obesity in children ages 2-5 increased from 5.0% to 13.9%  Obesity in children ages 6-11 increased from 6.5% to 18.8%  Obesity in children ages increased from 5.0% to 17.4%  Recently, researchers have been investigating the relationship between sleep and obesity in adults. There has been less research on children.  Our goal is to explore the relationship between several characteristics of children’s sleep habits (e.g. duration, quality, consistency) and pediatric obesity. To that end, we have conducted a pilot study to prepare for large- scale research beginning in the next year.  Convenience sample of 77 subjects collected from a group of pediatrician referrals to a dietitian at Texas Tech University Health Sciences Center.  Subjects completed standard sleep questionnaires (Pediatric Sleep Questionnaires and Pediatric Daytime Sleepiness Scale) with supplemental questions about their daily habits with respect to their sleep routine, household routine, level of physical activity, and use of electronic media.  We chose to focus primarily on the variables related to sleep habits, quality of sleep, and consistency of sleep.  “Sleep Habits” Variables: Sleep Duration, Naps (Weekends)  “Sleep Quality” Variables: Pediatric Sleep Questionnaires, Pediatric Daytime Sleepiness Scale, Feel Upon Waking, Sleep in School  “Sleep Consistency” Variables: Difference in Weekday/Weekend Bedtime, Share Room  Age:  Mean years (SD = 3.43), Median years  Minimum 2.67 years, Maximum years  Gender: 48 females (62.3%), 29 males (37.7%)  Body Mass Index:  Mean kg/m 2 (SD = 7.37), Median kg/m 2  Minimum kg/m 2, Maximum kg/m 2  Age has a significant effect on both Sleep Duration and BMI **Note that in the table above, the Age*Sleep interaction variable has the opposite effect of the Age and Sleep variables. Therefore, we must consider the age cut-point, which is about 8.0 years. Above age 8, children that sleep more tend to have lower BMI. Below age 8, children that sleep more tend to have higher BMI. As mentioned previously, the interaction between PSQ 2 and Feel Upon Waking has a strong effect. The table below contains some sample calculations that demonstrate this relationship.  The second Pediatric Sleep Questionnaire asks 6 questions about the daytime behavior of children; the behaviors in question are thought to be indicators of poor sleep habits.  PSQ2 displayed a very strongly skewed distribution (previous panel); therefore, we dichotomized PSQ2; PSQ2 = 0 vs. PSQ2 > 0 (absence vs. presence of behavior problems)  There is a strong relationship between reported problems on the PSQ2 and whether they reported feeling “Rested” or “Still Tired” upon waking.  This was a pilot study which helped find several problems that we will be better prepared for in the large-scale intervention study. One of the major problems was poor data collection and sampling; the next study will have better methodology to minimize these problems.  In our next study we will add a measure of physical fitness to complement Body Mass Index as another way to measure obesity.  Univariate Logistic Regression was used to investigate the effectiveness of our variables in predicting a BMI of over/under 30.  These results were consistent with our linear models; the same variables that showed strong linear relationships were successful in predicting over/under 30.  Multivariate logistic models found similar results; results not shown.  We also attempted to model the predicted BMI percentile for age and gender with our data. Unfortunately, this model failed; all of the subjects fell between the 97 th and the 99 th percentile (or above the 99 th, for which the value cannot be calculated), making meaningful results impossible. Body Mass Index  Body Mass Index is a number calculated from a person’s height and weight. It provides a reasonable measure of obesity for most people.  The formula for BMI is: Weight (kg) / (Height (m)) 2  In adults, a BMI of 18 or less is considered underweight; is considered normal; is considered overweight; and 30 or above is considered obese. These cutoffs are not the same for children, for reasons explained below.  BMI has several flaws as a measure of obesity for children.  It only counts height and weight; there is no way to account for body composition, waist circumference, or physical fitness.  Children grow at varying rates due to the different ages at which they reach puberty. A BMI of a perfectly normal 10-year-old may be the same as the BMI of an obese 5-year-old, making it very difficult to categorize children based on BMI.  Rather than use adult categories or inaccurate percentiles, we have chosen to use BMI as a linear response variable (and account for Age and Gender in the model ).  Older children tend to have lower values of Sleep Duration.  Older children also tend to have a higher BMI, in general.  Thus, include interaction of Age & Sleep Duration when predicting BMI. YesNo Naps On Weekends14 (23.3%)46 (76.7%) Sleep In School12 (20.0%)48 (80.0%) Share Room35 (58.3%)25 (41.7%) RestedStill Tired Feel Upon Waking29 (55.8%)23 (44.2%) **Note: There is a substantial amount of missing data; 17 of the subjects did not fill out many of the necessary sleep questionnaires. Only 51 of the 77 subjects are included in the multivariate analysis. Feel Upon Waking = “Still Tired”Feel Upon Waking = “Rested” PSQ 2 = 0215 PSQ 2 >  21/23 = 91.3% of subjects that report feeling “still tired” when they wake up also report problems on PSQ 2.  13/28 = 46.4% of subjects that report feeling “rested” when they wake up also report problems on PSQ 2.  Large difference suggests that we should account for this interaction.  This effect is also dependent on gender; males that report problems on PSQ 2 seem to have far greater increases in BMI than females.. VariableCoefficient90% Confidence IntervalP-Value Age2.451(0.156, 4.748) Gender : Male-2.061(-4.770, 0.648) PSQ (-0.725, 0.403) PSQ2 (Categorical)0.443(-5.084, 5.970) PDSS0.301(0.039, 0.563) Sleep Duration1.822(-0.905, 4.550) Bed Time Difference0.972(-0.142, 2.085) Feel Upon Waking : Rested-2.165(-8.361, 4.031) Naps (Weekends)4.278(0.893, 7.663) Sleep in School2.946(0.060, 5.830) Share Room-4.622(-7.180, ) Age*Sleep Duration-0.228(-0.481, 0.025) PSQ2(cat)*Feel Upon Waking7.809(1.282, ) **Coefficients represent the expected increase in predicted BMI Sleep Questionnaires:  PSQ 1 – Sleep Quality  PSQ 2 – Behavior Problems  PDSS – Daytime Sleepiness **Note: For each of these questionnaires, a higher score indicates more problems Variables correlated with decreases in BMI Variables correlated with increases in BMI Main Effect Variables Gender : Male PSQ 1 Share Room PDSS Bed Time Difference Naps (Weekends) Sleep In School Variables with Interactions Feel Upon Waking : Rested Age*Sleep Duration PSQ 2 (categorical) PSQ2(cat)*FeelUponWaking Age Sleep Duration Kenneth Nugent, MD, TTUHSC, Department of Internal Medicine Rishi Raj, MD, TTUHSC, Department of Internal Medicine Rita Corona, TTUHSC, Pediatrics Center for Disease Control website: PSQ 2 ProblemFeel Upon WakingProjected Increase in Expected BMI No (0)Still Tired (0)0 No (0)Rested (1)-2.47 Yes (1)Still Tired (0)0.08 Yes (1)Rested (1)5.25