Modeling the Relationship Between Sleep and Pediatric Obesity Andrew Althouse Carnegie Mellon University, Department of Statistics Southern Society of Clinical Investigation Meetings Adolescent Medicine and Pediatrics Friday, February 23, 2008

Speaker: Andrew Althouse Andrew Althouse has documented that he has nothing to disclose. DISCLOSURE STATEMENT

Rising Prevalence of Obesity  An NHANES survey conducted in 1980 found 15.0% of adults to be obese.  By 2004, that percentage had increased to 32.9%.  NHANES surveys found that obesity is also becoming more prevalent in children.  Two age groups were studied; each group had a marked increase in the percentage of children that were obese.

Challenges of Evaluating Pediatric Obesity  Definitions of “Obesity”  Adults: having a Body Mass Index greater than 30 kg/m 2.  Children: more difficult because of growth curve; cannot choose one number as a “cut-off” for obesity  One method defines a child as “obese” if their BMI is above the 95 th percentile for their age and gender.  95 th percentile today is higher than the 95 th percentile in  This standard would always suggest that 5% of children were obese; but there are more children with weight problems today than in

Sleep and Obesity: A Connection?  Adult Obesity may be connected to poor sleep habits.  Short Sleep --> Increased BMI Buscemi, Kumar, Nugent, et al. JCSM 2007; 3, 7, Gangswich, et al. Sleep 2005; 28: Singh, et al. JCSM 2005; 1:  Current research modeling this relationship in children Nixon, et al. Sleep 2008; 31(1); Hasler, et al. Sleep 2004; 27(4): Locard, et al. Int J Obes Relat Metab Disord 1992; 16(10):  Obese children may be more likely to become obese adults  If we can decrease the prevalence of obesity in children we may be able to decrease the prevalence of obesity in adults Taheri, S Arch Dis Child 2006;91:

Study Design  Convenience sample of 77 subjects  Pediatrician referrals to a dietitian at Texas Tech University Health Sciences Center (Lubbock, TX)  Data collected from January 2006 until March  Subjects completed standard sleep questionnaires  Pediatric Sleep Questionnaire 1: Sleep Habits  Pediatric Sleep Questionnaire 2: Behavioral Problems  Pediatric Daytime Sleepiness Scale  Supplemental questions about daily habits with respect to: sleep routine physical activity use of electronic media We chose to focus primarily on the variables related to sleep duration, quality of sleep, and consistency of sleep.

Subject Characteristics Age: Mean = years, SD = 3.42 Increasing trend in BMI with age Gender: 61% Females, 39% Males Females: higher median, skewed dist. Males: median approx. 30

Variables of Interest  Our response variable in all models was Body Mass Index (kg/m 2 ).  Predictors that we considered:  Sleep Duration: recorded in hours (to the nearest quarter-hour)  PSQ 1: high score indicates sleep problems  PSQ 2: high score indicates behavioral problems  PDSS: high score indicates child is tired during the day  Naps: Yes or No  Sleep in School: Yes or No  Share Room: Yes or No  Feel Upon Waking: Rested or Still Tired  Sleep Time Difference: difference between weekday bed time and weekend bed time (recorded in hours)  We did not include the variables regarding physical activity or electronic media use due to sparse data.

Sleep Duration Mean = 9.08 hours SD = 1.09 Negative correlation w/BMI:  Less Sleep = Higher BMI Negative correlation w/Age:  Less Sleep = Higher Age

Adjusted Statistical Modeling VariableCoefficient90% Confidence IntervalP-Value Age 2.451(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) PDSS 0.301(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 4.278(0.893, 7.663) Sleep in School2.946(0.060, 5.830) Share Room (-7.180, ) Age*Sleep Duration-0.228(-0.481, 0.025) PSQ2(cat)*Feel Upon Waking 7.809(1.282, )

The Age Cutoff  Interaction between age and sleep duration creates a “cutoff” at age 8 where the effect of the variable sleep duration changes.  This equation summarizes the effects of Age and Sleep: 2.451*(Age) *(Sleep) – 0.228*(Age*Sleep) AgePredicted BMI: 8 Hrs Sleep Predicted BMI: 9 Hrs Sleep Effect of 1-Hour Sleep Increase on Predicted BMI  Note the change in direction of the effect.  Increased magnitude as children get farther from age 8.

Behavioral Problems & Their Implications  Strong interaction between the presence of behavioral problems (determined by PSQ 2) and “Feel Upon Waking.”  No behavioral problems: “rested” children had a lower expected BMI than “still tired” children.  With behavioral problems: “rested” children had a higher expected BMI than “still tired” children. PSQ 2 ProblemFeel Upon WakingPredicted Change in Expected BMI No (0)Rested (1)-2.47 No (0)Still Tired (0)0 Yes (1)Still Tired (0)0.08 Yes (1)Rested (1)5.25

Summary of Findings  Protective Effects:  Sharing a Room  Male  Increased Sleep (if over age 8)  Increased Risk:  Taking Naps  Inconsistent Sleep Patterns  Feeling Rested (with Behavioral Problems)

Future Work  Current study limitations  Sparse data: physical activity and electronic media use  Difficulty understanding supplemental questions  Ongoing:  Redesign of questionnaires; pre-testing  Analysis of parent-child reliability issues  Manuscript in progress  Designing longitudinal study with sleep- intervention arm

Acknowledgements  NSF VIGRE (grant #: DMS )  Dr. Rebecca Nugent, Carnegie Mellon University, Statistics  Dr. Kenneth Nugent, TTUHSC Internal Medicine  Dr. Rishi Raj, TTUHSC Internal Medicine  Dr. Rita Corona, TTUHSC Internal Medicine  Dr. Yasir Yaqub, TTUHSC Internal Medicine  Dr. WM Hall, TTUHSC Pediatrics