Robin P Shook1, Kelsee Halpin2, Jordan A Carlson3, Ann Davis4, Kelsey Dean3, Amy Papa3, Ashley K Sherman5, Janelle R Noel-MacDonnell5, Shelly Summar3, Gary Krueger6, Deborah Markenson3, Sarah Hampl3. 1. Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO; Center for Children's Healthy Lifestyles and Nutrition, Kansas City, MO. Electronic address: rpshook@cmh.edu. 2. Division of Pediatric Endocrinology and Diabetes, Children's Mercy Kansas City, Kansas City, MO. 3. Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO; Center for Children's Healthy Lifestyles and Nutrition, Kansas City, MO. 4. Center for Children's Healthy Lifestyles and Nutrition, Kansas City, MO; Department of Pediatrics, University of Kansas Medical Center, Kansas City, KS. 5. Department of Health Services and Outcomes Research, Children's Mercy Kansas City, Kansas City, MO. 6. Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO.
Abstract
OBJECTIVE: To evaluate the utility of a routine assessment of lifestyle behaviors incorporated into the electronic health record (EHR) to quantify lifestyle practices and obesity risk at a pediatric primary care center. PATIENTS AND METHODS: Participants included 24,255 patients aged 2 to 18 years whose parent/caregiver completed a self-report lifestyle assessment during a well-child examination (January 1, 2013, through June 30, 2016). Cross-sectional analyses of age, race/ethnicity, body mass index, and lifestyle assessment responses were performed. Outcome measures included prevalence of patients meeting consensus recommendations for physical activity; screen time; and dairy, water, and fruit/vegetable consumption and the odds of obesity based on reported lifestyle behaviors. RESULTS: Prevalence of meeting recommendations for lifestyle behaviors was highest for physical activity (84%), followed by screen time (61%) and consumption of water (51%), dairy (27%), and fruits/vegetables (10%). Insufficient physical activity was the strongest predictor of obesity (odds ratio [OR], 1.65; 95% CI, 1.51-1.79), followed by excess screen time (OR, 1.36; 95% CI, 1.27-1.45). Disparities existed across ages, races/ethnicities, and sexes for multiple lifestyle habits. Youth who met 0 or 1 lifestyle recommendation were 1.45 to 1.71 times more likely to have obesity than those meeting all 5 recommendations. CONCLUSION: Healthy behaviors vary in prevalence, as does their association with obesity. This variation is partially explained by age, sex, and race/ethnicity. Meeting national recommendations for specific behaviors is negatively associated with obesity in a dose-dependent manner. These findings support the assessment of lifestyle behaviors in primary care as one component of multilevel initiatives to prevent childhood obesity.
OBJECTIVE: To evaluate the utility of a routine assessment of lifestyle behaviors incorporated into the electronic health record (EHR) to quantify lifestyle practices and obesity risk at a pediatric primary care center. PATIENTS AND METHODS: Participants included 24,255 patients aged 2 to 18 years whose parent/caregiver completed a self-report lifestyle assessment during a well-child examination (January 1, 2013, through June 30, 2016). Cross-sectional analyses of age, race/ethnicity, body mass index, and lifestyle assessment responses were performed. Outcome measures included prevalence of patients meeting consensus recommendations for physical activity; screen time; and dairy, water, and fruit/vegetable consumption and the odds of obesity based on reported lifestyle behaviors. RESULTS: Prevalence of meeting recommendations for lifestyle behaviors was highest for physical activity (84%), followed by screen time (61%) and consumption of water (51%), dairy (27%), and fruits/vegetables (10%). Insufficient physical activity was the strongest predictor of obesity (odds ratio [OR], 1.65; 95% CI, 1.51-1.79), followed by excess screen time (OR, 1.36; 95% CI, 1.27-1.45). Disparities existed across ages, races/ethnicities, and sexes for multiple lifestyle habits. Youth who met 0 or 1 lifestyle recommendation were 1.45 to 1.71 times more likely to have obesity than those meeting all 5 recommendations. CONCLUSION: Healthy behaviors vary in prevalence, as does their association with obesity. This variation is partially explained by age, sex, and race/ethnicity. Meeting national recommendations for specific behaviors is negatively associated with obesity in a dose-dependent manner. These findings support the assessment of lifestyle behaviors in primary care as one component of multilevel initiatives to prevent childhood obesity.