Park-Based Physical Activity in Diverse Communities
of Two U.S. Cities
An Observational Study
Myron F. Floyd, PhD, John O. Spengler, JD, PhD, Jason E. Maddock, PhD, Paul H. Gobster, PhD,
Luis J. Suau, MS
Background: Systematic study of human behavior in public parks and specific activity settings can inform
policy to promote physical activity in diverse communities.
Direct observation was used to assess physical activity in public parks in Tampa FL (n?10)
and Chicago IL (n?18). Parks were selected from census tracts with high concentrations
of white, African-American, and Hispanic populations. Representation from low- and
high-income census tracts was also achieved. Physical activity was measured by a modified
version of the System for Observing Play and Leisure Activity in Youth (SOPLAY). Activity
codes from SOPLAY were transformed to energy expenditure per person (kcal/kg/min).
Seventy percent of Tampa and 51% of Chicago park users were observed engaged in
sedentary behavior. In both cities, children were more likely than adults to be observed in
walking or vigorous activity. In Tampa, parks located in neighborhoods with the highest
concentration of Hispanic residents were associated with greatest levels of energy expen-
diture. In Chicago, parks in neighborhoods with the highest concentration of African
Americans showed the highest energy expenditure per person. Gender was associated with
physical activity only in Tampa parks. Energy expenditure also varied by activity areas.
Conclusions: More than one half of park users in both cities engaged in sedentary behavior. While
differences in park-based physical activity by neighborhood income and racial/ethnic
composition were observed, these differences can more likely be attributed to the types of
designated activity areas that support physical activity. The study findings suggest that
specific configurations of park environments can enhance physical activity in parks.
(Am J Prev Med 2008;34(4):299 –305) © 2008 American Journal of Preventive Medicine
ies show that adults and children from racial and ethnic
minority groups get less physical activity than their white
counterparts.1,2 Racial and ethnic minorities and low-
income populations also bear a disproportionate risk of
experiencing chronic diseases3 among which obesity
and overweight, stroke, diabetes, depression and anxi-
ack of physical activity among U.S. residents is a
major health concern, particularly among low-
income and minority populations. National stud-
ety, colon cancer, and cardiovascular diseases are
linked to physical inactivity.4 Efforts to increase physical
activity in diverse communities could have positive
The ecologic model of health behavior examines
how the modification of built environment features can
positively affect behaviors such as physical activity.5,6
Public parks can play a substantial role in increasing
leisure-time physical activity because they offer a wide
range of free or low-cost activities close to where people
live and because their existence, design, and quality are
influenced through public policy.7,8 Access to parks
and recreation areas has been identified as an impor-
tant predictor of physical activity,9 –11 and a national
study estimates that 70% of U.S. residents live within
walking distance of a public park.12 Moreover, 80% of
U.S. residents report using public parks, and nearly one
person in four uses them “frequently.” If neighborhood
parks are to help increase physical activity in diverse
and disadvantaged communities, research is needed to
describe how parks are used and identify which settings
support physical activity. Studies of ethnically diverse
From the Department of Parks, Recreation, and Tourism Manage-
ment, North Carolina State University (Floyd), Raleigh, North Caro-
lina; the Department of Tourism, Recreation, and Sport Manage-
ment, University of Florida (Spengler, Suau), Gainesville, Florida;
the Department of Public Health Sciences, University of Hawaii
(Maddock), Honolulu, Hawaii; and the U.S. Forest Service, Northern
Research Station (Gobster), Evanston, Illinois
Address correspondence and reprint requests to: Myron F. Floyd,
PhD, Department of Parks, Recreation, and Tourism Management,
Box 8004, 4012D Biltmore Hall, Raleigh NC 27695-8004. E-mail:
The full text of this article is available via AJPM Online at
www.ajpm-online.net; 1 unit of Category-1 CME credit is also avail-
able, with details on the website.
Am J Prev Med 2008;34(4)
© 2008 American Journal of Preventive Medicine • Published by Elsevier Inc.
0749-3797/08/$–see front matter
and underserved populations are also needed to ad-
dress disparities in physical activity through the provi-
sion of parks and recreation facilities.
Few studies have examined how the capacity of parks
and the activity spaces within them contribute to phys-
ical activity. McKenzie et al.13observed users of eight
large parks in Los Angeles and found that 62% of male
park users and 71% of female park users were seden-
tary. Vigorous-intensity activity in specific activity areas
ranged from 2% to 34% and was significantly lower in
picnic (13%) and open-space areas (28%) than in all
sport facilities (32%–34%) except for baseball/softball
fields (23% vigorous activity). Another study found that
girls living near parks with playgrounds, basketball
courts, walking trails, swimming areas, and tracks accu-
mulated more moderate-to-vigorous physical activity
(MVPA) than girls living farther away.11However, res-
idential areas with predominantly African-American
and Hispanic populations appear to have lower access
to park and recreation facilities.14–16
These study findings are helpful but provide an
incomplete understanding of the association between
public parks and physical activity in diverse communi-
ties. While parks have the potential to support physical
activity, a substantial amount of use can be sedentary.
Considerable variation exists in the conduciveness of
specific activity settings within parks to support physical
activity. Most studies of parks and physical activity have
focused on proximity of parks and have not linked
physical activity to specific modifiable park attributes.
Because public parks can be influenced through public
policy, identifying features most likely to support MVPA
in diverse communities could suggest how park settings
can be managed to increase physical activity among
residents. The objectives of the present study were to
(1) assess levels of physical activity in selected neigh-
borhood parks, (2) compare levels of physical activity
observed in parks located in neighborhoods of dif-
ferent racial/ethnic and income composition, and
(3) examine whether levels of physical activity asso-
ciated with specific activity areas vary by the racial/
ethnic and income composition of neighborhoods.
Data and Setting
Study data came from direct-use observations of ten neigh-
borhood parks in Tampa FL and 18 parks in Chicago IL.
ArcGIS 9.0 and census files were used to identify parks in
racially and ethnically diverse communities. Attempts were
made to select parks in predominantly (?50%) white (non-
Hispanic), African-American (black, non-Hispanic), and His-
panic census block groups and census tracts with low (below
metro area median and 30% below poverty) and upper/
middle (above metro area median and less than 10%
poverty) income within a 0.5-mile buffer. Also, attempts
were made to select three parks from each race/ethnicity-
by-income category, with the final selection done in con-
sultation with park administrators in each city. Block
groups had high representations of each ethnic group in
range?49%–61%; white range?72%–88%) and Chicago
(African-American range?60%–99%; Hispanic range?70%–
93%; white range?53%–84%). Actual median incomes for
the users of selected parks ranged from $27,321 to $50,368,
and poverty percentages ranged from 14% to 28%. In Chi-
cago neighborhoods, the median incomes of park users
ranged from $27,776 to $46,055, and poverty percentages
ranged from 10% to 34%. Parks were generally similar in
facilities, activity areas, and accessibility to residents located
within neighborhoods. The mean acreage for selected Tampa-
area parks was 41 acres with a range of 11 to 145 acres. The
mean acreage for Chicago-area parks was 46 acres with a
range of 8 to 207 acres.
Physical activity. Physical activity was measured using a mod-
ified version of the System for Observing Play and Leisure
Activity in Youth (SOPLAY)17similar to the method devel-
oped by McKenzie et al.13Observation codes accounted for
age group (children/adult), gender, and activity levels (sed-
entary, walking/moderate, and vigorous). Construct validity
of these physical activity codes has been established in previ-
ous studies.18,19Trained observers recorded observations of
physical activity in the parks between 10 AM and 6 PM from
Friday through Sunday during the spring (Tampa, March–
April) and early summer (Chicago, May–June) of 2005.
Following an established protocol, separate scans were made
for girls, boys, women, and men. Park activity areas were
scanned visually from left to right and the codes representing
park users’ activity levels were recorded on a standardized
form. Four scanning periods were conducted for each activity
zone (two for AM, two for PM hours). Cohen’s kappa coeffi-
cients for inter-observer agreement between paired observers
ranged from 0.79 to 0.97, which is well within the acceptable
Physical activity codes were converted to energy expendi-
ture (kcal/kg/min), providing a second measure of physical
activity using previously validated codes.13Energy expendi-
ture was estimated by summing the number of individuals in
sedentary, walking, and vigorous categories and then multi-
plying by their respective constants, 0.051kcal/kg/min,
0.096kcal/kg/min, and 0.144kcal/kg/min.17Calculation of
energy expenditure per person enables transformation of
these data into a linear format to compare relative activity
levels across parks, activity spaces, and neighborhoods. This
also allows for a comparison of means through ANOVA, using
energy expenditure per person as the dependent variable and
activity areas and neighborhood composition as independent
Age group and gender. Observers categorized individuals in
parks into two age groups, children and adults. Children were
coded as anyone who appeared to be 12 and under following
previously validated protocols.13High inter-rater reliability
indicated a sufficient agreement between observers on cate-
gorizations. Similarly, categorization as male or female was
based on apparent gender.
American Journal of Preventive Medicine, Volume 34, Number 4www.ajpm-online.net
Park activity zones. Activity zones for all parks and their
boundaries were mapped by two members of the research
team prior to observations. In most cases, activity zones
coincided with established recreation use areas such as play-
grounds, courts (e.g., tennis, basketball), picnic areas, sports
fields, and open spaces (see Table 1 for complete listing).
Each member of the observation team was instructed on zone
boundaries and carried a map of activity zones into the field.
Neighborhood composition. Neighborhood racial/ethnic com-
position was a categoric variable with three attributes (white,
African American, and Hispanic) taken from topologically
integrated geographic encoding and referencing (TIGER)
census files. Neighborhood income was a dichotomous vari-
able (low and middle/upper) from the same data source.
A combined racial/ethnic and income variable (e.g., low-
income white, high-income white) was created to examine
physical activity by neighborhood composition.
Differences in physical activity levels by age group and gender
were tested using chi-square. Differences in mean energy
expenditure by activity areas and racial/ethnic neighbor-
hoods were assessed by one-way ANOVA. Scheffe’s post-hoc
test was used to specify sources of difference in multiple
group comparisons. Differences in mean energy expenditure
by neighborhood income were evaluated by t-tests. All analy-
ses were conducted with SPSS version 14.0.
A total of 7043 park users were observed in the ten
Tampa parks; a total of 2413 were observed in the 18
Chicago parks. Overall, 11% of park users were ob-
served in vigorous activity, 23% were observed walking,
and 65% were observed as sedentary. The breakdown
for Tampa park users was 8% vigorous, 21% walking,
and 70% sedentary; for Chicago it was 22% vigorous,
28% walking, and 51% sedentary. Significantly more
adults than children were observed in the parks, espe-
cially in Tampa parks (56.4% vs 43.6%, Chicago; 66.3%
vs 33.7%, Tampa). Men and boys were significantly
more likely to be observed in the parks than women
and girls, with the pattern more pronounced in Chi-
cago parks (68.4% vs 31.6%, Chicago; 51.3% vs 48.7%,
Statistically significant associations were observed
between physical activity and age group and gender.
In Tampa parks, more children (44.4%) than adults
(23.2%) were observed in walking or vigorous activity
children were observed in walking or vigorous activity
compared to 47.2% of adults (?2
Gender differences were significant only for Tampa park
users, where 33.6% of males and 26.8% of females were
observed in walking or vigorous activity (?2
(2)?529.7, p?0.001). In Chicago parks, 52% of
Variation in Physical Activity by
Mean energy-expenditure-per-person values for parks in
different neighborhood types are shown in Table 2.
Overall, significant differences in mean energy ex-
penditure were observed in Tampa and Chicago
parks. In Tampa, parks in neighborhoods (census
tracts) with large concentrations of Hispanic Ameri-
cans showed the highest mean energy expenditure per
person (mean?0.069), followed by parks in predomi-
nantly white areas (mean?0.068) and parks in predomi-
nantly African-American areas (mean?0.067) (F?3.06,
p?0.047). Post-hoc tests revealed significant differences
Table 1. Primary activity zones in study parks used for physical activity observations by neighborhood type
109 229152 184
aCourts include tennis, racquetball, volleyball, and basketball courts.
bSports fields include soccer, football, and baseball/softball fields.
April 2008Am J Prev Med 2008;34(4)
in energy expenditure between parks in Hispanic
and African-American neighborhoods. In Chicago, users
of parks in neighborhoods identified as African American
showed the highest energy expenditure (mean?0.087),
followed by parks in Hispanic (mean?0.082) and white
(mean?0.082) neighborhoods (F?6.75, p?0.001). Schef-
fe’s post-hoc tests showed that mean energy expenditure
of park users in African-American neighborhoods was
significantly greater than mean energy expenditure of
park users in Hispanic and white neighborhoods. Signif-
icant differences in energy expenditure were also ob-
served according to neighborhood income (Table 3). In
both cities, greater mean energy expenditure was ob-
association stronger in Chicago (F?10.17, p?0.001) than
Tampa (F?6.44, p?0.011).
Analysis of variance was also used to examine varia-
tion in energy expenditure in neighborhood parks de-
fined jointly by racial/ethnic and income composition
(Table 4). In Tampa parks, differences in energy expen-
diture in parks of different racial/ethnic and income com-
position were statistically significant (F?8.96, p?0.001).
Scheffe’s post-hoc tests indicate that energy expenditure
was greatest in parks in neighborhoods identified as
high-income Hispanic and low-income white, and lowest
in high-income white and low-income Hispanic neighbor-
hoods. Different results were obtained from Chicago
parks. Although energy expenditure in parks of different
racial/ethnic and income composition was statistically
significant (F?10.16, p?0.001), parks in neighborhoods
identified as high-income African-American had higher
energy expenditure than all of the remaining racial/
ethnic–income neighborhood types.
It is important to note that these patterns are influ-
enced by the activity zones where observations of phys-
ical activity occurred. For example, in Tampa parks in
low-income white neighborhoods, 76.8% and 12.4% of
observations were in baseball/softball and open-space
activity areas, respectively. In high-income and His-
panic neighborhoods, 47.5% and 32% of observations
occurred near picnic shelters and playgrounds, respec-
tively. For the remaining subgroups, the percentage of
observations conducted near picnic shelters ranged be-
tween 59% and 69.5%. As detailed below, picnic shelters
were associated with low energy expenditure relative to
other activity zones. In Chicago parks, baseball/softball
fields and open-space areas generated the lowest energy
expenditure values. In low-income white neighbor-
hoods, 64% of observations were in baseball/softball
fields (26%) and open-space areas (38%). In high-
income white neighborhoods, 51% of observations
occurred in baseball/softball fields (40.8%) and open-
space areas (10.1%).
Table 2. Mean energy expenditure per person by racial
and ethnic neighborhood composition
a–cMeans with different superscript are significantly different at
p?0.05 (Scheffe’s post-hoc test).
Table 3. Mean differences in energy expenditure per
person by neighborhood income composition
Table 4. Mean energy expenditure per person in parks
from neighborhoods categorized by income and
Income and racial/
a–bMeans with different superscripts are significantly different at
p?0.05 (Scheffe’s post-hoc test).
cA park for this designation (higher-income and predominantly
African-American) could not be found using our criteria for neigh-
American Journal of Preventive Medicine, Volume 34, Number 4 www.ajpm-online.net
Physical Activity by Activity Zones
How activity zones contributed to physical activity can
be reported in greater detail. The percentages of
people observed walking or engaged in vigorous phys-
ical activity in primary activity spaces in Tampa parks
were as follows: tennis/racquetball courts (75%); bas-
ketball courts (74.4%); open-space areas (45.5%); play-
grounds (44.6%); baseball/softball fields (32.5%); fish-
ing areas (19.3%); and picnic shelters (16.9%). In
Chicago, the percentages of people observed in walking
or vigorous physical activity in primary activity zones were
as follows: tennis/racquetball courts (54.5%); basketball
courts (58.3%); open-space areas (46.8%); playgrounds
(50.9%); and baseball/softball fields (37%).
The ANOVA results of energy expenditure by activity
areas are reported in Table 5. For Tampa parks, the
greatest energy expenditure (0.098) was associated with
racquet sports (tennis and outdoor racquetball) and
basketball courts. Dog play areas (0.057), picnic shel-
ters (0.059), and fishing piers (0.060) were associated
with the lowest energy expenditure. Scheffe’s post-hoc
tests revealed significant separation among the activity
areas. Tennis/racquetball and basketball courts (0.096)
had greater energy expenditure than all other areas.
Interestingly, energy expenditure for baseball/softball
fields was not significantly different from energy expen-
diture documented in picnic, fishing, and dog play
areas. In Chicago parks, less separation in terms of
mean differences was observed among activity zones.
Mean energy expenditure per person on basketball
courts (0.088), playgrounds (0.088), and soccer fields
(0.094) was significantly higher than that observed on
baseball/softball fields (0.074).
Seventy percent of Tampa park users and 51% of
Chicago park users were observed in sedentary activi-
ties. Among Tampa park users, 21% and 8% were
observed in walking and vigorous activity, respectively.
Among Chicago park users, 28% and 22% were ob-
served in walking and vigorous activity, respectively.
These findings are similar to reports from past studies
using observational methods.18,21While many types of
park use, both active and passive, combine to provide
an array of social, economic, and psychological benefits
sought through leisure experience,22the prevalence of
sedentary activity in park settings suggests that there
may be further opportunities to encourage physically
active park use. Public parks are widely available, sub-
ject to public policy influence,8and can promote
population level changes in physical activity,1so studies
like the present one can provide information that leads
to more health-promoting park management policies.
The association between age group and physical
activity was statistically significant in both cities. In
Tampa parks, 44% of children were observed walking
or engaged in vigorous activity compared to 23.2% of
adults. In Chicago parks, 52% of children were ob-
served walking or engaged in vigorous activity versus
47% for adults. This evidence that many children are
getting physical activity by using parks is encouraging.
These results can inform policymakers on the impor-
tance of neighborhood parks as critical community
spaces where children can be physically active. Addi-
tionally, the study provides evidence for improving
facilities conducive to physical activity in existing parks
and creating new parks as places where children can
engage in MVPA.
The ANOVA procedures showed that physical activ-
ity, energy expenditure in particular, varied by neigh-
borhood racial/ethnic and income composition. For
example, in Tampa the highest levels of energy expen-
diture were generated in parks from high-income His-
panic neighborhoods and low-income white neighbor-
hoods. The lowest energy expenditure was associated with
high-income white neighborhoods and low-income His-
panic neighborhoods. These findings reflect the simi-
lar composition of Hispanic and white neighbor-
hoods. In Tampa, block groups with a population
greater than 50% Hispanic were also nearly 50% white.
In Chicago, the greatest energy expenditure was re-
corded from parks in high-income African-American
neighborhoods. In both cities, the association between
activity zones and physical activity appears to underlie
differences by racial/ethnic and income composition.
These physical activity patterns suggest that ethnic and
Table 5. Mean energy expenditure per person in parks by
Dog play areas
Dog play arease
3471 0.059a,b0.020 144.13* 0.127
a–dMeans with different superscript are significantly different at
p?0.05 (Scheffe’s post-hoc test).
eThis result should be interpreted with caution given the small N.
April 2008Am J Prev Med 2008;34(4)
racial groups vary in their use of parks. It was not
possible to establish those differences in the present
study. Further investigation is needed to identify re-
sources and configurations of parks that would most
effectively encourage people to be active in each kind
To put energy expenditure into perspective, a hypo-
thetical 150-pound man who lived in the least-active
neighborhood (African-American neighborhoods in
Tampa), who visited the parks three times a week at 30
minutes per time, and who had the average energy
expenditure would burn 21,379 kcal over the course of
a year. A person living in the most-active neighborhood
type (African-American neighborhoods in Chicago)
and visiting the parks for the same amount of time
would expend 27,760 kcal in a hypothetical year. This is
a difference of 6381 kcal a year, or almost 2 pounds.
Sedentary behavior and lower levels of energy expen-
diture were associated with dog play areas, picnic
shelters, baseball/softball fields, and open-space areas.
MVPA and higher energy expenditure were generated
by the use of soccer fields and playgrounds and by
basketball, tennis/racquetball, and volleyball courts.
Relative differences in physical activity by activity zones
were consistent across cities. While these patterns
would be expected, the present study provides quanti-
tative evidence of how various activity areas within parks
facilitate and constrain physical activity. It also high-
lights the need to consider how activities and facilities
now available in parks located in communities at
greater risk of inactivity and its health consequences
might be redesigned or better programmed to stimulate
physical activity and reduce racial/ethnic and income
inequalities in physical activity. Future research using
more rigorous designs, such as quasi-experimental evalu-
ations of park renovations, can build on these results to
better understand how specific configurations of facil-
ities enhance moderate and vigorous physical activity in
parks. Future studies should also identify the activity and
program preferences of neighborhood residents. Perhaps
moderate and vigorous physical activity in public parks
can be increased in ethnically diverse communities if
programs and facilities and other interventions are “cul-
turally salient and appropriate.”23
The study has several limitations. First, the SOPLAY
observations consist of momentary time sampling,
meaning that each park user’s activity level was assessed
only at one moment and each physical activity category
encompassed a range of intensities.13,24The energy
expenditure measures were based on the SOPLAY
physical activity categories, so they are not precise.
Second, the observations did not represent early morn-
ing, weekday, and seasonal park use. Different patterns
of physical activity could result if broader coverage was
achieved. Third, the neighborhood types in Tampa can
be better described as “mixed” areas where there were
high concentrations of both Hispanic and white resi-
dents. In contrast, Chicago neighborhoods exhibited
greater residential segregation. The study did, however,
present data on how parks located in ethnically diverse
residential areas contribute to physical activity. An addi-
tional strength is that the data were obtained by estab-
lished protocols from 28 parks in two large cities.13,17
The present results underscore the need to better
understand how public parks contribute to physical
activity in diverse communities. Evidence of the extent
of sedentary behavior in parks demonstrates the need
to consider how parks can be designed and managed to
encourage physically active park visits. Although parks
are frequently touted as critical resources for physical
activity, clearly more research is needed to guide man-
agerial decisions and policy. Future studies should
investigate how park infrastructure, amenities, and
programs in activity areas affect physical activity in
diverse communities. Another research priority would
be to evaluate interventions specifically designed to
increase physical activity in activity areas dominated by
This study was supported by a grant from the Robert Wood
Johnson Foundation, Active Living Research.
No financial disclosures were reported by the authors of
1. U.S. DHHS. Healthy People 2010. 2nd edition. www.healthypeople.gov/
2. Gordon-Larsen P, McMurray RG, Popkin BM. Determinants of adolescent
physical activity and inactivity patterns. Pediatrics 2000;105:E83.
3. CDC. Health disparities experienced by racial/ethnic minority popula-
tions. MMWR 2004;53:755–82.
4. U.S. DHHS. Physical activity and health: a report of the Surgeon General.
Atlanta GA: Department of Health and Human Services, CDC, National
Center for Chronic Disease Prevention and Health Promotion, 1996.
5. Sallis JF, Glanz K. The role of built environments in physical activity, eating,
and obesity in childhood. The Future of Children 2006;16:89–108.
6. Sallis JF, Cervero RB, Ascher W, Henderson KA, Kraft MK, Kerr J. An
ecological approach to creating active living communities. Annu Rev Public
7. Godbey GC, Caldwell LL, Floyd M, Payne LL. Contributions of leisure
studies and recreation and park management research to the active living
agenda. Am J Prev Med 2005;28(Suppl 2):150–8.
8. Moody JS, Prochaska JJ, Sallis JF, McKenzie TL, Brown M, Conway TL.
Viability of parks and recreation centers as sites for youth physical activity
promotion. Health Promot Pract 2004;5:438–43.
9. Sallis JF, Bauman MP. Environmental and policy interventions to promote
physical activity. Am J Prev Med 1998;15:379–97.
10. Giles-Corti B, Broomhall MH, Knuiman M, Collins C, Douglas K, Ng K,
et al. Increasing walking: How important is distance to, attractiveness, and
size of public open space? Am J Prev Med 2005;28(Suppl 2):169–76.
11. Cohen DA, Ashwood JS, Scott MM, Overton A, Evenson KR, Staten LK,
et al. Public parks and physical activity among adolescent girls. Pediatrics
12. Godbey GC, Graefe A, James SW. The benefits of local recreation and park
services: a nationwide study of the perceptions of the American public.
Ashburn, VA: National Recreation and Park Association, 1992.
13. McKenzie TL, Cohen DA, Sehgal A, Williamson S, Golinelli D. System for
Observing Play and Recreation in Communities (SOPARC): reliability and
feasibility measures. J Phys Act Health 2006;3(Suppl 1):S208–S222.
14. Powell LM, Slater S, Chaloupka FJ. The relationship between community
physical activity settings and race, ethnicity and socioeconomic status.
Evidence-based Prev Med 2004;1:135–44.
American Journal of Preventive Medicine, Volume 34, Number 4www.ajpm-online.net
15. Wolch J, Wilson JP, Fehrenbach J. Parks and parks funding in Los Angeles:
an equity mapping analysis. Urban Geogr 2005;26:4–35.
16. Gordon-Larsen P, Nelson MC, Page P, Popkin BM. Inequality in the built
environment underlies key health disparities in physical activity and
obesity. Pediatrics 2006;117:417–24.
17. McKenzie TL. System for Observing Play and Leisure Activity in Youth
(SOPLAY). 2002. Available from: URL: http://www-rohan.sdsu.edu/
faculty/sallis/SOPLAYprotocol.pdf. Accessed October 31, 2007.
18. Scruggs P, Beveridge S, Eisenman P, Watson D, Schultz B, Ransdell L.
Quantifying physical activity via pedometry in elementary physical educa-
tion. Med Sci Sports Exerc 2003;35:1065–71.
19. Rowe P, van der Mars H, Schuldheisz J, Fox S. Measuring students’ physical
activity levels: Validating SOFIT for use with high school students. J
Teaching Phys Educ 2004;23:235–51.
20. Landis JR, Koch GG. The measurement of observer agreement for cate-
gorical data. Biometrics 1977;33:159–74.
21. Cohen DA, McKenzie TL, Sehgal A, Williamson S, Golinelli D, Lurie N.
Contribution of public parks to physical activity. Am J Public Health
22. Bedimo-Rung AL, Mowen AJ, Cohen DA. The significance of parks to
physical activity and public health: a conceptual model. Am J Prev Med
23. Yancey AK, Ory MG, Davis SM. Dissemination of physical activity promotion
interventions in underserved populations. Am J Prev Med 2006;31
24. McKenzie TL. The use of direct observation to assess physical activity. In:
Welk G, ed., Physical activity assessments for health-related research.
Champaign, IL: Human Kinetics, 2002:179–95.
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