Abstract
Introduction:
This study compares traditional post-and-platform playgrounds to innovatively designed playgrounds to assess the degree to which design influences use and physical activity. Innovative playgrounds are defined as having (1) a variety of surface types; (2) naturalized and planted areas designed for play; (3) open-ended structures that do not dictate play sequences; (4) loose, movable equipment; and (5) not comprised solely of traditional post-and-platform structures. This study also examines how neighborhood contextual factors contribute to playground use and physical activity.
Methods:
The authors selected 30 traditional and 30 innovative playgrounds in 10 U.S. cities and used the System for Observing Play and Recreation in Communities (SOPARC) to document the number and characteristics of users during 19 hourly observations over 1 week at each playground in Summer 2021. Data were analyzed to identify factors associated with the number of users and the amount of moderate-to-vigorous physical activity (MVPA) within each playground.
Results:
Innovative playgrounds attracted 2.5x more users and generated almost 3x as much MVPA. After controlling for playground size, population density, neighborhood poverty, and destination location, innovative playgrounds still attracted 43% more visitors than traditional playgrounds. While playgrounds in high poverty neighborhoods saw less overall use, innovatively designed playgrounds mitigated the difference by 60% compared to traditional playgrounds.
Conclusions:
Playground characteristics that attract more users and are associated with more MVPA, like design, size, and the number of unique features should be integrated into future playground designs and renovations, with innovative designs prioritized for low-income neighborhoods.
INTRODUCTION
Playgrounds are located in 89% of all urban neighborhood parks in the U.S.,1 establishing them as a rich resource that supports physical activity (PA) among both children and adults.1-3 Caregivers typically chaperone young children at playgrounds, so this setting can help visitors reach the national PA recommendations of at least 60 minutes per day for children and 150 minutes per week for adults.4-6 Given that fewer than half of U.S. children and adults meet national PA guidelines,7 it is critical that leisure time environments are designed to maximize PA. Because physical inactivity has been ranked on par with smoking as a mortality risk factor8 and has been identified as the strongest modifiable risk factor for death from SARS-COV-2,9 innovations that increase PA for adults as well as children are likely to improve population health and well-being.
Playgrounds are not created equal,10,11 and not all are designed to promote PA for all age groups.12,13 Traditional post-and-platform playgrounds typically have at least 1 composite structure of a series of posts with interstitial platforms connected via stairs and ramps. They often contain static elements for climbing, sliding, hanging, and manipulating in prescribed play sequences (e.g., climb up and then slide down on a single surface type). Typically, most are not built to accommodate adult movement.3
In contrast, innovative playgrounds are defined as containing at least 3 of the following characteristics: (1) a variety of surface types; (2) naturalized and planted areas designed specifically for play; (3) open-ended structures that do not dictate play sequences; (4) loose, -movable equipment; and (5) not comprised solely of traditional post-and-platform structures. They are usually designed for multiple age groups, including adults and seniors. This definition was developed in a previous study, The London Study of Playgrounds, which found that compared to traditional U.S. playgrounds, London innovative playgrounds had much greater use, 58% more visit-hours, as well as 38% more children and 129% more adults. Respectively, London playground visitors engaged in 90% and 116% more moderate-to-vigorous physical activity (MVPA) than visitors to traditional U.S. playgrounds.3
While the greater number of visitors and PA at the London innovative playgrounds compared to U.S. playgrounds might be due to cultural differences, it is also possible that the differences could be attributable to design.3 In the past decade, major American cities have begun experimenting with newer, more innovative playground designs.14 These spaces have not yet been rigorously studied to assess their impact on PA for all ages. Using and adapting existing methods of systematic direct observation,15 this study addressed the primary research question of how use and PA in innovative playgrounds compare to traditional post-and-platform designed playgrounds across the U.S.
Because multiple disparities in park and playground use have been documented in previous studies,10,11,16,17 including the National Study of Neighborhood Parks,18 (e.g., more use by females and lower use in low-income neighborhoods), this study also investigated whether these disparities might be mitigated by innovative designs. The study specifically assessed whether innovative playgrounds attracted more users of all ages, reduced gender disparities in playground use and PA levels, which contextual factors impacted playground use and PA levels, and whether innovative designs might mitigate the negative impact of neighborhood poverty on playground use.
METHODS
Study Sample
Three innovatively designed playgrounds were selected using the same definition as in the London Study of Playgrounds3 choosing only those that were built or renovated in the previous 10 years in each of 10 cities and metro areas: Boston, Chicago, Cincinnati, Denver, Houston, Los Angeles, Memphis, New York, San Francisco, and Seattle (N=30). Then each innovative playground was matched with a traditional post-and-platform playground (N=30) that had been built or renovated in the same city in the past 10 years. Figures 1a and 1b provide examples of a traditional and innovative playground. The 2 types of playgrounds were matched by socioeconomic aspects of the surrounding census tract, including neighborhood household poverty level and racial/ethnic demographics.19
Figure 1a, 1b, 1c.
Example of post-and-platform design and innovative playground design and target areas, respectively.
Measures
Each playground was mapped with each feature designated as a target area that would be systematically observed on multiple times throughout 1 week during Summer 2021 (Figure 1c). The number of target areas per playground varied between 32 and 151. In each city 2 data collectors were trained in the granular version of the System for Observing Play and Recreation in Communities (SOPARC),15 in which observers rotated through each target area, counting every person in an area by apparent gender, age group (children, teens, adults, seniors), activity level (sedentary, moderate, vigorous), and apparent race/ethnicity (White, Black, Latino, Asian/other). SOPARC methodology has been shown to be both valid and reliable.20-22 The method is essentially a snapshot observation of individuals, so the level of physical activity recorded reflects the moment of observation. All data collectors were trained and their skills assessed by comparing observations between 2 independent observers, requiring at least 80% agreement on all variables documented.
Observations were conducted on Wednesdays, Fridays, Saturdays, and Sundays. If inclement weather occurred on a Wednesday or Friday, make-up observations were made on any available weekday. Inclement weather on weekend days, however, required make-up observations to be made up on a subsequent weekend, as previous data suggested that playgrounds may be used more on weekends than weekdays.1,18 In each playground, SOPARC observations were conducted 4–5 times per day, for a total of 19 observations per week during different hours between 10:00am and 6:00pm. In addition to recording the characteristics of the individuals, the predominant activity in the target area was recorded (e.g., climbing, sitting, crawling).
Data collectors were rigorously trained to conduct observations. Before making initial observations, they spent one half day at each playground to become familiar with the setting and target areas and to practice all protocols prior to data collection. To confirm compliance with the data collection schedule, observers were required to send time-stamped “selfies” of their arrival at and departure from the playground each day. Data were submitted at the end of each day. All the methods were approved by the RAND IRB.
Statistical Analysis
SOPARC observation data were aggregated to form the analytic dataset, consisting of the counts of observed users in a playground during the planned observation hour by apparent gender, race/ethnicity, age category, and PA levels. The analytic dataset has the typical structure of multivariate longitudinal data: each outcome (e.g., total counts, counts by gender) has repeated measures per playground and there are multiple playgrounds. There were no missing data because all observations were either conducted at planned times or during a make-up period if the weather was inclement.
Descriptive statistics were calculated for all outcomes, which include 2-sample t-tests for unadjusted mean differences between innovative and traditional playgrounds. Next, 2 negative binomial regression models were fitted to estimate the adjusted difference between the innovative and traditional playgrounds in total users and the count of those engaging in MVPA, respectively. Since both outcomes are counts with inflated variances, the negative binomial regression was preferred to the Poisson regression and linear regression. The negative binomial regression has been applied in previous studies based on the SOPARC data.23-27 The negative binomial regression models adjusted for fixed effects considered to be related to playground use, including city, hour of a weekday and hour of a weekend day separately, whether the playground was a destination spot located in a tourist area, population living within a half-mile radius, percent of households living in poverty within a half-mile radius, and playground size. In addition, the number of unique play elements (e.g., spinner, climber, slides) and the number of surface types in each playground (e.g., rubber, mulch, sand) were controlled for. The generalized estimating equation (GEE) technique was applied to adjust for serial correlation among repeated measures within a playground. Finally, a moderation analysis was conducted by including the interaction between local poverty rate and the indicator for innovative playground to test whether the effect of playground innovation varies by income. For simplicity, the parameter estimates of intercept, city, and time of day are not reported in the results.
RESULTS
Table 1 compares the contexts and visitor characteristics of the selected innovative and traditional playgrounds selected for the study. Because the traditional playgrounds were matched on population density and neighborhood poverty levels, no differences on these characteristics were found. The innovative playgrounds were about twice as large and provided an average of 5 more unique features, 2 more surface types, more opportunities for water play (although not statistically significant), more loose equipment, and more seating. Additionally, nearly half of the innovative playgrounds were situated in areas considered “destination” locations (i.e., places where people might visit because of their proximity to tourist attractions/sites).
Table 1.
Descriptive Characteristics of Playgrounds and Playground Users
| Variable | Traditional (n=30) |
Innovative (n=30) |
|---|---|---|
| Playground and neighborhood characteristics, N (range) | ||
| Neighborhood population density (sq. ft.) (1/2 mile) | 16,207 (839–76,937) | 16,224 (411–71,315) |
| % Individuals in poverty (1/2 mile) | 13.7% (3.3%–32.4%) | 13.7% (2.8%–31.2%) |
| % Families in poverty (1/2 mile) | 9.9% (2.1%–26.7%) | 8.7% (1.5%–27.6%) |
| Playground size (sq. ft.) | 23,074 (4,827–59,416) | 46,954*** (13,099–108,089) |
| Number of unique features | 23 (14–33) | 28*** (18–37) |
| Number of surface types | 3 (1–5) | 5**** (2–9) |
| % Destination location | 0.0% | 46.7% **** |
| User characteristics | ||
| Average users observed, n | 309 | 822 *** |
| Average male users, n (%) | 130 (43.6) | 361 (44.9) |
| Male children, n (%) | 76 (57.7) | 212 (58.1) |
| Male teens, n (%) | 5 (5.3) | 12 (3.5) |
| Male adults, n (%) | 45 (31.8) | 130 (36.6) |
| Male seniors, n (%) | 3 (1.9) | 7(1.8) |
| Average female users, n (%) | 179 (53.0) | 461 (55.1) |
| Female children, n (%) | 79 (48.5) | 208 (44.6) |
| Female teens, n (%) | 5 (5.1) | 19 (3.8) |
| Female adults, n (%) | 89 (40.6) | 223 (49.1) ** |
| Female seniors, n (%) | 6 (2.4) | 11 (2.5) |
| Average in sedentary PA, n (%) | 178 (50.5) | 430 (52.0) |
| Average in moderate PA, n (%) | 107 (37.4) | 300 (37.3) |
| Average in vigorous PA, n (%) | 24 (8.8) | 91 (10.7) |
| Average females in MVPA, n (%) | 70 (51.0) | 205 (51.5) |
| Average female children in MVPA, n (%) | 43 (62.9) | 134 (63.3) |
| Average female teens in MVPA, n (%) | 2 (5.0) | 9 (3.9) |
| Average female adults in MVPA, n (%) | 23 (26.8) | 60 (31.4) |
| Average female seniors in MVPA, n (%) | 1 (2.0) | 3 (1.3) |
| Average males in MVPA, n (%) | 61 (45.7) | 187 (48.5) |
| Average male children in MVPA, n (%) | 44 (68.9) | 139 (73.2) |
| Average male teens in MVPA, n (%) | 3 (5.3) | 7 (4.0) |
| Average male adults in MVPA, n (%) | 13 (21.5) | 39 (22.0) |
| Average male seniors in MVPA, n (%) | 1 (1.0) | 2 (1.3) |
Note: Boldface indicates statistical significance (*p<0.05; **p<0.01; ***p<0.001; ****p<0.0001).
PA, physical activity; MVPA, moderate to vigorous physical activity.
A notable difference in the use of the 2 different playground types was the number of visitors observed. There were over 2.5 times as many visitors observed in innovative playgrounds. Even when playground size was considered, there were nearly 31% more users per square foot in innovative playgrounds. The proportion of users by gender and age groupings did not differ between the innovative and traditional playgrounds. Overall, there was a larger proportion of female adults than male adults in both types of playgrounds and there were relatively few teens and few seniors visiting either type. Across the 19 SOPARC observation periods in each playground and summing all the target areas, the average number counted per playground per time period was 16 persons in the traditional playgrounds and 43 in the innovative playgrounds. MVPA occurring in both playground types was commensurate with the number of visitors.
Table 2 shows the results of models predicting both the number of visitors and the amount of MVPA observed in the playground, after controlling for playground size and the contextual factors of population density, neighborhood poverty, and destination location. Innovative playground design remained a strong predictor, accounting for 43.3% more persons and MVPA observed. Playground size was an independent predictor with about a 1% increase in visitors for every 1000 additional sq feet. With the average size of an innovative playground being about 23,000 sq feet larger than the average traditional playground, size contributed to attracting about 23% more visitors and MVPA. The neighborhood population density strongly predicted playground use: for every 10,000 additional people within a ½ mile radius, there were 60% more playground visitors and 47.7% more MVPA. Neighborhood poverty was negatively associated with visitors and MVPA, such that every 1% increase resulted in nearly 5% fewer visitors and 4% less MVPA. Location also has an impact on use and MVPA. Playgrounds not in destination locations had nearly 51% fewer visitors and nearly 52% less MVPA.
Table 2.
Negative Binomial Models Showing Association of Playground and Neighborhood Characteristics With Playground Use and Moderate to Vigorous Physical Activitya
| Contextual variables | Number of observed visitors | Number of observed visitors in MVPA | ||||
|---|---|---|---|---|---|---|
| Estimate | SE | Difference associated with factor, % |
Estimate | SE | Difference associated with factor, % |
|
| Innovative | 0.36 ** | 0.12 | 43.33% | 0.36 ** | 0.13 | 43.33% |
| Traditional | – | – | – | – | – | – |
| Not a destination location | −0.71 **** | 0.13 | −50.84% | −0.73 **** | 0.13 | −51.81% |
| A destination location | – | – | – | – | – | – |
| Population within ½ mile radius | 0.47 **** | 0.07 | 60.00% | 0.39 **** | 0.07 | 47.70% |
| Playground size (1,000 sq ft) | 0.01 ** | 0.00 | 1.01% | 0.01 *** | 0.00 | 1.01% |
| Percent families in poverty | −0.05 **** | 0.01 | −4.88% | −0.04 **** | 0.01 | −3.92% |
| Weekend days | 0.02 | 0.22 | 2.02% | 0.04 | 0.25 | 4.08% |
| Weekdays | – | – | – | – | – | – |
| Number of unique play elements | 0.06 * | 0.03 | 6.18% | 0.06 | 0.03 | 6.18% |
| Number of surface types | 0.05 | 0.04 | 5.13% | 0.05 | 0.04 | 5.13% |
Notes: Boldface indicates statistical significance (*p<0.05; **p<0.01; ***p<0.001; ****p<0.0001).
For simplicity, the parameter estimates of intercept, city, and time of day are not reported in the results.
MVPA, moderate to vigorous physical activity.
The number of unique play features influenced both the number of visitors and total MVPA. For every additional unique play feature (e.g., swings, slides, climbers, water play) there were 6% more visitors and 6% more MVPA. Innovative playgrounds had an average of 5 more unique play features, which accounted for 30% more observed playground visitors. There were no differences in the number of visitors observed on weekends versus weekdays.
Table 3 presents a moderation model that examines the interaction between neighborhood poverty and playground type. The significant term of percent of families in poverty shows a negative association between poverty and an outcome for the traditional playground. When the families in poverty increases by 1%, traditional playgrounds have a mean decrease of use by 6.76% and a decrease of MVPA by 5.82%, adjusting for other covariates. Conceptually, the insignificant term of innovative design shows a lack of difference in an outcome by playground design for a neighborhood with 0% families in poverty. This interpretation is a slight extrapolation since the minimum poverty rate in our data is 2.1%. The significant 2-way interaction term between poverty and playground type indicates that the innovative playground design type can significantly alleviate the relationship with poverty, or alternatively, poverty can significantly moderate the effect of playground design. Compared with traditional playgrounds, innovative playgrounds have a much weaker relationship with poverty. When the families in poverty is increased by 1%, innovative playgrounds have a mean decrease of use by 2.68% and a decrease of MVPA by 1.74%, adjusting for other covariates. The relative difference is 4.08% in use and 4.08% MVPA between playground types, when the families in poverty increases by 1%. In other words, innovative playgrounds reduce the gap in playground use associated with neighborhood poverty by 60% for total number of visitors and 70% for the amount of total MVPA generated.
Table 3.
Negative Binomial Models Showing the Moderation Effect Between Poverty and Playground Typesa
| Model predicting visitors, n | Model predicting MVPA | |||||
|---|---|---|---|---|---|---|
| Contextual variables | Estimate | SE | Difference associated with factor, % |
Estimate | SE | Difference associated with factor, % |
| Innovativeb | −0.04 | 0.20 | −3.92% | 0.01 | 0.21 | 1.01% |
| Traditional | – | – | – | – | – | – |
| Not a “destination” location | −0.71 **** | 0.14 | −50.84% | 0.72 **** | 0.14 | −51.32% |
| Destination location | – | – | – | 0.00 | 0.00 | 0.00% |
| Population in ½ mile radius (10,000) | 0.49 **** | 0.08 | 63.23% | 0.40 **** | 0.08 | 49.18% |
| Playground size (1,000 sq ft) | 0.01 **** | 0.00 | 1.01% | 0.01 **** | 0.00 | 1.01% |
| % families in poverty | −0.07 **** | 0.01 | −6.76% | 0.06 **** | 0.01 | −5.82% |
| Interaction of innovative parks and poverty | 0.04 ** | 0.01 | 4.08% | 0.04 ** | 0.01 | 4.08% |
| Interaction of traditional parks and poverty | – | – | – | – | – | – |
| Weekend days | 0.01 | 0.22 | 1.01% | 0.03 | 0.26 | 3.05% |
| Weekdays | – | – | – | – | – | – |
| Number of unique play elements | 0.06 | 0.03 | 6.18% | 0.05 | 0.03 | 5.13% |
| Number of surface types | 0.04 | 0.04 | 4.08% | 0.05 | 0.04 | 5.13% |
Notes: Boldface indicates statistical significance (*p<0.05; **p<0.01; ***p0.001; ****p<0.0001).
For simplicity, the parameter estimates of intercept, city, and time of day are not reported in the results.
Refers to innovative playgrounds where % families in poverty is zero.
MVPA, moderate to vigorous physical activity.
DISCUSSION
The major finding of this study is the significantly greater use of innovative playgrounds compared to traditional playgrounds, even after controlling for a variety of contextual factors including neighborhood population density, poverty level, and design features such as playground size and number of features. The association of size with greater playground use is of particular importance when the relatively small footprint of playgrounds is compared to overall park features in other studies.10,11,18,28 The average innovative playground studied was about 1 acre, while the traditional playgrounds were about ½ acre; respectively they served an average of 43 and 16 individuals at any given moment. From a public health perspective, the return on investment for larger playgrounds should be noted by city officials and park planners.
The role of population density in promoting greater playground use by 60% suggests that city planners should ensure that playgrounds are available within a ½ mile radius of all residents. In the highest density areas, installing a playground every ¼ mile may be reasonable. Given that health recommendations for children are 60 minutes of MVPA daily and for adults are at least 150 minutes of MVPA per week, having a convenient setting that supports PA for all ages is critical.
The number of unique play features in a playground had an independent role in visitation but did not appear to mediate the greater number of visitors to innovative playgrounds. Because a higher variety of features is associated with higher visitation and MVPA regardless of design, future playground renovations of all types of playgrounds should be considered adding more variety. The number of unique features provides a variety of opportunities to move in different ways (e.g., chances to climb, spin, crawl, balance, jump) as well as to exercise different muscle groups. These options may underlie the greater attraction to variety.
The location of the playground in a tourist area also had a large impact on the number of users and levels of MVPA. Location is important not only for convenience, but also because proximity to other nearby attractions (e.g., a beach, wharf, or downtown area) might provide family members with different preferences an opportunity to combine visits in 1 trip.
Nearly 80% of the adults in the playgrounds were women, which did not vary significantly between innovative and traditional playgrounds. The difference in playground visitation by gender is likely due to women assuming more caretaking roles than men. As this study was completed in the summer of 2021, it is possible that the gender disparities in job losses due to the COVID-19 pandemic influenced the higher prevalence of women in the playground.29,30
Although differences were found in the use of traditional and innovative playgrounds, there were no differences in the demographics or MVPA of the visitors, factors noted when previously comparing London innovative playgrounds to traditional American playgrounds.3 Nevertheless, the increased attraction of innovative playgrounds was replicated in this study.
The current study is based on systematic direct observation by well-trained staff using a validated tool, which eliminate many biases associated with self-report. Although observations may not always be perfect, they have been shown to be reliable and valid,20,21 with any discrepancies not affecting overall findings. Data were not collected during inclement weather, however there were changes in temperature and humidity across the days and weeks which may have differentially influenced attendance at playgrounds and biased the findings. Data were collected during the same weeks across the U.S., even though climate differs across regions. The simultaneous collection of data in both traditional and innovative playgrounds in each of the 10 cities should have eliminated climate differences as a potential confounder.
Limitations
All data were collected during summer 2021 when the COVID-19 pandemic was in play. The degree to which the pandemic affected playground attendance cannot be estimated. Masking mandates and playground closures did differ across the U.S., and the differences in COVID-19 social norms may have biased results, though results are likely similar within each city.
CONCLUSIONS
Of all the associations found with playground use and MVPA, the significant manipulable factors were playground size, innovative design, and the number of unique features. While contextual factors of destination location, poverty, and population density were also significant, these cannot be easily changed. Therefore, the current findings suggest that playground renovations and creations prioritize innovative designs, and maximize the playground footprint and the number of unique features. Because playground design can mitigate the negative impact of neighborhood poverty on playground attendance and MVPA, these designs are likely to have a larger impact in low-income neighborhoods.
ACKNOWLEDGMENTS
This study was supported by NHLBI #R01HL145145. The research presented in this paper is that of the authors and does not reflect the official policy of the NIH. No financial disclosures were reported by the authors of this paper.
Footnotes
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Credit Author Statement
Deborah A. Cohen, Conceptualization, methodology, investigation, writing, supervision, funding acquisition; Meghan Talarowski, Conceptualization, methodology, investigation, writing-- review and editing, supervision; Bing Han, PhD, methodology, validation, formal analysis, writing-- review and editing, Stephanie Williamson, validation, data curation, formal analysis, writing-- review and editing; Emily Galfond, formal analysis, data curation, visualization, writing-- review and editing; Deborah R Young, writing-- review and editing; Sarah Eng, data curation, writing-- review and editing; Thomas L McKenzie, methodology, writing--review and editing.
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