As obesity risk increases in adults and youth in the U.S., the risks for multiple types of disease also increase, including at least 6 types of cancer. Smart growth communities represent 1 of 5 major building trends in the U.S., today, and the one most associated with obesity prevention potential. However, smart growth communities have not been systematically evaluated in research, either as the planned set of 10 principles or in terms of the combination of individual factors that contribute most to obesity risk prevention.
The aim of this translational research study is to evaluate the longitudinal, temporal relationships of an entire built environment, the smart growth community, on family obesity risk, and to evaluate changes from this environment that mediate this risk. The research design is a 3 group hybrid design, involving aspects of both matching and a randomized trial. The sample consists of 600 families (1 index parent, 1 index child between age 10-14, 36% white, 44% Hispanic, 5% African-American, 11% Asian, 4% other). The intervention group is new resident families who are randomly drawn to live in a low-moderate income smart growth community in California (n=300). The randomly selected control is families matched in demographic and income characteristics to the intervention group who are interested in living in a smart growth community, but are not drawn in the random lottery (n=150). The matched control is 150 families who are matched on demographics and live adjacent to the smart growth community. The sample is stratified, with 50% moderate income single family home owners, 25% low- moderate income renters of multi-family residences, and 25% very low-low income renters. Measurement is longitudinal, with 4 waves of anthropomorphic, accelerometry, communication network, and self-report data on physical activity, obesity risk (BMI), and food intake;GIS mapped environmental indices that reflect measurable built environment characteristics of smart growth;and archival data on regional planning, traffic, and density. Analyses will evaluate the effects of all and individual smart growth principles on obesity risk, including multi-level modeling controlling for contextual effects of neighborhood and family variables, growth and growth mixture modeling to evaluate trajectories of physical activity changes, and structural equation modeling to test theoretical models of relationships. The findings are expected to have significant implications for the inclusion of health and obesity prevention factors in community planning. Results are expected to generalize across rural, suburban, and urban communities in the U.S.
Smart growth is one of five major community planning trends sweeping the U.S. today. The CDC estimates that 1/3 of all existing and new communities will use smart growth principles in their planning within 5-7 years. The 10 principles constituting smart growth have broad application to urban, suburban, and rural areas, as well as to unused infill and green field spaces. However, while a few of the 10 individual principles, e.g., green space allotment for walking, have been shown to relate cross-sectionally to increased physical activity, there has been no study of smart growth in its'entirety as a intervention for preventing obesity and improving health, no comparison of the relative impact of its'individual principles, and no longitudinal or randomized study of its'effects. The proposed study is innovative in that it involves the partnership of a transdisciplinary team of researchers, regional planners, a county public health department, and a major health coalition in evaluating the effects of smart growth on changing physical activity, eating behavior, BMI, and overall health practices in a randomized trial, the first of its'type. The findings are expected to have a significant impact on future community planning related to obesity prevention and health promotion, and will be disseminated through county public health and regional planning departments.
|Pickering, Trevor A; Huh, Jimi; Intille, Stephen et al. (2016) Physical Activity and Variation in Momentary Behavioral Cognitions: An Ecological Momentary Assessment Study. J Phys Act Health 13:344-51|
|Dunton, Genevieve Fridlund; Liao, Yue; Intille, Stephen et al. (2015) Momentary assessment of contextual influences on affective response during physical activity. Health Psychol 34:1145-53|
|Liao, Yue; Intille, Stephen S; Dunton, Genevieve F (2015) Using ecological momentary assessment to understand where and with whom adults' physical and sedentary activity occur. Int J Behav Med 22:51-61|
|O'Reilly, Gillian A; Huh, Jimi; Schembre, Susan M et al. (2015) Association of usual self-reported dietary intake with ecological momentary measures of affective and physical feeling states in children. Appetite 92:314-21|
|Dunton, Genevieve Fridlund; Almanza, Estela; Jerrett, Michael et al. (2014) Neighborhood park use by children: use of accelerometry and global positioning systems. Am J Prev Med 46:136-42|
|Liao, Yue; Intille, Stephen; Wolch, Jennifer et al. (2014) Understanding the physical and social contexts of children's nonschool sedentary behavior: an ecological momentary assessment study. J Phys Act Health 11:588-95|
|Jerrett, Michael; McConnell, Rob; Wolch, Jennifer et al. (2014) Traffic-related air pollution and obesity formation in children: a longitudinal, multilevel analysis. Environ Health 13:49|
|Dunton, Genevieve F; Huh, Jimi; Leventhal, Adam M et al. (2014) Momentary assessment of affect, physical feeling states, and physical activity in children. Health Psychol 33:255-63|
|Su, Jason G; Jerrett, Michael; McConnell, Rob et al. (2013) Factors influencing whether children walk to school. Health Place 22:153-61|
|Jerrett, Michael; Almanza, Estela; Davies, Molly et al. (2013) Smart growth community design and physical activity in children. Am J Prev Med 45:386-92|
Showing the most recent 10 out of 24 publications