BEAT Courses Training Resources

Listed below are the recorded talks presented along with relevant citations and materials from the Online BEAT Training Course. 

Citations and Resources related to the Introduction Module


IOM Bridging the Evidence gap in obesity prevention

More information coming soon

Strategies to prevent childhood obesity, RWJF

More information coming soon

Brownson, R., Hoehner, C., Day, K., Forsyth, A., Sallis, J. (2009). Measuring the built environment for physical activity: state of the science. American Journal of Preventive Medicine 36(4S), S99-S123.


Physical inactivity is one of the most important public health issues in the U.S. and internationally. Increasingly, links are being identified between various elements of the physical—or built—environment and physical activity. To understand the impact of the built environment on physical activity, the development of high-quality measures is essential. Three categories of built environment data are being used: (1) perceived measures obtained by telephone interview or self-administered questionnaires; (2) observational measures obtained using systematic observational methods (audits); and (3) archival data sets that are often layered and analyzed with GIS. This review provides a critical assessment of these three types of built-environment measures relevant to the study of physical activity. Among perceived measures, 19 questionnaires were reviewed, ranging in length from 7 to 68 questions. Twenty audit tools were reviewed that cover community environments (i.e., neighborhoods, cities), parks, and trails. For GIS-derived measures, more than 50 studies were reviewed. A large degree of variability was found in the operationalization of common GIS measures, which include population density, land-use mix, access to recreational facilities, and street pattern. This first comprehensive examination of built-environment measures demonstrates considerable progress over the past decade, showing diverse environmental variables available that use multiple modes of assessment. Most can be considered first-generation measures, so further development is needed. In particular, further research is needed to improve the technical quality of measures, understand the relevance to various population groups, and understand the utility of measures for science and public health.

(Am J Prev Med 2009; 36(4S):S99–S123) © 2009 American Journal of Preventive Medicine

Carlson, S., Densmore, D., Fulton, J., Yore, M., Kohl, H. (2009). Differences in physical activity prevalence and trends from 3 U.S. surveillance systems: NHIS, NHANES and BRFSS. Journal of Physical Activity and Health 6(Suppl 1), S18-S27.


Three U.S. surveillance systems-National Health Interview Survey (NHIS), National Health and Nutrition Examination Survey (NHANES), and Behavioral Risk Factor Surveillance System (BRFSS)--estimate physical activity prevalence. METHODS: Survey differences were examined qualitatively. Prevalence estimates by sex, age, and race/ethnicity were assessed for comparable survey periods. Trends were examined from NHIS 1998 to 2007, NHANES 1999 to 2006, and BRFSS 2001 to 2007. RESULTS: Age-adjusted prevalence estimates appeared most similar for NHIS 2005 (physically active: 30.2%, inactive: 40.7%) and NHANES 2005 to 2006 (physically active: 33.5%, inactive: 32.4%). In BRFSS 2005, prevalence of being physically active was 48.3% and inactive was 13.9%. Across all systems, men were more likely to be active than women; non-Hispanic whites were most likely to be active; as age increased, overall prevalence of being active decreased. Prevalence of being active exhibited a significant increasing trend only in BRFSS 2001 to 2007 (P < .001), while prevalence of being inactive decreased significantly in NHANES 1999 to 2006 (P < .001) and BRFSS 2001 to 2007 (P < .001). CONCLUSIONS: Different ways of assessing physical activity in surveillance systems result in different prevalence estimates. Before comparing estimates from different systems, all aspects of data collection and data analysis should be examined to determine if comparisons are appropriate.

Dill, J. (2009). Bicycling for transportation and health: the role of infrastructure. Journal of Public Health Policy 30, S95-S110.


This paper aims to provide insight on whether bicycling for everyday travel can help US adults meet the recommended levels of physical activity and what role public  infrastructure may play in encouraging this activity. The study collected data on bicycling behavior from 166 regular cyclists in the Portland, Oregon metropolitan area using global positioning system (GPS) devices. Sixty percent of the cyclists rode for more than 150 minutes per week during the study and nearly all of the bicycling was for utilitarian purposes, not exercise. A disproportionate share of the bicycling occurred on streets with bicycle lanes, separate paths, or bicycle boulevards. The data support the need for well-connected neighborhood streets and a network of bicycle-specific infrastructure to encourage more bicycling among adults. This can be accomplished through comprehensive planning, regulation, and funding.

Saelens, B. & Handy, S. (2008). Built environment correlates of walking: a review. Medicine & Science In Sports & Exercise, 40(7), S550-S556.


The past decade has seen a dramatic increase in empirical investigation into the relations between built environment and physical activity. To create places that facilitate and encourage walking, practitioners need an understanding of the specific characteristics of the built environment that correlate most strongly with walking. This article reviews evidence on the built environment correlates with walking.


Included in this review were 13 reviews published between 2002 and 2006 and 29 original studies published in 2005 and up through May 2006. Results were summarized based on specific characteristics of the built environment and transportation walking versus recreational walking.


Previous reviews and newer studies document consistent positive relations between walking for transportation and density, distance to nonresidential destinations, and land use mix; findings for route/network connectivity, parks and open space, and personal safety are more equivocal. Results regarding recreational walking were less clear.


More recent evidence supports the conclusions of prior reviews, and new studies address some of the limitations of earlier studies. Although prospective studies are needed, evidence on correlates appears sufficient to support policy changes.