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A natural experiment to assess recess frequency on children’s physical activity in Arizona (U.S.) elementary schools



In the United States, the number of state policies mandating recess in schools has rapidly increased over the past decade; however, few policies specify recess frequency. Informed by an ecological model of physical activity (PA) policy, this study examined and compared total amounts and intensity of PA expended during recess among children attending schools in compliance with Arizona recess policy ARS§ 15–118 mandating 2 + daily recess periods versus not.


PA during recess was measured among grade three children (ages 8–10) in four randomly selected elementary schools (two complying averaging 30 daily recess minutes; two non-complying averaging 15 daily recess minutes) in Maricopa County, Arizona. Group-level PA was assessed by direct observation using the System for Observing Play and Leisure (137 observations). A subset of students (N = 134) from all schools wore ActiGraph GT3X + devices during recess to measure individual PA. General linear mixed effects models were used to analyze the impact of recess frequency on group and individual PA during recess.


Students attending complying schools spent significantly greater proportions of time in moderate-to-vigorous PA (MVPA) based on direct observation (5%) and accelerometry (15%) and less time being sedentary based on accelerometry (14%) during recess. Across the school day, this would equate to 5.1 more MVPA minutes based on systematic direct observation and 9.5 more MVPA minutes based on accelerometry, and 4.1 less minutes being sedentary based on accelerometry if students received two daily 15-minute recess periods compared to one.


Students attending elementary schools implementing 2 + recesses, in accordance with state policy, demonstrated greater MVPA and less sedentary time, providing preliminary evidence that recess frequency is associated with greater PA intensity among children during recess. Schools that adhere to state-level PA policies may provide a more supportive environment for PA, resulting in increased movement among students. Specifying recess frequency should be considered in statewide recess policy.

Peer Review reports


Obesity rates among children in the United States (US) have tripled in the past three decades [1], contributing to increased risks of cardiovascular disease [2], type 2 diabetes [3], and mental health problems such as anxiety and depression [4]. Insufficient physical activity (PA) and increased sedentary behavior are associated with excess weight gain [5] and obesity [2] among children. Urgent action is needed to address high levels of inactivity [6] as more than 75% of US youth do not meet the recommended daily 60 min of moderate-to-vigorous PA (MVPA) [7, 8] and poor mental health among US youth is increasing [9]. Additionally, sedentary time among children is on the rise nationwide [10], further contributing to higher risk of weight gain [11], obesity-related health issues such as cardiometabolic diseases and certain cancers [12], and poor mental health [13].

Schools are a critical source of daily PA due to their accessibility, structure, and systems to support healthy behaviors and health behavior change [14]. Schools are the only setting that reaches nearly all children in the US [15,16,17,18], and most children spend approximately half of their waking hours at school [19]. School-based PA is positively related to children’s cognitive functioning [20] and the mental health of children and adolescents [21]. In elementary schools, physical education, recess, classroom-based PA, and other before- and after-school programs contribute substantially to MVPA accumulation [22,23,24,25]. Among these, recess stands out as the most significant source of PA at school, accounting for up to 44% of all PA during school hours [26] and helping to counter sedentary time [27, 28].

Although the majority of elementary schools in the US (83%) provide students with one daily recess period [29, 30], less than one quarter (21%) provide recess twice a day [31]. In 2019, Arizona (AZ) enacted a statewide law (ARS§ 15–118) requiring all K-5 schools to offer at least two daily recess periods. This legislation represents one of the first state-level policies in the US specifically addressing recess frequency. According to the law, “Each school district and charter school shall provide at least two recess periods during the school day for pupils in kindergarten programs and grades 1 through 5 … a school district or a charter school may count a pupil’s participation in a physical education course during a day as one of that day’s recess periods…” [32]. Duration is not specified in the AZ state law.

Increasing recess frequency offers a cost-effective, accessible, and sustainable opportunity to improve children’s health on a population level and school-based PA policy is widely recognized as one solution to combat global physical inactivity, as a generally small per-capita expense with broad reach [33]. However, there is limited research linking school policy to PA outcomes, and the specific effect of recess frequency on PA remains unknown. There is a need to examine children’s PA in scenarios that differ in recess frequency as some research has suggested a compensation effect where one school PA opportunity replaces another (e.g., increased PA at recess may be associated with decreased PA during physical education) [34]. Additionally, most studies on school-based PA policies rely on school staff-reported measures [35], rather than objective assessments [36, 37]. Understanding the optimal frequency of recess can guide consistent policy recommendations, given the significant variation in current recess legislation and practices [38].

This cross-sectional natural experiment is part of a larger longitudinal investigation of PA policies and practices in AZ elementary schools. The larger project is framed on an adapted ecological model of PA policy [19] to assess the implementation and dissemination stages of ARS§ 15–118 affecting children’s PA (see model in Appendix 1). The purpose of this study was to gather preliminary results on the impact of recess frequency on individual and group measured PA, including MVPA, light physical activity (LPA), and sedentary behavior (SB) during recess. Given the existing literature reporting differences in PA levels between boys and girls [39, 40], differences in both individual and group PA between sexes were also assessed.


Study design

This study used a cross-sectional quasi-experimental design to compare the impact of recess frequency on objectively measured individual and group-level PA between students attending schools in compliance with recess policy ARS§ 15–118 (2 + daily recesses) and those not (< 2 daily recesses). Four elementary schools were randomly selected to represent policy compliance and non-compliance based on data from a larger longitudinal survey that assessed reported PA policies and practices throughout Arizona [41]. The survey, administered online to school administrators and teachers each fall, collected information on school-, district-, and state-level policies, as well as PA practices among third-grade students (representing the average elementary grade). Further details on the survey and administration can be found in other publications [41, 42].

Participants and recruitment

Of the 171 schools that responded to the fall 2021 survey conducted among administrators and teachers, 60% offered two or more recesses, while 14% offered less than two recesses for grade three students. Approximately 26% of respondents did not know or did not respond to the question regarding recess frequency. For the current study, two complying and non-complying schools were randomly selected from each category using a random number generator function in Microsoft Excel. Schools were eligible if they: [1] met one of the two frequency categories [2], were a public school located within Maricopa County, the largest county in AZ, where 62% of the state population resides [3, 43], and served K-5 (including K-6/8/12) students. The research team contacted schools through phone calls and emails, providing information about the study and inviting them to participate. Principals who agreed to participate were offered a $50 gift card as an incentive for assisting with recruitment of third grade teachers and students. All third-grade children at participating schools were observed during recess time and invited to wear accelerometers. Children who returned signed parental consent (40% response rate) participated in device-based data collection with accelerometers during recess time. Third-grade teachers received a $50 gift card for help coordinating data collection. Third-grade students who wore an accelerometer during recess received a $10 gift card. Ethical approval was granted from Arizona State University, and written parent consent along with child assent were obtained.

Data collection

Members of the research team visited third grade classrooms of the participating schools to confirm school schedules with teachers, place devices on child participants, and conduct observational assessments of one or both recess sessions. To mitigate the impact of seasonal variations, data collection took place during warm months (April, May, October 2022) with daily temperatures similar across time points and conducive to outdoor play (average 86 °F/30°C).

Group-Level Physical Activity. Direct observations to measure mean group-levels of PA during recess were conducted using the System for Observing Play and Leisure Activity in Youth (SOPLAY) [44]. SOPLAY is a validated systematic observation tool used extensively to measure group-levels of PA during recess and provides for the additional collection of contextual environmental supports such as temperature, presence of organized activity, and equipment. Direct observation of contextual variables produces objective information with strong internal (face) validity [45] that have been shown to affect movement at recess [46,47,48,49,50,51,52]. A minimum of two observers collected observations at each school site over three to four school days. Observers were trained using established training protocols that include using standardized online videos and field-based practice until an acceptable reliability of > 80% was met [44]. Maps of each school playground were obtained using aerial images from Google Maps. Playgrounds were divided into smaller target zones for observation. Prior to data collection, observers visited each playground to verify the map and target zones and establish an observation rotation schedule.

During observations, observers systematically recorded environmental supports during recess and categorized students as engaging in vigorous (representing moderate-to-vigorous; MVPA), walking (representing light physical activity; LPA), or sedentary behavior (SB) using established procedures and momentary time sampling techniques [44]. Data were recorded on paper SOPLAY coding sheets attached to clipboards with three-unit counters. Observers conducted visual scans from the left to right, pausing for one second for each child, and tallied activity levels by pressing down on the counters. Separate sweeps were conducted for girls and boys as differences in PA and SB at recess exist between sex, with boys generally engaged in more MVPA compared to girls [53]. Observers systematically rotated throughout established target zones to record activity throughout the entire play area.

Individual Physical Activity. A sample of third grade students at the four schools wore ActiGraph GT3X + accelerometer devices (100 Hz; ActiGraph LLC, Pensacola, US), the most commonly used accelerometers in PA research [54], on their dominant wrist for a minimum of five school days. The wrist location was selected to promote greater wear compliance compared to the hip among children [55]. The research team visited each participating classroom to instruct children and teachers on how to wear the devices. Classroom teachers were given an instructional FAQ and asked to record recess times and relevant information (e.g., student absences) on a daily report. Child sex was recorded by either researchers or classroom teachers. Participants received their device to wear from their teachers upon classroom arrival and removed them at the end of each school day.

Data analysis

Descriptive statistics (means and standard deviations) were conducted to describe the measures of central tendency and dispersion of observational and device-based PA. Proportions of MVPA, LPA, SB were converted to minutes based on a 15-minute recess period, the most common recess length among our sample. Recess at one school (school 4) was 20 min, so PA amounts were adjusted to represent a 15-minute time block for comparison.

Group-Level Physical Activity. Mean observation counts derived from direct observation of all third-grade students at participating schools for each PA level were averaged across days and by sex.

Individual Physical Activity. We followed recommendations of the National Cancer Institute to promote consistent and standard reporting for accelerometer studies [56], except for minimum wear time since 24-hour measurements were not collected as this study focused only on movement during recess. Choi cut points were used to determine a standard wear time recommended in accelerometer studies with youth [57]. Students with at least one recess of valid wear time were included in the analyses, and non-wear times were excluded from analyses. Recess segments were defined based on school bell schedules. Crouter linear regression vector magnitude cut-points for youth [58] were used to determine levels of PA overall, by school, by gender, and by recess frequency. The device data was processed using ActiLife software (v6.13.4).

Covariates. The presence of environmental supports for PA (e.g., presence of equipment, organized activity, temperature) collected during observations as well as school-level income and child sex were controlled for in statistical analyses. Presence of equipment and organized activity (binary) were averaged to produce a scalar variable ranging from 0 (never) to 1 (always). Income was a continuous variable based on the percentage of students eligible for free and reduced-price lunch using 2021–2022 data from the National Center for Education Statistics [59].

Data analysis

Because student participants were nested within schools, and school settings influence health behaviors [60, 61], multilevel modeling was used to assess the impact of recess frequency on movement during recess (MVPA, LPA, and SB). To examine mean group movement assessed by direct observation and individual movement assessed by accelerometry, general linear mixed effects models (GLMM) with Bonferroni post hoc comparisons were utilized to test the association between recess frequency, taking into account the clustering of students within the same school for the individual data. Separate GLMMs (MVPA, LPA, SB) with covariates described below were fit to both the group and individual PA data.

In the GLMMs examining group PA (SOPLAY observations), recess frequency, sex and the interaction between frequency and sex were also entered as main predictors with school income, organized activity, presence of equipment, and temperature as covariates. For models examining individual PA (accelerometry), recess frequency, sex, and the interaction between frequency and sex were entered as primary predictors with income included as a covariate. Data from students at schools with 2 + recesses were averaged prior to running the GLMMs. Analyses were also conducted without averaging (i.e., two data points for these students); however, we found no changes in statistical outcomes. All models were estimated using a maximum likelihood estimator and significance level of 0.05 using SPSS Statistics for Windows, version 26 (IBM Corp.) [62].


The average enrollment of the four schools was 739 students, with an approximately even proportion of boys (50%) and girls (50%). Participating schools averaged 86 third grade students, comparable to the average Maricopa County school enrollment of 99 students in grade three [63]. An average of 36 devices were worn per school (142 total, representing 42% of third grade students at all schools). School-level income varied with two schools classified as low-poverty, one as mid-low poverty, and one considered high poverty (Table 1).

Recess length at the non-complying schools was 15 or 20 min, and 15 min at complying schools. On average, children at non-complying schools spent 17.5 daily minutes at recess and children at complying schools spent 30 daily minutes at recess. Equipment was present during recess less than half of the time during observations (46%), while the frequency of organized activities were generally low (17%) across schools during observations. Descriptive information about participating schools and their recess environments are shown in Table 1.

Table 1 School-level demographic information and recess characteristics

Group-level physical activity

A total of 137 SOPLAY scans were conducted over 22 recess periods at the four schools. Descriptive results indicated that, on average across schools, most time at recess was spent in LPA (M = 43%, SD = 7%). When examined by policy compliance category, high frequency schools averaged greater MVPA (29% v. 24%). When converted to minutes (per 15 min), students at high frequency schools engaged in 4.39 min compared to low frequency schools where students engaged in 3.6 min of MVPA per recess. This equates to 8.8 MVPA minutes per day, or an additional 5.1 min per day of MVPA among students attending schools offering two recess periods compared to one. Full results by recess frequency for observational data are shown in Table 2. Results of direct observations at individual schools can be found in Appendix 2.

Table 2 Mean Recess Physical Activity Levels by Recess Frequency: Direct Observation

Three separate GLMMs were conducted to determine the impact of recess frequency on observed MVPA, LPA, and SB. Covariates included temperature, organized activity, equipment, SLI.

In the MVPA model, recess frequency, t [40]=-3.95, SE = 4.54, P < .01; sex, t [40] = 4.02, SE = 1.82, P < .001, and the interaction between frequency and sex, t [40] = 2.31, SE = 3.63, P < .05, were significant predictors. On average across all schools, students with one daily recess engaged in significantly less MVPA per recess than students at schools with two daily recesses (b=-17.91, 95%CI [-27.08, -8.73]). Boys engaged in a significantly higher proportion of MVPA per recess than girls regardless of frequency (b = 7.29, 95%CI [3.62, 10.96]). When examined by sex, the significantly lower proportion of MVPA per recess at schools offering one daily recess persisted for both boys, F [40] = 4.40, P < .05, and girls, F [40] = 15.56, P < .001.

In the LPA model, recess frequency was a significant predictor, t [40] = 2.62, SE = 5.21, P < .05, while sex and the interaction between frequency and sex were not. On average across all schools, students with one daily recess engaged in significantly more LPA per recess than students at schools with two daily recesses (b = 13.66, 95%CI [3.12, 24.19]).

In the SB model, sex was a significant predictor, t [40]=-5.32, SE = 2.16, P < .001, while recess frequency and the interaction between frequency and gender were not. On average across all schools, boys engaged in a lower proportion of SB than girls per recess (b=-11.48, 95%CI [-15.84, -7.12]).

Individual physical activity

A total of 142 third grade students wore accelerometers to measure individual PA during recess (ranging from 28 to 42 per school; Table 1); however, data from 134 students were included in analysis after removing those that did not wear the device for a complete recess period (n = 8). Descriptive results indicated that, on average across all four schools (six recess periods), most time at recess was spent in MVPA (M = 41%), followed by time in LPA (M = 36%) then SB (M = 23%). When examined by frequency, students at high frequency schools spent less time in SB (17%) compared to low frequency schools (31%), similar time in LPA (35% v. 37%), and greater time in MVPA (48%) compared to low frequency schools (33%). When converted to minutes (per 15 min), students at low frequency schools engaged in 4.9 min compared to 7.2 min for students at high frequency schools per recess. When doubled (to represent high frequency schools with two 15-minute recess periods), this equated to 14.4 MVPA minutes per day. This translates to an additional 9.5 min per day of MVPA for students attending schools with two recess periods compared to one recess. Full results by frequency for device-based (i.e., accelerometry) data are shown in Table 3. Full descriptive information of accelerometer data for each school are shown in Appendix 3.

Table 3 Mean Recess Physical Activity Levels by Recess Frequency: Accelerometry

Three separate GLMMs were conducted to determine the impact of recess frequency on device-based MVPA, LPA, and SB with school income included as a covariate.

In the MVPA model, frequency, t(134)=-10.83, SE = 1.90, P < .001 and sex, t(134) = 3.90, SE = 1.89, P < .001 were significant predictors while the interaction between frequency and sex was not. On average across all schools, students with one daily recess engaged in significantly less MVPA per recess than students at schools with two daily recesses (b=-20.58, 95%CI [-24.34, -16.82]). Boys engaged in a significantly higher proportion of MVPA per recess than girls regardless of frequency (b = 7.39, 95%CI [3.65, 11.14]).

In the LPA model, frequency, t(134) = 6.36, SE = 1.18, P < .001, and sex, t(134)=-3.07, SE = 1.17, P < .01, were significant predictors while the interaction between frequency and sex was not. On average across all schools, students with one daily recess engaged in significantly more LPA per recess than students at schools with two daily recesses (b = 7.48, 95%CI [5.15, 9.80]). Across all recesses, boys engaged in a lower proportion of LPA than girls during recess (b=-3.60, 95%CI [-5.91, -1.28]).

In the SB model, frequency, t(134) = 8.15, SE = 1.61, P < .001, and sex, t(134)=-2.37, SE = 1.60, P < .05, were significant predictors while the interaction between frequency and sex was not. Students at schools with one daily recess engaged in greater proportions of SB per recess than those at schools with two or more daily recesses (b = 13.08, 95%CI [9.92, 16.27]). On average across all schools, boys engaged in a lower proportion of SB than girls per recess (b=-3.80, 95%CI [-6.96, − 0.63]).


The purpose of this observational study was to assess differences in children’s PA among AZ elementary schools with two or more daily recess periods, compared to those with less, in accordance with state policy ARS§ 15–118. Both group-level (observational) and individual-level (device-based) MVPA, LPA, and SB during recess were compared between schools and also between boys and girls. We found that children attending schools implementing two or more daily 15-minute recesses in accordance with ARS§ 15–118 spent an additional 5.1 (based on direct observation) and 9.5 (based on device-based accelerometry) minutes in MVPA, and 4.1 less minutes being sedentary (based on device-based accelerometry) during recess per day compared to children attending schools with one 15-minute recess period.

Comparisons to prior studies

Our results, using both observational (group-level) and device-based (individual) data, show that children attending schools offering two or more daily recesses had greater proportions of MVPA during recess compared to schools offering less than two daily recesses. Previous studies have demonstrated that additional recess periods throughout the school day contribute to higher odds of meeting the recommended 60 min of daily PA for children [65] and improve individual levels of MVPA and overall step count [66]. One recent study of National Youth Fitness Surveillance data showed that children with the highest level of parent-reported recess provision (e.g., 30 or more daily recess minutes), had two-fold higher odds of meeting national standards of 60 min daily of MVPA measured using accelerometry, compared to children with the lowest recess provision [65]. In our study, children’s MVPA at high-frequency schools was greater during both recess periods compared to MVPA levels at low-frequency schools, countering the idea that children compensate by engaging in higher levels of PA when not offered additional opportunities to be active. Similar research has been reported in the physical education literature where children’s PA levels are significantly higher on physical education days than non-physical education days [67,68,69,70], suggesting that greater levels of PA during physical education and recess may be indicative of habitual PA among children [69] and higher recess frequency may be an indicator of an overall supportive culture of PA within a school, rather than contributing to a compensatory PA effect.

Our device-based (individual-level) results indicate that children attending schools with two or more daily recesses spend less time sedentary compared to schools with one daily recess, an important finding considering that sedentary time among children at recess is associated with poor fitness [71]. Children who spend more than 15 min of recess time being sedentary are more than 40 times more likely to have unhealthy levels of cardiorespiratory fitness compared to children who spend less than 15 recess minutes sedentary [72]. Results of both device-based and observational data sources indicated that children at schools with one recess engaged in more light PA compared to those at schools with two recess periods. Although these differences were slight (average time in light PA was less than one minute different between schools with high and low recess frequency), light PA is an important health behavior and associated with reduced disease risk including decreased inflammation [73] and excess adiposity [74] among children. Also, while not a focus of our study, increasing opportunities for PA through increased recess frequency may also help improve physical and social health outcomes [30]. Our findings that boys engaged in significantly more MVPA and less SB than girls is consistent with literature reviews reporting that boys are more physically active compared to girls at recess [75].

Individual vs. group physical activity

Direct observation and accelerometry, both considered forms of objective PA measurement, were used concurrently in the present study. Direct observation is regarded as the gold standard method for assessing PA at recess because it involves in-person observation of behavior [76] and allows for the assessment of environmental support factors during recess (e.g., presence of equipment, organized activity) that can often only be assessed with physical presence [76]. Accelerometry is a commonly used measure for school-aged children and allows for accurate, device-based, and continuous monitoring of PA at the individual level [77]. Results from direct observation and accelerometry often do not agree with each other [78]. In our study, individual proportions of activity levels during recess between direct observation and accelerometry differed (e.g., overall MVPA from direct observation = 28% v. accelerometry = 41%), resulting in some variations between the estimated number of minutes spent in each activity level during a 15-minute recess period. To enhance wear compliance, the accelerometers were positioned on the wrist. Studies have shown that wrist-worn accelerometers can lead to an overestimation of MVPA [79]. This overestimation is attributed to the devices capturing arm movements, which may not accurately reflect whole-body physical activity [80]. Furthermore, the dynamic and sporadic movements of children’s activities may be inaccurately classified as MVPA. The device’s challenge in distinguishing between various movement types contributes to an overall exaggeration of PA intensity [81]. Consequently, this accelerometry-induced overestimation may contribute to observed disparities between direct observation and accelerometer-derived data.

However, the overall trend of our results from the regression analyses were similar between direct observation and accelerometry, due to the following possible reasons: (1) the duration of the recess periods were relatively short, (2) the trained observers coded the activity intensity accurately, (3) compliance with accelerometer wearing was high, and (4) the accelerometer data processing was appropriate.

Policy implications

Supporting the larger project’s adapted ecological model of PA policy, the current study showed the importance of state policy, school environment (e.g., environmental support), and individual factors (e.g., student demographics, such as gender) that support student physical activity and health at school [41]. The number of states with laws mandating recess in schools has increased over the past decade in the United States, with 12 states currently mandating recess explicitly and eight states with active proposed legislation in support of recess as confirmed by all state government websites reporting on school PA laws at the time of publication in 2023. As with school-based PA laws, however, the lone existence of a policy does not equate to adherence [19, 33] and current recess legislation and practices vary widely [38]. For example, school-based PA policies that are more comprehensive and include specific language (e.g., prohibit withholding) are positively related to children’s PA at recess [41].

Our results support the positive health impact of considering frequency in recess policy, and recommending at least 30 min of recess per day. In the case of this research in Arizona, policy-compliant schools offered 30-minutes of daily recess, which may be a good target for other states. Current US national recommendations for schools to implement 20 min of recess [82, 83] may be overly restrictive, considering the literature base supporting an array of benefits of more recess [83, 84], including children’s overall PA and body mass index [66, 85, 86]. However, future studies should investigate different recess durations to determine optimum length for health gains while also considering preferences of school staff and competing demands from other academic classes and content areas during the school day.

This study is not without limitations. Although our sample was randomly selected, included a diverse range of income levels, and was focused on schools in a large county (over 60% of the AZ population resides in Maricopa County); [87] the sample size of four schools was small and may not be representative of all schools, particularly those in rural areas. Additionally, although our sample size of students in grade three is similar to schools in Maricopa County, Arizona generally has higher average school enrollment and diverse racial and ethnic populations compared to other elementary schools in the United States [59]. Our use of both observations and device-based assessment provided a more comprehensive battery of measures compared to one technique alone; however, comparisons should be made with caution as observational data included average activity levels among all students across multiple recess observations whereas device data included averages among individuals across all eligible recess periods. During systematic observations, researchers made judgements about child sex based on visual characteristics. Although this aligns with current practice, we note that this strategy may be biased as researchers were not aware of the actual sex of children observed in the study and made assumptions based on gendered perceptions. We also note that our study was cross-sectional so causal conclusions cannot be assumed.


Schools are ideal spaces for targeted behavior change and population health improvement due to their accessible systems of support and service for children. Our study supports the positive impact of two daily recess periods on children’s moderate-to-vigorous physical activity and sedentary behavior. Adding an additional recess period to the school day may 15 h of MVPA for children throughout the school year. Considering language that specifies frequency, and potentially duration, in state-level policies can support widespread benefits for children’s health.

Data availability

The datasets supporting the conclusions of this article are available in the ASU Library Research Data Repository [].





Physical activity


Moderate to vigorous physical activity


Light physical activity


Sedentary behavior


System for Observing Play and Leisure Activity in Youth


General linear mixed effects models


  1. Fryar CD, Carroll MD, Ogden CL. Prevalence of overweight, obesity, and severe obesity among children and adolescents aged 2–19 years: United States, 1963–1965 through 2015–2016. 2018; Available from:

  2. Cote AT, Harris KC, Panagiotopoulos C, Sandor GGS, Devlin AM. Childhood obesity and cardiovascular dysfunction. J Am Coll Cardiol. 2013;62(15):1309–19.

    Article  PubMed  Google Scholar 

  3. Bacha F, Gidding SS. Cardiac abnormalities in youth with obesity and type 2 Diabetes. Curr Diab Rep. 2016;16(7):62.

    Article  CAS  PubMed  Google Scholar 

  4. Halfon N, Larson K, Slusser W. Associations between obesity and comorbid mental health, developmental, and physical health conditions in a nationally representative sample of US children aged 10 to 17. Acad Pediatr. 2013;13(1):6–13.

    Article  PubMed  Google Scholar 

  5. Remmers T, Sleddens EFC, Gubbels JS, de Vries SI, Mommers M, Penders J, et al. Relationship between physical activity and the development of body mass index in children. Med Sci Sports Exerc. 2014;46(1):177–84.

    Article  PubMed  Google Scholar 

  6. Woods CB, Volf K, Kelly L, Casey B, Gelius P, Messing S, et al. The evidence for the impact of policy on physical activity outcomes within the school setting: a systematic review. J Sport Health Sci. 2021;10(3):263–76.

    Article  PubMed  PubMed Central  Google Scholar 

  7. National Physical Activity Plan Alliance. The 2018 United States Report Card on Physical Activity for Children and Youth [Internet]. Washington, D.C.: National Physical Activity Plan Alliance. ; 2018. Available from:

  8. Centers for Disease Control and Prevention (CDC). Physical activity facts [Fact sheet] [Internet]. 2020 [cited 2021 Apr 23]. Available from:

  9. Centers for Disease Control and Prevention and Human Services. Youth Risk Behavior Survey Data Summary & Trends Report: 2011–2021. [Internet]: U.S. Department of Health and Human Services. 2023 [cited 2023 November 23]. Available from: C%20more%20than%204,10%20(10%25)%20attempted%20suicide.

  10. Kohl HW, Craig CL, Lambert EV, Inoue S, Alkandari JR, Leetongin G, et al. The pandemic of Physical Inactivity: global action for public health. The Lancet. 2012;380(9838):294–305.

    Article  Google Scholar 

  11. Mitchell JA, Pate RR, Beets MW, Nader PR. Time spent in sedentary behavior and changes in childhood BMI: a longitudinal study from ages 9 to 15 years. Int J Obes. 2013;37(1):54–60.

    Article  CAS  Google Scholar 

  12. Cook S, Auinger P, Li C, Ford ES. Metabolic syndrome rates in United States adolescents, from the National Health and Nutrition Examination Survey, 1999–2002. J Pediatr. 2008;152(2):165–170e2.

    Article  PubMed  Google Scholar 

  13. Biddle SJH, Asare M. Physical activity and mental health in children and adolescents: a review of reviews. Br J Sports Med. 2011;45(11):886–95.

    Article  PubMed  Google Scholar 

  14. Perry C, Parcel G, Stone E, Nader P, McKinlay S, Luepker RV, et al. The child and Adolescent Trial for Cardiovascular Health (CATCH): overview of the intervention program and evaluation methods. Cardiovasc Risk Factors. 1992;2(1):36–44.

    Google Scholar 

  15. Pate RR, Davis MG, Robinson TN, Stone EJ, McKenzie TL, Young JC. Promoting physical activity in children and youth: a leadership role for schools: a scientific statement from the American Heart Association Council on Nutrition, Physical Activity, and metabolism (Physical Activity Committee) in collaboration with the councils on Cardiovascular Disease in the Young and Cardiovascular nursing. Circulation. 2006;114(11):1214–24.

    Article  PubMed  Google Scholar 

  16. Sallis J, Bauman A, Pratt M. Environmental and policy interventions to promote physical activity. Am J Prev Med. 1998;15(4):379–97.

    Article  CAS  PubMed  Google Scholar 

  17. Sallis JF, McKenzie TL, Conway TL, Elder JP, Prochaska JJ, Brown M, et al. Environmental interventions for eating and physical activity: a randomized controlled trial in middle schools. Am J Prev Med. 2003;24(3):209–17.

    Article  PubMed  Google Scholar 

  18. Story M. School-based approaches for preventing and treating obesity. Int J Obes. 1999;23(S2):43–51.

    Article  Google Scholar 

  19. Lounsbery MA, McKenzie TL, Morrow SM Jr, Holt KA. District and school physical education policies: implications for physical education and recess time. Ann Behav Med. 2013;45(suppl1):131–41.

    Article  Google Scholar 

  20. Donnelly JE, Hillman CH, Castelli D, Etnier JL, Lee S, Tomporowski P, Lambourne K, Szabo-Reed AN. Physical activity, fitness, cognitive function, and academic achievement in children: a systematic review. Med Sci Sports Exerc. 2016;48(6):1197–222.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Andermo S, Hallgren M, Nguyen TTD, Jonsson S, Petersen S, Friberg M, Romqvist A, Stubbs B, Elinder LS. School-related physical activity interventions and mental health among children: a systematic review and meta-analysis. Sports Med Open. 2020;6(1):25.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Lounsberry M, McKenzie TL, Morrow J, Holt KA. School Physical Activity Policy Assessment (S-PAPA) [Internet]. 2012 [cited 2023 Jan 29]. Available from:

  23. Sallis JF, McKenzie TL, Beets MW, Beighle A, Erwin H, Lee S. Physical education’s role in public health: steps forward and backward over 20 years and HOPE for the future. Res Q Exerc Sport. 2012;83(2):125–35.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Story M, Nanney MS, Schwartz MB. Schools and obesity prevention: creating school environments and policies to promote healthy eating and physical activity. Milbank Q. 2009;87(1):71–100.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Payne VG, Morrow JR. School physical education as a viable change agent to increase youth physical activity. Pres Counc Phys Fit Sports Res Dig. 2009;10(2):1–8.

    Google Scholar 

  26. Erwin H, Abel M, Beighle A, Noland MP, Worley B, Riggs R. The contribution of recess to children’s school-day physical activity. J Phys Act Health. 2012;9(3):442–8.

    Article  PubMed  Google Scholar 

  27. Guinhouya BC, Lemdani M, Apété GK, Durocher A, Vilhelm C, Hubert H. How school time physical activity is the big one for daily activity among schoolchildren: a semi-experimental approach. J Phys Act Health. 2009;6(4):510–9.

    Article  PubMed  Google Scholar 

  28. Ridgers ND, Stratton G, Fairclough SJ. Assessing physical activity during recess using accelerometry. Prev Med. 2005;41(1):102–7.

    Article  PubMed  Google Scholar 

  29. Lee SM, Burgeson CR, Fulton JE, Spain CG. Physical education and physical activity: results from the School Health policies and Programs Study 2006. J Sch Health. 2007;77(8):435–63.

    Article  PubMed  Google Scholar 

  30. Clevenger KA, Perna FM, Moser RP, Berrigan D. Associations between state laws governing recess policy with children’s physical activity and health. J Sch Health. 2022;92(10):976–86.

    Article  PubMed  Google Scholar 

  31. Parsad B, Lewis L. Calories in, calories out: Food and exercise in public elementary schools, 2005. ED TAB. NCES 2006-057. Natl Cent Educ Stat [Internet]. 2006 [cited 2023 Jan 29]. Available from

  32. Arizona State Legislature. ARS 15–118 [Internet]. 2019. Available from:

  33. An R. Policy and physical activity. J Sport Health Sci. 2021;10(3):253–4.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Marks J, Barnett LM, Strugnell C, Allender S. Changing from primary to secondary school highlights opportunities for school environment interventions aiming to increase physical activity and reduce sedentary behaviour: a longitudinal cohort study. Int J Behav Nutr Phys Act. 2015;12(1):1–10.

    Article  Google Scholar 

  35. Barroso CS, Kelder SH, Springer AE, Smith CL, Ranjit N, Ledingham C et al. Senate Bill 42: Implementation and impact on physical activity in middle schools. J Adolesc Health. 2009;45(3):S82–90.

  36. An R, Liu J, Liu R. State laws governing school physical education in relation to attendance and physical activity among students in the USA: a systematic review and meta-analysis. J Sport Health Sci. 2021;10(3):277–87.

    Article  PubMed  Google Scholar 

  37. Robertson-Wilson JE, Dargavel MD, Bryden PJ, Giles-Corti B. Physical activity policies and legislation in schools. Am J Prev Med. 2012;43(6):643–9.

    Article  PubMed  Google Scholar 

  38. Jarrett OS. US Play Coalition [Internet]. 2019. A research-based case for recess: Position paper. Available from:

  39. Powell E, Woodfield LA, Nevill AAM. Children’s physical activity levels during primary school break times: a quantitative and qualitative research design. Eur Phys Educ Rev. 2016;22(1):82–98.

    Article  Google Scholar 

  40. Trost SG, Pate RR, Sallis JF, Freedson PS, Taylor WC, Dowda M, Sirard J. Age and gender differences in objectively measured physical activity in youth. Med Sci Sports Exerc. 2002;34(2):350–5.

    Article  PubMed  Google Scholar 

  41. Nam K, Wilson K, Schulke M, Kulinna PH, Poulos A. The relationship between policy strength and physical activity practices in Arizona public elementary schools. J Phys Act Health. 2023;1–9.

  42. Griffo J, Nam K, van der Mars H, Kulinna P, Ross A. What recess policy? Assessing Arizona schools’ adherence to Senate Bill 1083. J Teach Phys Educ. 2022;1–7.

  43. U.S. Census Bureau. Quick Facts; Arizona and Maricopa County Arizona [Internet]. 2023. Available from:,maricopacountyarizona/PST045222

  44. McKenzie TL. SOPLAY: System for Observing Play and Leisure Activity in Youth [Internet]. 2006. Available from:

  45. McKenzie TL, van der Mars H. Top 10 research questions related to assessing physical activity and its contexts using systematic observation. Res Q Exerc Sport. 2015;86(1):13–29.

    Article  PubMed  Google Scholar 

  46. McKenzie TL, Crespo NC, Baquero B, Elder JP. Leisure-time physical activity in elementary schools: analysis of contextual conditions. J Sch Health. 2010;80(10):470–7.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Drijvers H, Seghers J, van der Mars H, Iserbyt P. Student participation in physical activity recess programs in secondary schools. Int J Kinesiol High Educ. 2022;6(4):212–24.

    Article  Google Scholar 

  48. Kercood S, Sallis JF, Conway TL, McKenzie TL, Prochaska JJ, Moody JS, et al. School physical and social environment changes in relation to physical activity in middle school. Health Behav Policy Rev. 2015;2(3):171–81.

    Article  Google Scholar 

  49. Zask A, van Beurden E, Barnett L, Brooks LO, Dietrich UC. Active school playgrounds—myth or reality? Results of the move it groove it project. Prev Med. 2001;33(5):402–8.

    Article  CAS  PubMed  Google Scholar 

  50. Verstraete SJM, Cardon GM, De Clercq DLR, De Bourdeaudhuij IMM. Increasing children’s physical activity levels during recess periods in elementary schools: the effects of providing game equipment. Eur J Public Health. 2006;16(4):415–9.

    Article  PubMed  Google Scholar 

  51. Beighle A. Increasing physical activity through recess. Research Brief. Robert Wood Johns Found [Internet]. 2012; Available from:

  52. Behrens TK, Holeva-Eklund WM, Luna C, Carpenter D, Tucker E, Field J, et al. An evaluation of an unstructured and structured approach to increasing recess physical activity. J Sch Health. 2019;89(8):636–42.

    Article  PubMed  Google Scholar 

  53. McLellan G, Arthur R, Donnelly S, Buchan DS. Segmented sedentary time and physical activity patterns throughout the week from wrist-worn ActiGraph GT3X + accelerometers among children 7–12 years old. J Sport Health Sci. 2020;9(2):179–88.

    Article  PubMed  Google Scholar 

  54. de Vries SI, Bakker I, Hopman-Rock M, Hirasing RA, van Mechelen W. Clinimetric review of motion sensors in children and adolescents. J Clin Epidemiol. 2006;59(7):670–80.

    Article  PubMed  Google Scholar 

  55. Scott JJ, Rowlands AV, Cliff DP, Morgan PJ, Plotnikoff RC, Lubans DR. Comparability and feasibility of wrist- and hip-worn accelerometers in free-living adolescents. J Sci Med Sport. 2017;20(12):1101–6.

    Article  PubMed  Google Scholar 

  56. Ward DS, Evenson KR, Vaughn A, Rodgers AB, Troiano RP. Accelerometer use in physical activity: best practices and research recommendations. Med Sci Sports Exerc. 2005;37(11):582–8.

    Article  Google Scholar 

  57. Choi L, Liu Z, Matthews CE, Buchowski MS. Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sports Exerc. 2011;43(2):357–64.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Crouter SE, Flynn JI, Bassett DR. Estimating physical activity in Youth using a wrist accelerometer. Med Sci Sports Exerc. 2015;47(5):944–51.

    Article  PubMed  PubMed Central  Google Scholar 

  59. National Center for Education Statistics. Public elementary/secondary school universe survey [Internet]. 2021. Available from:

  60. Kristensen PL, Olesen LG, Ried-larsen M, Grøntved A, Wedderkopp N, Froberg K, et al. Between-school variation in physical activity, aerobic fitness, and organized sports participation: a multi-level analysis. J Sports Sci. 2013;31(2):188–95.

    Article  PubMed  Google Scholar 

  61. Bonell C, Parry W, Wells H, Jamal F, Fletcher A, Harden A, et al. The effects of the school environment on student health: a systematic review of multi-level studies. Health Place. 2013;21:180–91.

    Article  PubMed  Google Scholar 

  62. SPSS Statistics for Windows [computer program]. Armonk, N.Y., USA: IBM Corp.

  63. Arizona Department of Education. Accountability & research data: Graduation rate, dropout rate, and enrollment reports [Internet]. 2021 [cited 2022 Aug 5]. Available from:

  64. National Center for Education Statistics. U.S. Department of Education, Institute of Education Sciences. Concentration of public school students eligible for free or reduced-price lunch (NCES 20200144; The condition of education). Available from:

  65. Clevenger KA, Belcher BR, Berrigan D. Associations between amount of recess, physical activity, and cardiometabolic traits in U.S. children. Transl J Am Coll Sports Med. 2022;7(3).

  66. Farbo D, Maler LC, Rhea DJ. The preliminary effects of a multi-recess school intervention: using accelerometers to measure physical activity patterns in elementary children. Int J Environ Res Public Health. 2020;17(23):8919.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Brusseau T, Kulinna P, Tudor-Locke C, Van Der Mars H, Darst P. Children’s step counts on weekend, physical education, and non-physical education days. J Hum Kinet. 2011;27(2011):123–34.

    Article  Google Scholar 

  68. Meyer U, Roth R, Zahner L, Gerber M, Puder JJ, Hebestreit H, Kriemler S. Contribution of physical education to overall physical activity. Scand J Med Sci Sports. 2013;23(5):600–6.

    Article  CAS  PubMed  Google Scholar 

  69. Wilson WJ, Yun J, Kern BD. Contribution of physical education and recess to children’s habitual physical activity. Elem Sch J. 2022;123(2):253–70.

    Article  Google Scholar 

  70. Yli-Piipari S, Kulmala JS, Jaakkola T, Hakonen H, Fish JC, Tammelin T. Objectively measured school day physical activity among elementary students in the United States and Finland. J Phys Act Health. 2016;13(4):440–6.

    Article  PubMed  Google Scholar 

  71. Bartholomew JB, Clutton J, Burford K, Aadland E, Resaland GK, Jowers EM, et al. Individual- and environmental-level predictors of recess activity and sedentary behavior: findings from the I-Can! Study. Transl J Am Coll Sports Med. 2022;7(4).

  72. Calahorro-Cañada F, Torres-Luque G, López-Fernández I, Carnero EA. Sedentariness and physical activity during school recess are associated with VO2peak. Int J Environ Res Public Health. 2020;17(13):4733.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Agbaje AO. Longitudinal mediating effect of fat mass and lipids on sedentary time, light PA, and MVPA with inflammation in youth. J Clin Endocrinol Metab. 2023;dgad354.

  74. Kwon S, Janz KF, Burns TL, Levy SM. Association between light-intensity physical activity and adiposity in childhood. Pediatr Exerc Sci. 2011;23(2):218–29.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Pulido Sánchez S, Iglesias Gallego D. Evidence-based overview of accelerometer-measured physical activity during school recess: an updated systematic review. Int J Environ Res Public Health. 2021;18(2):578.

    Article  PubMed  PubMed Central  Google Scholar 

  76. McKenzie TL. 2009 C. H. McCloy Lecture seeing is believing: Observing physical activity and its contexts. Res Q Exerc Sport. 2010;81(2):113–22.

  77. Welk GJ, Corbin CB, Dale D. Measurement issues in the assessment of physical activity in children. Res Q Exerc Sport. 2000;71(sup2):59–73.

    Article  PubMed  Google Scholar 

  78. Kohl HW, Fulton JE, Caspersen CJ. Assessment of physical activity among children and adolescents: a review and synthesis. Prev Med. 2000;31(2):54–76.

    Article  Google Scholar 

  79. Kim Y, Hibbing P, Saint-Maurice PF, Ellingson LD, Hennessy E, Wolff-Hughes DL, Perna FM, Welk GJ. Surveillance of youth physical activity and sedentary behavior with wrist accelerometry. Am J Prev Med. 2017;52(6):872–9.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Trost SG, Brookes DS, Ahmadi MN. Evaluation of wrist accelerometer cut-points for classifying physical activity intensity in youth. Front Digit Health. 2022;4:884307.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Price L, Wyatt K, Lloyd J, Abraham C, Creanor S, Dean S, Hillsdon M. Are we overestimating physical activity prevalence in children? J Phys Act Health. 2018;15(12):941–5.

    Article  PubMed  Google Scholar 

  82. Centers for Disease Control and Prevention and SHAPE America—Society of Health and Physical, Educators. Strategies for recess in schools [Internet]. Atlanta, GA: Centers for Disease Control and Prevention, US Dept of Health and Human Services. ; 2017. Available from:

  83. National Association for Sport and Physical Education. Recess for elementary school students. Position statement [Internet]. ERIC Clearinghouse. ; 2006. Available from:

  84. Chen K, Phipps S. Why can’t you sit still? The effect of daily physical activity on childhood inattention/hyperactivity and the educational gender gap. Soc Sci Med. 2021;284:114232.

    Article  PubMed  Google Scholar 

  85. Clevenger K, McNarry M, Mackintosh K, Berrigan D. Association of recess provision with elementary school-aged children’s physical activity, adiposity, and cardiorespiratory and muscular fitness. 2021;34(2):99–106.

  86. Miller DP. Associations between the home and school environments and child body mass index. Soc Sci Med. 2011;72(5):677–84.

    Article  PubMed  Google Scholar 

  87. U.S. Census Bureau. Annual estimates of the resident population: April 1, 2020 to July 1, 2022 [Internet]. 2023. Available from:

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No external funding was obtained. An internal grant supported the study through the College of Health Solutions at Arizona State University.

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AP: Conceptualization, Methodology, Resources, Writing – Original draft preparation, review, and editing, Supervision; KW: Investigation, Formal analysis, Writing – Original draft preparation; MS and KN: Investigation, Writing – Original draft preparation; POV and YB: Writing – Review and editing; PHK: Investigation, Writing – Original draft preparation, review, and editing; All authors reviewed and approved the final manuscript.

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Correspondence to Allison Poulos.

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Poulos, A., Wilson, K., Schulke, M. et al. A natural experiment to assess recess frequency on children’s physical activity in Arizona (U.S.) elementary schools. BMC Public Health 24, 225 (2024).

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