240129 Does physical activity spatially cluster? Preliminary findings from an analysis of older women living in three states

Tuesday, November 1, 2011

Kosuke Tamura, MS, MA , Department of Health and Kinesiology, Purdue University, West Lafayette, IN
Robin Puett, PhD , Department of Environmental Health Sciences, Arnold School of Public Health, University of South Carolina, Columbia, SC
Heather Whitcomb, MA , Department of Health and Kinesiology, Purdue University, West Lafayette, IN
Jaime Hart, ScD , Channing Laboratory, Brigham and Women's Hospital and Department of Epidemiology, Harvard School of Public Health, Harvard University, Boston, MA
Francine Laden, ScD , Channing Laboratory, Department of Environmental Health, Brigham and Women's Hospital and Harvard Medical School, Harvard University, Boston, MA
Philip J. Troped, PhD, MS , Department of Health and Kinesiology, Purdue University, West Lafayette, IN
Background: Among public health practitioners and researchers, there has been a growing recognition of the need for environmental and policy approaches to effectively promote physical activity. Evidence of relationships between the built environment and physical activity has accumulated in recent years. However, little is known about how physical activity may be spatially clustered, and whether the built environment can explain these clusters. The purpose of this study was to identify spatial clustering of physical activity.

Methods: A sample of 23,449 Nurses' Health Study participants (mean age = 70.3 6.9 years) from California, Massachusetts, and Pennsylvania completed survey items on physical activity in 2004. A binary outcome was created for meeting physical activity recommendations via walking (≥ 500 MET-min/week). A spatial scan statistic was used to test for spatial clustering (i.e., areas with high or low prevalence at county level) of meeting recommendations in unadjusted and age-adjusted models.

Results: In California, two spatial clusters of counties were identified for meeting physical activity recommendations (p<.01), adjusting for age. In one cluster participants had a 52% greater likelihood of meeting physical activity recommendations and in the other cluster, a 59% lower likelihood. In Massachusetts, one spatial cluster was identified (p<.04), indicating participants within the cluster had a 22% lower likelihood of meeting recommendations.

Conclusion: Preliminary findings suggest that physical activity is spatially clustered among this sample of older women. Upcoming analyses will determine whether certain characteristics of the built environment can explain spatial clustering of physical activity.

Learning Areas:
Epidemiology
Public health or related research
Social and behavioral sciences

Learning Objectives:
1. Define the concept of spatial clustering. 2. Describe how spatial statistics can be applied to the analysis of geospatial patterns of physical activity. 3. Explain the meaning of a spatial cluster of physical activity.

Keywords: Physical Activity, Epidemiology

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am a doctoral student specializing in spatial epidemiology, physical activity, and the built environment. I conducted the analysis for the study presented in this abstract.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.