Physical Inactivity

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Percentage of adults age 18 and over reporting no leisure-time physical activity (age-adjusted). The 2024 Annual Data Release used data from 2021 for this measure.

Physical inactivity is linked to increased risk of health conditions such as Type 2 diabetes, cancer, stroke, hypertension, cardiovascular disease, and shortened life expectancy.1

Physical activity is associated with improved sleep, cognitive ability, bone and musculoskeletal health, and reduced risk of dementia.2,3,4 Physical activity, in addition to diet, is important for the prevention of obesity.3

Environmental factors that impact an individual’s physical activity include the availability of recreation amenities, such as parks, bike paths, or walking trails.5 Communities with lower incomes that have experienced disinvestment are less likely to have recreational facilities and parks nearby that are accessible and affordable.3,6 

Find strategies to address Physical Inactivity

Data and methods

Data Source

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 United States 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. Data from the BRFSS are used to measure various health behaviors and health-related quality of life (HRQoL) indicators in the Health Snapshots and downloadable datasets. HRQoL measures are age-adjusted to the 2000 U.S. standard population. 

Prior to the 2016 Annual Data Release, 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 Annual Data Release 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.  

For the 2021 Annual Data Release, 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 

1Zhang 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. 

2Zhang 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. 

3PLACES Project. Centers for Disease Control and Prevention. Accessed March 9, 2021.

Key Measure Methods

Physical Inactivity is a percentage

Physical Inactivity is based on responses to Behavioral Risk Factor Surveillance System (BRFSS) surveys 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 leisure-time physical activities may be associated with age. We report an age-adjusted rate in order to fairly compare counties with differing age structures.

The method for calculating Physical Inactivity has changed

With the 2022 Annual Data Release, the source for this measure switched from the United States Diabetes Surveillance System to BRFSS. 

Physical Inactivity is created using statistical modeling

Surveys collect information about a limited portion of a population. Statistical modeling is used to obtain more informed and reliable estimates than survey data alone can provide.can be used to predict how people who share certain characteristics with those surveyed may have responded to the survey.  Modeling can increase the power of survey data by generatinges 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.

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 itstheir methods. 

Caution should be used when comparing these estimates across states

Estimates may not be comparable across states because of methodological changes discussed in “The method for calculating Physical Inactivity has changed”.

Caution should be used when comparing these estimates across years

Estimates may not be comparable across years because of methodological changes discussed in “The method for calculating Physical Inactivity has changed”.


The numerator is the number of 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 respondents age 18 and older.

Measure limitations

This measure does not account for physical activity as a part of paid or unpaid labor. Many physically active jobs are minimum wage or low-income positions; this may result in lower-income communities being inaccurately labeled "physically inactive" and could reinforce stigmatizing ideas of inactivity among economically disadvantaged populations.   

Can This Measure Be Used to Track Progress

Modeled estimates have specific drawbacks with their usefulness in tracking progress in communities. Modeled data may not capture the effects of local conditions, such as health promotion policies. In order to better understand and validate modeled estimates, it can be helpful to supplement with additional local data.

Additionally, methodological changes limit the ability to track progress across years using this measure. For more information on methodological changes, see above.

Finding More Data

Disaggregation means breaking data down into smaller, meaningful subgroups. Disaggregated data are often broken down by characteristics of people or where they live. Disaggregated data can reveal inequalities that are otherwise hidden. These data can be disaggregated by:

  • Age
  • Gender
  • Race
  • Education
  • Income
  • Subcounty Area

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. However, using these 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.


1 Lee IM, Shiroma EJ, Lobelo F, et al. 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 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.   

Gray CL, Messer LC, Rappazzo KM, Jagai JS, Grabich SC, Lobdell DT. The association between physical inactivity and obesity is modified by five domains of environmental quality in U.S. adults: A cross-sectional study. PLOS ONE. 2018;13(8):e0203301. 

Task Force on Community Preventive Services. Recommendations to increase physical activity in communities. American Journal of Preventive Medicine. 2022;22(4):67-72.

5 Humpel N, Owen N, Leslie E. Environmental factors associated with adults’ participation in physical activity: A review from the American Journal of Preventive Medicine. 2022;22(3):188-199  

6 Aytur SA, Rodriguez DA, Evenson KR, Catellier DJ, Rosamond WD. The sociodemographics of land use planning: Relationships to physical activity, accessibility, and equity. Health & Place. 2008;14(3):367-385.