Physical Inactivity

Percentage of adults age 18 and over reporting no leisure-time physical activity (age-adjusted).

The 2022 County Health Rankings used data from 2019 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.[1] In addition, physical inactivity at the county level is related to health care expenditures for circulatory system diseases.[2] Physical activity improves sleep, cognitive ability, and bone and musculoskeletal health, as well as reduces risks of dementia.[3] Physical inactivity is not only associated with individual behavior but also community conditions such as expenditures on recreational activities, access to infrastructure, and poverty.[4]

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 18 and over reporting no leisure-time physical activity in the past month.

Physical Inactivity estimates are age-adjusted

Age is a non-modifiable risk factor, and as age increases, poor health outcomes are more likely. We report an age-adjusted rate in order to fairly compare counties with differing age structures.

The method for calculating Physical Inactivity has changed

In the 2022 County Health Rankings, the source for this measure switched from the United States Diabetes Surveillance System to the Behavioral Risk Factor Surveillance System. 

Physical Inactivity is created using statistical modeling

Statistical modeling is used to obtain more informed and reliable estimates than survey data alone can provide. Modeling generates more stable estimates for places with small numbers of residents or survey responses. The Physical Inactivity estimates are produced from one year of survey data and are created using complex statistical modeling. For more technical information on PLACES modeling using BRFSS data, please see their methodology.[5]

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 assumptions about statistical relationships that may not hold in all cases. Finally, there is no perfect model and each model generally has limitations specific to their methods.


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 18 and older.

Can This Measure Be Used to Track Progress

First, it is important to note the change in data source in the 2022 Rankings from United States Diabetes Surveillance System to Behavioral Risk Factor Surveillance System (BRFSS). This measure could be used to track progress, but only after considering its substantial limitations. Methodological changes in the BRFSS, which were implemented in the 2016 Rankings, make comparisons with estimates prior to that release year difficult. Additional changes to the methodology to create the estimates were implemented in the 2021 Rankings, further making comparisons with estimates prior to that release year difficult. Finally, current estimates are produced using sophisticated modeling techniques which make them difficult to use for tracking progress, especially 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.

Data Source

Years of Data Used


Behavioral Risk Factor Surveillance System

The Behavioral Risk Factor Surveillance System (BRFSS) is a state-based random digit dial (RDD) telephone survey that is conducted annually in all states, the District of Columbia, and U.S. territories. Data obtained from the BRFSS are representative of each state’s total non-institutionalized population over 18 years of age and have included more than 400,000 annual respondents with landline telephones or cellphones since 2011. Data are weighted using iterative proportional fitting (also called "raking") methods to reflect population distributions. For the County Health Rankings, data from the BRFSS are used to measure various health behaviors and health-related quality of life (HRQoL) indicators. HRQoL measures are age-adjusted to the 2000 U.S. standard population.

Prior to the 2016 County Health Rankings, up to seven survey years of landline only BRFSS data were aggregated to produce county estimates. However, even with multiple years of data, these did not provide reliable estimates for all counties, particularly those with smaller respondent samples. For the 2016 County Health Rankings and beyond, the CDC produced county estimates using single-year BRFSS data and a multilevel modeling approach based on respondent answers and their age, sex, and race/ethnicity, combined with county-level poverty, as well as county- and state-level contextual effects.[1] To produce estimates for those counties where there were no or limited data, the modeling approach borrowed information from the entire BRFSS sample as well as Census Vintage population estimates. CDC used a parametric bootstrapping method to produce standard errors and confidence intervals for those point estimates. This estimation methodology was validated for all U.S. counties, including those with no or small (< 50 respondents) samples.[2] This same method was used in constructing the 500 cities study, which includes BRFSS data for the 500 largest cities in the U.S. and can be found here:

For the 2021 Rankings, the CDC has updated their modeling procedure for producing small-area estimates. With the PLACES project, a multilevel statistical modeling framework using multilevel regression and poststratification (MRP) is performed for small-area estimation that links BRFSS data with high spatial resolution population demographic and socioeconomic data from the Census’ American Community Survey (ACS). The CDC has performed internal and external validation studies, which confirm strong consistency between their model-based estimates and the direct BRFSS survey estimates at both the state and county levels. For more technical information on the PLACES modeling procedure, please see their website.[3]

One limitation of the BRFSS is that all measures are based on self-reported information, which cannot be validated with medical records. Another limitation is that these model-based estimates were created by borrowing information from the entire BRFSS, which may or may not accurately reflect those counties’ local intervention experiences. Additionally, the confidence intervals constructed from these methods appear much smaller than confidence intervals reported for direct survey methods in previous years.

  1. Zhang X, Holt JB, Lu H, Wheaton AG, Ford ES, Greenlund K, Croft JB. Multilevel regression and poststratification for small-area estimation of population health outcomes: a case study of chronic obstructive pulmonary disease prevalence using the Behavioral Risk Factor Surveillance System. American Journal of Epidemiology 2014;179(8):1025–1033.
  2. Zhang X, Holt JB, Yun, S, Lu H, Greenlund K, Croft JB. Validation of multilevel regression and poststratification methodology for small area estimation of health outcomes. American Journal of Epidemiology 2015;182(2):127-137.
  3. PLACES Project. Centers for Disease Control and Prevention. Accessed March 9, 2021.
Digging Deeper
Subcounty Area1

The PLACES Project provides county-, city-, census tract-, and zip code-level small-area estimates for chronic disease risk factors, health outcomes, and clinical preventive service use, including Physical Inactivity, across the United States.

For larger counties, you can access county- or MSA-specific data from the CDC at However, using this data requires somewhat advanced analytic capabilities. In many states, you can access county level BRFSS estimates, and in some cases you can stratify those estimates by age, gender, education, income, or race. You can find BRFSS resources for most states in our Find More Data section.


[1] 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.
[2] 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.
[3] Piercy KL, Troiano RP, Ballard RM, Carlson SA, Fulton JE, Galuska DA, George SM, Olson RD. The physical activity guidelines for Americans. JAMA. 2018; 320(19):2020-2028.
[4] 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.
[5] PLACES Project. Centers for Disease Control and Prevention. Accessed March 9, 2021.

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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.

Establish a break from the school day, typically before lunch, that involves planned, inclusive, actively supervised games or activities; also called semi-structured, or structured recess
Offer group educational, social, creative, musical, or physical activities that promote social interactions, regular attendance, and community involvement among older adults
Offer exercise classes (e.g., aerobic dance, yoga, Tai Chi, cycling, etc.) and fitness program support in community, senior, fitness, and community wellness centers
Provide patients with prescriptions for exercise plans, often accompanied by progress checks at office visits, counseling, activity logs, and exercise testing
Support a combination of land uses (e.g., residential, commercial, recreational) in development initiatives, often through zoning regulations or Smart Growth initiatives

The County Health Rankings provide a snapshot of a community’s health and a starting point for investigating and discussing ways to improve health. Select a state and a measure below to see what’s happening locally.