BACKGROUND: Identifying neighborhood environment attributes related to childhood obesity can inform environmental changes for obesity prevention. PURPOSE: To evaluate child and parent weight status across neighborhoods in King County (Seattle metropolitan area) and San Diego County differing in GIS-defined physical activity environment (PAE) and nutrition environment (NE) characteristics. METHODS: Neighborhoods were selected to represent high (favorable) versus low (unfavorable) on the two measures, forming four neighborhood types (low on both measures, low PAE/high NE, high PAE/low NE, and high on both measures). Weight and height of children aged 6-11 years and one parent (n=730) from selected neighborhoods were assessed in 2007-2009. Differences in child and parent overweight and obesity by neighborhood type were examined, adjusting for neighborhood-, family-, and individual-level demographics. RESULTS: Children from neighborhoods high on both environment measures were less likely to be obese (7.7% vs 15.9%, OR=0.44, p=0.02) and marginally less likely to be overweight (23.7% vs 31.7%, OR=0.67, p=0.08) than children from neighborhoods low on both measures. In models adjusted for parent weight status and demographic factors, neighborhood environment type remained related to child obesity (high vs low on both measures, OR=0.41, p<0.03). Parents in neighborhoods high on both measures (versus low on both) were marginally less likely to be obese (20.1% vs 27.7%, OR=0.66, p=0.08), although parent overweight did not differ by neighborhood environment. The lower odds of parent obesity in neighborhoods with environments supportive of physical activity and healthy eating remained in models adjusted for demographics (high vs low on the environment measures, OR=0.57, p=0.053). CONCLUSIONS: Findings support the proposed GIS-based definitions of obesogenic neighborhoods for children and parents that consider both physical activity and nutrition environment features.
BACKGROUND: Identifying neighborhood environment attributes related to childhood obesity can inform environmental changes for obesity prevention. PURPOSE: To evaluate child and parent weight status across neighborhoods in King County (Seattle metropolitan area) and San Diego County differing in GIS-defined physical activity environment (PAE) and nutrition environment (NE) characteristics. METHODS: Neighborhoods were selected to represent high (favorable) versus low (unfavorable) on the two measures, forming four neighborhood types (low on both measures, low PAE/high NE, high PAE/low NE, and high on both measures). Weight and height of children aged 6-11 years and one parent (n=730) from selected neighborhoods were assessed in 2007-2009. Differences in child and parent overweight and obesity by neighborhood type were examined, adjusting for neighborhood-, family-, and individual-level demographics. RESULTS:Children from neighborhoods high on both environment measures were less likely to be obese (7.7% vs 15.9%, OR=0.44, p=0.02) and marginally less likely to be overweight (23.7% vs 31.7%, OR=0.67, p=0.08) than children from neighborhoods low on both measures. In models adjusted for parent weight status and demographic factors, neighborhood environment type remained related to childobesity (high vs low on both measures, OR=0.41, p<0.03). Parents in neighborhoods high on both measures (versus low on both) were marginally less likely to be obese (20.1% vs 27.7%, OR=0.66, p=0.08), although parent overweight did not differ by neighborhood environment. The lower odds of parent obesity in neighborhoods with environments supportive of physical activity and healthy eating remained in models adjusted for demographics (high vs low on the environment measures, OR=0.57, p=0.053). CONCLUSIONS: Findings support the proposed GIS-based definitions of obesogenic neighborhoods for children and parents that consider both physical activity and nutrition environment features.
Authors: Mia A Papas; Anthony J Alberg; Reid Ewing; Kathy J Helzlsouer; Tiffany L Gary; Ann C Klassen Journal: Epidemiol Rev Date: 2007-05-28 Impact factor: 6.222
Authors: R J Kuczmarski; C L Ogden; L M Grummer-Strawn; K M Flegal; S S Guo; R Wei; Z Mei; L R Curtin; A F Roche; C L Johnson Journal: Adv Data Date: 2000-06-08
Authors: Cindy W Leung; Steven E Gregorich; Barbara A Laraia; Lawrence H Kushi; Irene H Yen Journal: Int J Behav Nutr Phys Act Date: 2010-06-01 Impact factor: 6.457
Authors: Amy Jennings; Ailsa Welch; Andy P Jones; Flo Harrison; Graham Bentham; Esther M F van Sluijs; Simon J Griffin; Aedín Cassidy Journal: Am J Prev Med Date: 2011-04 Impact factor: 5.043
Authors: Lindsay T Hoyt; Lawrence H Kushi; Cindy W Leung; Dana C Nickleach; Nancy Adler; Barbara A Laraia; Robert A Hiatt; Irene H Yen Journal: Pediatrics Date: 2014-10-13 Impact factor: 7.124
Authors: Brian E Saelens; Karen Glanz; Lawrence D Frank; Sarah C Couch; Chuan Zhou; Trina Colburn; James F Sallis Journal: Obesity (Silver Spring) Date: 2018-08 Impact factor: 5.002
Authors: Jordan A Carlson; Brian E Saelens; Jacqueline Kerr; Jasper Schipperijn; Terry L Conway; Lawrence D Frank; Jim E Chapman; Karen Glanz; Kelli L Cain; James F Sallis Journal: Health Place Date: 2015-01-09 Impact factor: 4.078
Authors: Shannon M Robson; Sarah C Couch; James L Peugh; Karen Glanz; Chuan Zhou; James F Sallis; Brian E Saelens Journal: J Acad Nutr Diet Date: 2016-04-01 Impact factor: 4.910
Authors: Stephanie T Broyles; Candice A Myers; Kathryn T Drazba; Arwen M Marker; Timothy S Church; Robert L Newton Journal: J Urban Health Date: 2016-04 Impact factor: 3.671