The ACSM American Fitness Index: Using Data to Identify Opportunities to Support Physical Activity : Translational Journal of the American College of Sports Medicine

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The ACSM American Fitness Index: Using Data to Identify Opportunities to Support Physical Activity

Zollinger, Terrell W.1; Ainsworth, Barbara E.2,3; Thompson, Walter R.4; Volpe, Stella L.5; Keith, NiCole R.6; Patch, Gretchen S.7; Coffing, Jessica M.8; Craft, Lynette L.7

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Translational Journal of the ACSM 8(1):p 1-11, Winter 2023. | DOI: 10.1249/TJX.0000000000000223
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The American College of Sports Medicine (ACSM) launched the ACSM American Fitness Index® (Fitness Index) program in 2008 because a void existed among other summary reports that did not take into consideration both personal health behaviors and the environment in which people live to summarize the fitness of and resources for residents. Reported annually, the focus of the Fitness Index program is 1) to inform decision makers and the public of the health, social, and economic benefits of physical activity, including policies and infrastructure that promote healthy behaviors; 2) to engage city officials and residents to recognize their city’s strengths and opportunities to better promote a healthful environment; and 3) build local capacity and partnerships to implement policy and infrastructure changes to enable physically active lifestyles for all residents (1). The Fitness Index program provides data, materials, resources, and connections to health promotion partners on a Web site to help cities improve their indicators (2).

Since the mid-1980s, there has been considerable expansion of efforts to conduct and report surveillance activities to assess population health status and physical activity levels at the state level. Components of national surveys, such as the Centers for Disease Control and Prevention (CDC) Behavioral Risk Surveillance System survey (BRFSS) and the US Census Bureau American Community Survey (3,4), are frequently used by researchers who translate the findings and present them in the popular press. In the last few decades, it became possible to generate substate-level and city-level health and physical activity reports, as well as city-level infrastructure and other relevant measures. Population health measures have been aggregated at the state and substate levels for use by researchers and public health professionals (e.g., Robert Wood Johnson Foundation–supported County Health Rankings and more recently the CDC PLACES project (5,6)). Organizations also have evaluated and reported community assets that support physical activity (e.g., Trust for Public Land (7), Walk Score (8)). A few organizations have aggregated groups of health measures on focused topics; however, none were identified that aggregated both population health indicators and community assets into a single index to specifically measure cities’ overall fitness (9,10). To fill this void and in response to the goal of improving the nation’s health and promoting physically active lifestyles, the Fitness Index was developed.

Most city officials recognize that higher levels of physical fitness, healthier population attributes, and access to resources to promote physical activity can be major attractions for businesses, their employees, and future residents (11). Consequently, city officials, public health professionals, and community-based organizations interested in improving health measures benefit from understanding how residents’ health and community assets (e.g., walkability, parks, recreation centers) in their cities compare with those in other cities and how these influence focused interventions and policy efforts (12).

The central component of the Fitness Index program is a data report that includes individual city rankings and scores based on indicators of personal health behaviors and outcomes as well as community assets and policies that support physical activity. The 2022 Fitness Index included 34 indicators consisting of 9 health behaviors, 10 health outcomes, 6 built environment measures, 6 types of recreational facilities, and 3 policy measures as shown in Fig. 1. These indicators were used to create scores and ranks for two subscales and a total score for each city evaluated.

Figure 1:
Fitness Index indicators and scores.

This article describes the Fitness Index methods and data sources, along with its strengths and limitations. The rationale and background that led to the development of the Fitness Index and the steps involved in creating it are also included. In addition, this article discusses how the Fitness Index evolved during the past 15 yr to remain current and relevant for stakeholders. Finally, suggestions are presented for how these rankings and data can be translated into action within cities.


The first step in creating the Fitness Index involved identifying available population health and community measures related to physical fitness and mental health. A national panel of 26 content experts, using a modified Delphi method to reach consensus, assessed and weighted possible measures on a scale from 0 to 3. The panelists were asked to rate the importance of each measure based on their expert opinion if the measure was not relevant (and thus, not included), had lower importance or impact on fitness (weight = 1), had moderate importance or impact on fitness (weight = 2), or had high importance or impact on fitness (weight = 3). From this process, 31 indicators were originally identified for inclusion in the Fitness Index calculations and then separated into two indicator subgroups: personal health and community/environment.

Indicator values were gathered from the publicly available data sources shown in Table 1 and entered into a spreadsheet for analysis. Two indicators were coded before being entered in the spreadsheet: Complete Streets Policies and Physical Education Requirement (shown in Table 2). Complete Streets Policies were graded on a scale from 0 to 2 by type of policy at the city or county level with those including enforcement mechanisms receiving the highest grade. State-level education policies were used for the Physical Education Requirement indicator. Local school district policies may be stronger, but not weaker, than state policies. Because cities typically have multiple school districts, it was not feasible to create a single code for a city-level physical education requirement indicator. The Physical Education Requirement indicator was coded from 1 to 3, based on the number of grade levels (elementary, middle, high schools) for which the state required physical education.

TABLE 1 - Fitness Index Indicators Included from 2008 to 2022.
Source Fitness Index Report Years
Health behaviors
 % exercising in the last 30 days CDC BRFSS—County Data 2008–2022
 % physically active at least moderately CDC BRFSS—County Data 2008–2013
 % meeting aerobic activity guidelines CDC BRFSS—County Data 2014–2022
 % meeting aerobic and strength activity   guidelines CDC BRFSS—County Data 2014–2022
 % bicycling or walking to work US Census American Community Survey 2008–2022
 % using public transportation to work US Census American Community Survey 2008–2022
 % consuming 5+ fruits/vegetables per day CDC BRFSS—County Data 2008–2013
 % consuming 2+ fruits per day CDC BRFSS—County Data 2014–2022
 % consuming 3+ vegetables per day CDC BRFSS—County Data 2014–2022
 % sleeping 7+ hours per day CDC BRFSS—County Data 2018, 2021–2022
 % smoking CDC BRFSS—County Data 2008–2022
Health outcomes
 % in excellent or very good health CDC BRFSS—County Data 2008–2022
 % physical health not good during the   past 30 days CDC BRFSS—County Data 2008–2022
 % mental health not good during the   past 30 days CDC BRFSS—County Data 2008–2022
 % with obesity CDC BRFSS—County Data 2008–2022
 % with asthma CDC BRFSS—County Data 2008–2022
 % with high blood pressure CDC BRFSS—County Data 2017–2022
 % with angina or coronary heart disease CDC BRFSS—County Data 2008–2022
 % with stroke CDC BRFSS—County Data 2017–2022
 % with diabetes CDC BRFSS—County Data 2008–2022
 Cardiovascular disease deaths/100,000 residents CDC Wonder 2008–2017
 Diabetes deaths/100,000 residents CDC Wonder 2008–2017
 Pedestrian fatalities/100,000 residents National Highway Traffic Safety Administration 2019–2022
 % with health insurance CDC BRFSS—County Data 2008–2013
Built environment
 Air quality index Environmental Protection Agency 2019–2022
 Bike Score® Bike Score® 2019–2022
 Farmers markets/1,000,000 residents USDA Farmers Market 2008–2020
 % with food insecurity Feeding America® Map the Meal Gap 2021–2022
 Park land as a percent of MSA land area Trust for Public Land—City Park Facts 2008–2018
 Parks/10,000 residents Trust for Public Land—City Park Facts 2008–2022
 Acres of parkland/1000 residents Trust for Public Land—City Park Facts 2008–2018
 % within a 10-min walk to a park Trust for Public Land—City Park Facts 2015–2022
 Walk Score® Walk Score® 2014–2022
 Primary health care providers/100,000   residents HRSA Area Health Resource File 2008–2013
Recreational facilities
 Ball diamonds/10,000 residents Trust for Public Land—City Park Facts 2008–2022
 Dog parks/10,000 residents Trust for Public Land—City Park Facts 2008–2018
 Basketball hoops/10,000 residents Trust for Public Land—City Park Facts 2017–2022
 Park playgrounds/10,000 residents Trust for Public Land—City Park Facts 2008–2022
 Golf courses/100,000 residents Trust for Public Land—City Park Facts 2008–2016
 Recreational centers/20,000 residents Trust for Public Land—City Park Facts 2008–2022
 Swimming pools/100,000 residents Trust for Public Land—City Park Facts 2008–2022
 Tennis courts/10,000 residents Trust for Public Land—City Park Facts 2008–2022
Policy and funding
 Complete Streets policy Smart Growth America/National Complete  Streets Coalition—City Policy 2019–2022
 Park expenditure/resident (adjusted) Trust for Public Land—City Park Facts 2008–2022
 Physical education requirement National Association of State Boards of  Education—State Policy 2008–2022
HRSA, US Health Resources and Services Administration; USDA, US Department of Agriculture.

TABLE 2 - Policy Grading Rubric Used to Assign Values before Ranking.
Complete streets policy type
 Ordinance, law, tax levy 2
 Policy, design manual/guide, plan, internal   policy, executive order, resolution 1
 No policy type 0
Physical education requirement
 Required at 3 grade levels (elementary,   middle, and high schools) 3
 Required at 2 grade levels 2
 Required at 1 grade level 1

Indicators were then ranked (worse value = 1) and multiplied by the weight assigned by the expert panel. Unhealthy indicators, such as percentage of residents smoking, were reverse ranked (worst value = 100). The weighted ranks were then summed by indicator subgroup to create subscores. Overall scores were then standardized to a scale with an upper limit of 100 by dividing the overall score by the maximum possible value and multiplying by 100. The individual weighted scores were also averaged for both indicator subgroups to create the total score. Both the subscores and the total scores for the 100 cities were then ranked (best = 1). The following formula summarizes the scoring process:

City Scorek=rkiwki/CityScorekmax×100

where n = the number of personal health indicators or number of community/environment indicators, k = the indicator group, r = the city rank on the indicator, w = the weight assigned to the indicator, and City Scorekmax = the hypothetical score if a city ranked best on each indicator in the group.

The Fitness Index included indicators for the most populated US communities designated by the US Office of Management and Budget using US Census Bureau data. From 2008 to 2017, metropolitan statistical areas (MSAs) were chosen as the unit of measurement because they represent the group of counties comprising the larger urban areas where residents live, work, and access community assets. Starting in 2018, the Fitness Index shifted the unit of measurement to cities rather than MSAs. This approach acknowledges that the central city and surrounding suburbs are controlled by different government agencies and may have different health behaviors and community-level infrastructure to support physically active lifestyles. Among the most populous cities, it should be noted that Arlington, VA, is sometimes referred to as Arlington County, whereas other times, just Arlington. The US Census Bureau refers to Arlington, VA, as a “Census Designated Place”—a city, town, place equivalent, and township—and is included in the Fitness Index list of the 100 most populated cities (13).


The Fitness Index utilizes indicators that are generally available to the public and, to ensure validity and reliability, are from reputable, regularly updated sources as shown in Table 1. Only modifiable measures are included to enable city officials to implement targeted programs, policies, and strategies to improve fitness. Each year, the Fitness Index content experts, ACSM staff, and technical consultants review potential new measures, data sources, and current indicator weights to determine if changes are necessary to ensure that the Fitness Index data report is up-to-date and uses the best measures available. Nineteen of the 34 indicators used in the 2022 report have been used in the Fitness Index since 2008 as shown in Table 1. Changes in the indicators used are the result of changes in the data collected by the sources or new measures that became available and were judged to be important, whereas less impactful or less reliable measures were removed.

Occasionally, missing data were encountered and addressed in various ways, depending on the indicator. The Fitness Index summary report, an executive summary of the data report, includes a footnote to indicate where data were either missing or not reported in the same way across cities. When appropriate, alternative ways to obtain missing data are noted in the summary report.

In addition to the indicators used to create the Fitness Index, population characteristics were obtained (e.g., age, race/ethnicity, disability, education, poverty levels) for each city from the most recent census reports. Population characteristics are not included in the Fitness Index scoring calculations or rankings; however, they are made available to city officials to help identify other cities with comparable characteristics for better interpretation of the scores and rankings. These data values are made available on the Fitness Index Web site in a city comparison tool (14). Because the data used for the Fitness Index were previously collected by other organizations and publicly available in a de-identified summary form, the Fitness Index program was judged to be exempt from institutional review board approval.


ACSM designed the Fitness Index to help city officials identify opportunities to improve the health of residents and expand community assets, including policies, to better enable physically active lifestyles. Cities with the highest scores in the Fitness Index report have high community fitness, a concept analogous to individuals having high personal fitness.

Results from the 2022 Fitness Index showed high rankings for cities where residents had good health behaviors and sufficient community assets to support physical activity and health, as is seen in Table 3 (15,16). The higher-ranking communities also provided more financial support for parks with recreational features and enacted policies supporting school physical education and Complete Streets for safer walking, bicycling, and other forms of active living (17–19). Conversely, rankings were lower for cities where residents had less positive health behaviors and a higher prevalence of chronic disease risk factors and conditions, as well as fewer community assets and policies to support physical activity (20).

TABLE 3 - 2022 Fitness Index Rankings and Scores (out of 100) for the 100 Largest Cities in the United States
Cities Total Personal Health Community/Environment
Rank Score Rank Score Rank Score
Arlington, VA 1 85.0 1 86.8 1 82.5
Madison, WI 2 78.2 2 84.9 22 68.5
Minneapolis, MN 3 78.0 4 76.7 3 80.0
Washington, DC 4 77.9 5 75.7 2 81.1
Seattle, WA 5 77.8 3 79.5 12 75.2
Irvine, CA 6 72.0 6 75.6 25 66.9
Portland, OR 7 71.4 12 71.8 18 70.8
St. Paul, MN 8 71.4 19 65.5 4 79.9
Denver, CO 9 69.9 9 72.6 27 65.9
Chicago, IL 10 69.6 22 63.5 6 78.4
Oakland, CA 11 69.1 8 73.0 32 63.5
Boise, ID 12 66.5 18 66.1 24 67.0
Boston, MA 13 66.4 31 59.0 10 77.1
San Francisco, CA 14 65.8 27 62.1 17 71.1
Aurora, CO 15 63.8 17 66.5 37 60.0
Lincoln, NE 16 63.5 26 62.1 29 65.6
New York, NY 17 63.1 21 64.3 36 61.3
Atlanta, GA 18 62.0 36 57.7 23 68.2
Jersey City, NJ 19 62.0 15 67.9 45 53.4
San Jose, CA 20 62.0 11 72.3 57 47.1
Buffalo, NY 21 61.6 55 50.3 8 77.9
Honolulu, HI 22 61.3 25 62.3 38 59.9
San Diego, CA 23 61.2 20 64.6 42 56.3
Santa Ana, CA 24 61.0 7 74.5 75 41.6
Tampa, FL 25 60.3 33 58.8 34 62.6
Fremont, CA 26 60.3 13 69.8 58 46.5
Austin, TX 27 59.5 14 68.0 56 47.2
Sacramento, CA 28 58.4 49 53.4 31 65.5
Plano, TX 29 58.3 29 61.0 44 54.5
Spokane, WA 30 58.2 56 49.9 20 70.1
Anaheim, CA 31 57.8 10 72.5 88 36.7
Milwaukee, WI 32 57.8 67 45.0 11 76.4
Albuquerque, NM 33 57.8 50 52.3 28 65.8
Raleigh, NC 34 57.8 24 63.0 50 50.3
Tucson, AZ 35 57.7 37 56.9 41 59.0
Richmond, VA 36 57.7 57 49.7 21 69.2
Durham, NC 37 57.0 16 66.9 69 42.7
Pittsburgh, PA 38 56.7 72 41.3 5 78.9
St. Petersburg, FL 39 56.0 63 46.1 19 70.2
Miami, FL 40 55.0 58 49.6 33 62.9
Long Beach, CA 41 55.0 44 54.1 43 56.3
Glendale, AZ 42 54.8 41 55.9 46 53.2
Virginia Beach, VA 43 54.7 35 58.3 52 49.6
Omaha, NE 44 54.5 62 46.2 26 66.5
Newark, NJ 45 54.1 54 50.4 40 59.4
New Orleans, LA 46 53.7 65 45.4 30 65.6
Norfolk, VA 47 53.5 74 39.1 14 74.3
Chula Vista, CA 48 53.5 28 61.6 74 41.7
Colorado Springs, CO 49 52.9 30 60.7 72 41.8
Reno, NV 50 52.9 32 58.8 64 44.3
Orlando, FL 51 52.4 60 47.3 39 59.7
Los Angeles, CA 52 52.0 40 56.3 61 45.7
Winston-Salem, NC 53 51.7 34 58.6 73 41.8
Charlotte, NC 54 51.6 23 63.1 92 35.1
Cleveland, OH 55 51.1 81 33.0 9 77.3
Anchorage, AK 56 51.0 38 56.5 67 43.2
Dallas, TX 57 50.1 51 51.9 55 47.6
Chandler, AZ 58 49.3 47 53.8 68 42.8
Hialeah, FL 59 49.2 61 46.6 47 53.1
Scottsdale, AZ 60 49.2 42 55.9 81 39.6
Houston, TX 61 48.7 39 56.4 85 37.5
Philadelphia, PA 62 48.5 88 28.0 7 78.0
Nashville, TN 63 48.0 46 53.9 82 39.5
Stockton, CA 64 48.0 53 51.8 70 42.5
Mesa, AZ 65 47.9 43 55.2 86 37.2
Phoenix, AZ 66 47.4 45 54.1 84 37.7
Baltimore, MD 67 46.8 91 27.4 13 74.7
Cincinnati, OH 68 45.7 90 27.6 16 71.8
San Antonio, TX 69 45.1 52 51.9 91 35.2
St. Louis, MO 70 44.2 96 23.9 15 73.4
Jacksonville, FL 71 43.6 73 40.6 54 48.1
Greensboro, NC 72 43.2 71 41.8 62 45.1
Gilbert, AZ 73 42.5 48 53.5 98 26.7
Garland, TX 74 42.4 59 47.4 90 35.2
Fort Wayne, IN 75 42.1 64 45.5 87 37.2
Columbus, OH 76 41.3 80 33.7 48 52.1
El Paso, TX 77 41.1 75 38.7 63 44.5
Fresno, CA 78 39.9 66 45.2 94 32.2
Laredo, TX 79 39.1 79 34.4 60 45.8
Irving, TX 80 38.5 76 38.5 83 38.6
Corpus Christi, TX 81 38.3 85 30.3 51 49.7
Fort Worth, TX 82 38.0 68 44.2 96 29.0
Arlington, TX 83.5 37.8 69 44.2 97 28.7
Toledo, OH 83.5 37.8 98 21.3 35 61.6
Bakersfield, CA 85 37.3 70 42.7 95 29.5
Lubbock, TX 86 37.1 78 34.6 77 40.8
Chesapeake, VA 87 37.0 77 34.6 79 40.6
Kansas City, MO 88 36.4 87 28.1 53 48.4
Wichita, KS 89 35.8 83 31.5 71 42.0
Riverside, CA 90 35.5 82 32.4 80 39.9
Detroit, MI 91 35.2 95 24.6 49 50.5
Lexington, KY 92 34.4 86 29.6 76 41.2
Henderson, NV 93 34.3 92 26.0 59 46.3
Memphis, TN 94 33.0 84 30.7 89 36.4
Las Vegas, NV 95 32.9 93 25.4 65 43.6
Louisville, KY 96 30.5 97 21.7 66 43.2
Indianapolis, IN 97 29.7 89 27.8 93 32.5
Tulsa, OK 98 26.9 100 17.4 78 40.7
North Las Vegas, NV 99 25.9 94 25.3 99 26.6
Oklahoma City, OK 100 20.1 99 19.8 100 20.5

Cities that ranked near the top had scores in the 70s and 80s on a 100-point scale, indicating that all cities still had room for improvement. Similarly, cities near the bottom of the rankings generally had scores in the 20s, 30s, and 40s on a 100-point scale, showing that residents in those cities had some good personal health indicators or outcomes, and there were community assets in place that could provide a foundation for focused initiatives and more supportive policies.

The rankings of the personal health subscores are generally correlated with the rankings of the community/environment subscores. For example, in the 2022 Fitness Index, the top-ranked city overall, Arlington, VA, ranked first in both the personal health subscores and the community/environment subscores. Minneapolis, MN, which ranked third overall, ranked fourth in the personal health subscores and third in the community/environment subscores. Four of the top 10 cities ranked among the top for the personal health and community/environment indicators. This implies that these cities were supportive of walking and bicycling, provided access to parks with various recreational facilities (ball diamonds, basketball hoops, playgrounds, recreational centers, swimming pools, and tennis courts), had park funding, and had physical education policies (17,21,22). Cities ranked in the bottom 10 had fewer of these assets, with three cities ranked from 93 to 100 in the community/environment indicators.

When ACSM launched the Fitness Index in 2008, there were very few city-level reports on the health and well-being of residents, and none were identified that provided a single combined measure of multiple indicators. Uniquely, the ACSM combined personal health indicators with community assets in one index measure and at a metropolitan level. In the beginning, MSAs were used because they included not only activities and other assets within the city limits, but also captured suburban areas where residents contribute to the personal and community assets of the city. Later, with better city-level data available, an understanding of the demographic differences of urban and suburban residents, and recognizing the need for city-specific data for city policy makers, the Fitness Index report was narrowed to cities in 2018 (23). Even today there seem to be no other reports that include personal health and community assets in a single focused city-level report. City officials have used the Fitness Index and the rankings to make better judgments when reviewing the needs of their city and its citizens.

The 100 cities included in the Fitness Index are home to 64.8 million people, approximately 20% of the US population (24). Consequently, as these cities’ officials continue their efforts to improve the health and fitness of their residents, a large proportion of the US population could benefit. The data sources used for the Fitness Index are publicly available from reliable sources that have been updated regularly. As such, officials of cities not included in the Fitness Index are able to collect indicator data for their city using similar approaches to identify opportunities to provide support for physical activity and health among their residents. Furthermore, because the Fitness Index report identifies cities with the best values for each indicator, these cities can serve as models that other cities could emulate.

A challenge for the selection of personal health and community asset indicators for the Fitness Index was choosing indicators that can reasonably be changed in a timely fashion through personal and city-level action. For example, including indicators that may be difficult to change (e.g., availability of land space in a city to create new parks) may make it more difficult for a city to improve that indicator from one year to the next.

A majority of personal health indicators and outcomes do not change rapidly, and there may be a significant time lag for the impact of new initiatives to be seen in some of the health indicators (25). Improvements in community asset indicators are important long-term investments; consequently, notable improvements in the health of residents are expected to slowly but surely follow (26).

Because private recreational spaces are not counted in the national database, the actual availability of community assets may be underestimated in the data report; only those managed by the city’s parks and recreation department are counted. For example, privately owned and commercial facilities, such as health clubs, YMCAs, and gyms that require paid memberships, as well as recreational facilities integrated into neighborhoods supported by homeowners’ association fees, are not included.

Some data limitations for the Fitness Index include sample variability from year to year and could seem to be a slight change in a city’s scores and ranking. Much of the data used in the Fitness Index are responses to surveys that have well-known biases and variability. Also, the lack of important community asset data available for all 100 cities, such as the number of miles of multipurpose trails per capita, is another limitation. Including these indicators would add value to the Fitness Index. Finally, using city-level data limits inferences to subcity geographical areas, such as neighborhoods or townships, for comparisons.

Dissemination and Use

The Fitness Index project places pertinent and important information at the fingertips of city officials to support policies that can directly impact the health and fitness of the residents. As such, the Fitness Index uses a robust dissemination plan to reach targeted audiences. Strategies were tailored to reach diverse audiences and to leverage multiple forms of communication as shown in Table 4. The ultimate goal of the dissemination plan is to provide public health professionals, community-based organizations, and residents involved in local advocacy efforts with the data needed to engage city officials in data-driven policy decisions. For example, advocates can cite per-capita funding of city parks and recreation departments for their cities and how access to parks and similar assets directly affect residents’ ability to be physically active and improve their mental health. Research shows that investment in public parks results in an increase in physical activity (22). In addition, it is important for cities to support the development of infrastructure that promotes walking and bicycling, in addition to parks and recreational facilities (27,28). This includes having places to walk within a close proximity to home like parks and recreational facilities, walking and bicycling trails, and other free and low-cost places for physical activity (18,29). Adoption and enforcement of Complete Streets policies to provide connected sidewalks and protected bicycle lanes in addition to driving lanes not only enables residents to commute or recreate safely on foot or nonmotorized devices, but also results in more ecologically friendly transportation (30).

TABLE 4 - Dissemination Strategies for the Fitness Index.
Strategy Target Audience Media Type*
Direct email campaigns ACSM members and certified professionals, city officials (e.g., mayors; councilors; departments of public health, transportation, parks and recreation), newsletter subscribers Owned, paid
News releases and media advisories Journalists (broadcast, print, online, radio) Earned
Social media: ACSM members and certified professionals, city officials, community-based organizations, public Earned, owned, paid
 Facebook, Instagram, LinkedIn, Pinterest, Twitter
Traditional media: City officials, public Earned
 Broadcast (radio, television), print (newspapers, magazines)
Web sites: ACSM members and certified professionals, city officials, community-based organizations, journalists, public Owned
 Blog, city comparison tool, infographic, summary report
*Media types: owned, exposure achieved via platforms owned and controlled by an organization; paid, exposure achieved by purchasing access to target audiences; earned, exposure achieved based solely on the merit of the content.

The Fitness Index is a significant resource for researchers interested in studying associations between personal health indicators and community support for physical activity and policy decisions affecting overall population health. For example, it may be used to answer the question, “Does a connection exist between a city’s walkability, Complete Streets policies, number of pedestrian fatalities per 100,000 residents, and other health outcomes?” Data are also available for multiple years, allowing for longitudinal analyses. Social and behavioral scientists might use the Fitness Index to compare changing behaviors within the culture of a targeted city or make comparisons between geographically different cities (e.g., northeast versus southwest or east coast versus west coast cities). Political scientists might also find it interesting to determine if a certain political climate attracts more (or less) fit people.


The Fitness Index provides a resource for city officials, public health professionals, and community-based organizations to examine how their city can support physical activity and health for residents. The Fitness Index not only provides a more comprehensive assessment of a city’s fitness but also helps identify a variety of opportunities for city officials to focus efforts on improving specific indicators that are included in the Index. Individual behavior efforts to become more fit would be difficult if community support for physically active lifestyles was not in place. City officials can use the Fitness Index rankings and data to identify areas of excellence of peer cities that can provide model programs and policies. The data can also help prompt city officials to evaluate streets for safe walking and bicycling, to identify opportunities for improving access to parks and park amenities within neighborhoods, and to pass policies that ensure adequate funding for these community assets.

The authors would like to acknowledge all the Advisory Board members who have contributed to the ACSM American Fitness Index.

T.W.Z. and J.M.C. received payments to collect and analyze data for the American Fitness Index under contract from ACSM. G.S.P. and L.L.C. were full-time employees of ACSM during the period of this study. The other authors report no conflicts of interest. Funding for the Fitness Index was provided by the Elevance Health Foundation and ACSM.


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