Percentage of adults age 20 and over reporting no leisure-time physical activity.
The 2021 County Health Rankings used data from 2017 for this measure.
Reason for Ranking
Decreased physical activity has been related to several disease conditions such as type 2 diabetes, cancer, stroke, hypertension, cardiovascular disease, and premature mortality, independent of obesity. Inactivity causes 11% of premature mortality in the United States, and caused more than 5.3 million of the 57 million deaths that occurred worldwide in 2008. In addition, physical inactivity at the county level is related to health care expenditures for circulatory system diseases. Physical activity improves sleep, cognitive ability, and bone and musculoskeletal health, as well as reduces risks of dementia. Physical inactivity is not only associated with individual behavior but also community conditions such as expenditures on recreational activities, access to infrastructure, and poverty.
Key Measure Methods
Physical Inactivity is a Percentage
Physical Inactivity is based on responses to the Behavioral Risk Factor Surveillance Survey and is the percentage of adults ages 20 and over reporting no leisure-time physical activity in the past month.
The Method for Calculating Physical Inactivity Changed
Data for Physical Inactivity are provided by the CDC Interactive Diabetes Atlas which uses BRFSS data to provide county-level estimates. Beginning with the 2015 County Health Rankings, Physical Inactivity estimates include both landline and cell phone users. Previously, only landline users were included in the data. This change was implemented in order to provide users with the most accurate estimates of health in their community as possible.
Physical Inactivity is Created Using Statistical Modeling
Our physical inactivity estimates are produced from three years of survey data and created using a complex statistical model. Modeling generates more stable estimates for places with small numbers of residents or survey responses. There are also drawbacks to using modeled data. The smaller the population or sample size of a county, the more the estimates are derived from the model itself and the less they are based on survey responses. Models make statistical assumptions about relationships that may not hold in all cases. Finally, there is no perfect model and each model generally has limitations specific to their methods. For more technical information on BRFSS modeling in the Diabetes Atlas, please see their methodology.
Caution Should be Used When Comparing these Estimates Across State Lines
The model used to create these estimates includes a state-level factor that limits comparability between neighboring counties of adjacent states.
The data for this measure are from self-reported measures. Some individuals may report activities as physically active while others may not; e.g., household cleaning. The data only refer to time spent inactive during leisure time and do not account for inactivity while at work. Additionally, the degree of intensity of exercise and duration are unknown for individuals who report leisure-time physical activity.
The numerator is the number of BRFSS Respondents who answered "no" to the question, "During the past month, other than your regular job, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise?"
The denominator is the number of BRFSS respondents age 20 and older.
Can This Measure Be Used to Track Progress
This measure could be used to track progress, but only after considering its substantial limitations. Methodological changes in the Behavioral Risk Factor Surveillance System, which are discussed above and were implemented in the 2015 Rankings, make comparisons with estimates prior to that release year difficult. In addition, current estimates are produced using sophisticated modeling techniques which make them difficult to use for tracking progress in small geographic areas.
Modeled estimates have specific drawbacks with regard to their usefulness in tracking progress in communities. Modeled data are not particularly good at incorporating the effects of local conditions, such as health promotion policies or unique population characteristics, into their estimates. Counties trying to measure the effects of programs and policies on the data should use great caution when using modeled estimates. In order to better understand and validate modeled estimates, confirming this data with additional sources of data at the local level is particularly valuable.
Years of Data Used
United States Diabetes Surveillance System
The US Diabetes Surveillance System is an interactive web application that allows the user to view diabetes surveillance data and trends at national, state, and county levels. Data from CDC's Behavioral Risk Factor Surveillance System (BRFSS) and from the US Census Bureau's Population Estimates Program were used to obtain county-level estimates of diagnosed diabetes, newly diagnosed diabetes, obesity, and physical inactivity.
There are several methods to try to get more specific data than the county level. For larger counties, you can access county- or MSA-specific data from the CDC at http://www.cdc.gov/brfss/smart/smart_data.htm. However, using this data requires somewhat advanced analytic capabilities.
The 500 Cities Project also provides city- and census tract-level small area estimates for chronic disease risk factors, health outcomes, and clinical preventive service use, including physical inactivity, for the largest 500 cities in the United States.
In many states, you can access county-level BRFSS estimates, and in some cases, you can stratify those estimates by age, gender or race. You can find BRFSS resources for most states in our Finding More Data section.
 Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT, for the Lancet Physical Activity Series Working Group, Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy, The Lancet. 2012; 380.9838:219-229.
 Rosenberger RS, Sneh Y, Phipps TT, Gurvitch R. A spatial analysis of linkages between health care expenditures, physical inactivity, obesity and recreation supply. Journal of Leisure Research. 2005; 37.2:216-235.
 Piercy, K. L., Troiano, R. P., Ballard, R. M., Carlson, S. A., Fulton, J. E., Galuska, D. A., ... & Olson, R. D. (2018). The physical activity guidelines for Americans. JAMA, 320(19), 2020-2028.
 Lee KH, Dvorak RG, Schuett MA, Van Riper CJ. Understanding spatial variation of physical inactivity across the continental United States. Landscape and Urban Planning (2017) 165:61-71.
See how this component fits into our model
When it comes to developing and implementing solutions to problems that affect communities, evidence matters. The strategies below give some ideas of ways communities can harness evidence to make a difference locally. You can learn more about these and other strategies in What Works for Health, which summarizes and rates evidence for policies, programs, and systems changes.