Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Sch Health. Author manuscript; available in PMC 2011 Nov 16.
Published in final edited form as:
PMCID: PMC3217268

Leisure-Time Physical Activity in Elementary Schools: Analysis of Contextual Conditions

Thomas L. McKenzie, PHD, Professor Emeritus,a Noe C. Crespo, PHD, MS, MPH, Postdoctoral Research Fellow,b Barbara Baquero, MPH, Evaluation Coordinator,c and John P. Elder, PHD, MPH, Professord



Little is known about children’s leisure-time physical activity (PA) at school and how it is associated with contextual variables. The purpose of this study was to objectively assess children’s voluntary PA during 3 daily periods and examine modifiable contextual factors.


We conducted SOPLAY (System for Observing Play and Leisure Activity in Youth) observations before school, during recess, and at lunchtime in 137 targeted activity areas in 13 elementary schools over 18 months. During observations, each child was coded as Sedentary, Walking, or Vigorous, and simultaneous entries were made for area characteristics (accessibility, usability, presence of supervision, loose equipment, and organized activities). Logistic regression analysis was used to test associations between PA and area characteristics.


Assessors made 2349 area visits and observed 36,995 children. Boys had more moderate-to-vigorous physical activity (MVPA; 66.2 vs 60.0%, p < .001) and more vigorous PA (29.8 vs 24.6%; p < .001) than girls. Areas were typically accessible and usable, but provided organized activities infrequently (16.5%). Odds of engaging in MVPA were greater during lunch and recess than before school and in areas with play equipment (p < .05).


Children accrued a substantial amount of voluntary PA during leisure time at school. Their PA would likely be increased if school playground equipment was more readily available and if supervisors were taught to provide active games and promote PA rather than suppress it.

Keywords: child and adolescent health, health policy, organization and administration of school health programs, physical fitness and sport

Physical inactivity (PA) is associated with numerous preventable diseases and is recognized recently as a serious public health problem. National recommendations released in 2008 call for children aged 6 to 17 years to participate daily in 60 minutes or more of moderate-to-vigorous physical activity (MVPA).1 This goal echoes the 2005 Dietary Guidelines for Americans,2 and both reports suggest that children’s PA should be developmentally appropriate, enjoyable, involve a variety of activities, and can be obtained in a variety of settings and accrued throughout the day.

Numerous reports within government3,4 and outside of it58 have called on schools to take a proactive role in promoting PA, especially as a means to combat health problems such as cardiovascular disease, obesity, and type 2 diabetes. Engaging in PA at school is particularly relevant to minority and low-income children, who are at greater risk for inactivity and overweight911 and may not have access to or afford activity programs outside of school.

Most PA studies at school have been done during physical education (PE),6 a curricular area which is often required and is delivered within a highly structured environment that focuses on specific instructional goals. The 2006 School Health Policies and Programs Study (SHPPS), indicated, however, that only 3.8% of elementary schools offered PE daily,12 and, even when offered daily, PE cannot provide enough PA to reach national recommendations.13

Much less is known about children’s voluntary engagement in PA than in PE, and there is a paucity of data on how recess and other leisure time periods at school contribute to children’s overall PA.14 Leisure time at school is important for reasons other than PA, such as allowing for children to socialize and have a break from the classroom,1517 and recent position statements recommended that recess periods total at least 2018 or 30 minutes per day.6 The 2006 SHPPS national survey indicated, however, that only 67.8% of elementary schools provided daily recess for students in all grades.12 Additionally, the Center on Education policy indicated that as a result of the No Child Left Behind Act time for PE and recess had declined substantially between 2001 and 2007 (by as much as 40 and 50 minutes per week, respectively, in some districts).19

Some studies in the United States20,21 and elsewhere2224 have investigated children’s activity at recess using heart rate monitors and pedometers, but these methods provide little information on the context in which activity occurs. Meanwhile, a few intervention studies have indicated that strategies such as restructuring playgrounds by using colorful markings25 and providing game equipment24 may be useful in increasing children’s PA during recess. Recently there has been interest in studying PA from an ecological perspective using direct observation,2629 a method which has the advantage of being able to record PA simultaneously with current contextual influences, including physical and social environmental conditions.30 Assessing the occurrence of potentially modifiable factors and their relationships with PA is important to the design of interventions aimed at increasing PA. The purpose of the present study was to use direct observation to (a) objectively measure leisure-time PA of children before class, at recess, and during lunch, and (b) to assess contextual factors associated with their PA levels.


Background and Schools

Data were collected during baseline measures in the 13 Title I elementary schools participating in Aventuras para Niños (Adventures for Children), a community intervention trial to prevent and control childhood obesity.28,31 The schools were in 3 school districts in San Diego County, CA, and had an average enrollment of 667 children (range = 438 to 1136). All were from low-income neighborhoods and had been invited to participate. School participation criteria included (a) having a Latino enrollment of at least 70%; (b) not having participated in other obesity-related programs or special PE training within the past 4 years; and (c) having defined enrollment boundaries (ie, not charter or magnet schools drawing students from a broad region).

Data were collected over 18 months, ending in January 2004. Observations were made during leisure time periods on campus before school, at recess, and during active lunchtime sessions during 5 days at each school.

All potential areas for PA in each school were identified and measured prior to data collection. Agreement among assessors was established on the location, size, and boundaries of each target area, and maps detailing them and where observers should stand when observations were made. A total of 137 outdoor areas were selected, averaging 10.5 per school (SD = 2.8). Indoor activity areas, such as gymnasiums and multipurpose rooms, were not available in the schools.


Observation Instrument

Observers used SOPLAY (System for Observing Play and Leisure Activity in Youth), an instrument designed to obtain data on the number of children and their PA levels during play and leisure.32 The system is based on momentary time sampling and has been used previously to assess children’s leisure-time PA and relevant playground characteristics in 24 middle schools,32 and a modified version has been used to study PA in community parks.33

Systematic SOPLAY scans of target areas were made during 3 measurement periods (before school, at recess, and during lunchtime). During a scan, the PA of each student in an area was coded as Sedentary (ie, lying down, sitting, or standing), Walking, or Vigorous. These activity codes have been validated by both heart rate monitoring34 and accelerometry.29 Simultaneous entries were made for the time of the observation and for area contextual characteristics, including accessibility, usability, and whether or not supervision, organized activities, and equipment were provided.


Observation Schedule and Protocol

Observations were made during 65 days over the 18-month period, and areas were observed in a specific order during each observation period. During visits to an area, observers scanned the space visually from left to right and entered a code representing each student’s activity level into a handheld mechanical recording device at an approximate rate of 1 child per second. Separate scans were made for girls and boys.

Observer Training and Calibration

Three paid, bilingual, female assessors were trained to collect data. They memorized operational definitions of behavior dimensions and their subcategories first and then learned general procedures for recording data. Videotaped examples and role-playing were used to demonstrate categories, and this was followed by practice observations on playgrounds. Training, which included information on how to reduce reactivity, continued until each observer exceeded an interobserver agreement score of 80% on DVD video segments (approximately 16 hours). Review sessions approximately 1 hour in length were provided each semester.

Data Analysis

The unit of analysis was an observational scan of a predetermined target area. The sample size for the logistic models using area characteristics as explanatory variables was 1223 scans. An additional 1126 scans of target areas were made, but were omitted from analyses because the areas were vacant when observed. Descriptive statistics were used to report means and standard deviations, and chi-square tests were used to compare each area characteristic (ie, supervised, organized, or equipped) by time period (before school, recess, or lunch).

Separate analyses were conducted for each dependent variable (ie, sedentary, walking, vigorous activity, and MVPA). Because of high variability in the total number of children in populated target areas (range = 1 to 103), count data from observations were expressed as proportions to represent the percentage of children in each activity level. This was done by dividing the number of children observed as sedentary, or walking, or vigorously active by the total number of children in the area (eg, 15% of children in one scan were vigorously active). These proportions were calculated separately for boys and girls. For activity measures, counts were tallied for those engaged in Sedentary, Walking, and Vigorous behavior in each area to obtain a summary score. An additional summary score, MVPA, was created by summing the Walking and Vigorous categories.

Histograms and normality tests revealed that the proportions for each PA variable were highly skewed, primarily because of the influence of observations in which no or all of children were engaging in a specific activity level (eg, during a scan 100% were walking and none was sedentary). Several transformations of the dependent variables were conducted, but none improved the distributions to approximate normality. Thus, logistic regression analysis was used to test the associations between time periods and area characteristics to each dependent variable. Each dependent variable was divided into 3 ordered categories as follows: low (0% to 30%), medium (31% to 70%), and high (71% to 100%), except for vigorous activity. There were few scans with high proportions of vigorous activity; therefore, the range for the “high” category of vigorous activity was increased to capture more of the variance (ie, 61% to 100%). The proportional odds assumption was not met for any of the models; therefore, polychotomous logistic regressions were used with unordered dependent variables. For each of these, the low category was used as the reference. Two separate approaches (A and B) were used in order to assess the between- and within-sex associations, respectively. In approach A, models were run with gender as an independent categorical variable and with the gender × area characteristic interaction. In approach B, models were run stratified by gender.

Independent variables (with their respective codes) were supervised (0 = no, 1 = yes); equipment available (0 = no, 1 = yes); organized activity (0 = no, 1 = yes); time period (0 = before school, 1 = recess, 2 = lunch); sex (0 = girl, 1 = boy); and school site (13 unordered school codes). School site was included as a fixed effect to adjust for possible clustering effects of schools. For all independent variables, zero was used as the reference category.

To assess the effects of area characteristics, 3 separate models were fitted using approaches A and B (as described previously). Model 1 was fitted with supervised (yes vs no); model 2 was fitted with equipment available (yes vs no); and model 3 was fitted with organized activity (yes vs no). To assess the effects of the leisure time period, the same procedures were used as for area characteristics, except that leisure time period was entered as one variable with 3 unordered categories (before school was the reference category). All analyses were conducted using SAS (SAS Institute, Cary, NC, V. 9.1.3), and the PROC LOGISTIC procedure was used to carry out polychotomous logistic regression. The total number of children observed during a scan was included as a “weight” variable to account for differences in the number of children between scans; with more weight given to scans with more children. The “normalize” statement was used to reduce the total weights to the number of scans (not the number of children).


General Descriptive Findings

Assessors made 2349 visits to the 137 areas in the 13 schools and coded the activity levels of a total of 36,955 children (17,146 girls; 19,809 boys) during the 3 time periods: before school (6255 girls; 6675 boys), recess (6043 girls; 6831 boys), and lunch periods (4848 girls; 6303 boys). To aid in the interpretation of results, the following subsections refer first to the descriptive data without statistical tests (see Table 2 for leisure time periods and Table 3 for area contexts) and then to the results of logistic regressions with reference to odds ratios and statistical significance.

Table 2
Mean Proportion of Children in Defined Activity Levels During 3 Leisure Time Periods
Table 3
Mean Proportion of Children in Defined Activity Levels by Playground Area Contexts

Table 1 provides overall descriptive data and shows children were sedentary 36.8% of the time and engaged in walking and vigorous PA 36% and 27.2% of the time, respectively. Girls were sedentary proportionally more often than boys and they engaged in vigorous PA and MVPA less often.

Table 1
Proportion of Children in Defined Physical Activity Levels During Leisure Time Periods at School

Activity areas were typically accessible (99.4%) and usable (98.5%), but rarely provided organized activities (16.5%). They were directly supervised 59.6% of the time and had loose equipment available 34.7% of the time. Results of the chi-square tests showed that areas were more often supervised (63.3%, χ2 = 43.7, df = 2, p < .01), organized (20.4%, χ2 = 24.9, df = 2, p < .01), and equipped (48.6%, χ2 = 231.7, df = 2, p < .001) during recess and/or lunch periods than before school.

Leisure Time Periods

Table 2 shows children tended to engage in more MVPA during lunch periods than during recess or before school. Logistic regression results concur with these findings. For both boys and girls, the odds of engaging in MVPA were greater during lunch and recess compared with before school. In addition, there was a tendency for boys to engage in more MVPA than girls during each time period, and this was borne out by the logistic models. Boys showed greater odds of engaging in MVPA than girls during recess and lunch than before school (all odds ratios and gender interaction terms were significant at p < .05).

Area Contexts

Table 3 provides information on PA levels of girls and boys relative to whether or not areas were directly supervised, had organized activities, and provided loose play equipment. Trends showed children engaged in less MVPA when areas were supervised or had organized activities. In contrast, children tended to more often engage MVPA when areas had loose play equipment available. In addition, as shown by consistently greater percentages of walking and MVPA, boys were more active than girls, regardless of whether areas were supervised, organized, or equipped.


When observed, over half the areas (56.9%) were being supervised directly by school staff. In areas that were not supervised, a greater proportion of children were observed walking (34.3% vs 40.5%) and engaging in MVPA (61.6% vs 67.2%). Logistic regressions confirmed these trends for both boys and girls (Table 4), whereby the odds of walking or engaging in MVPA were lower in areas with supervision (most odds ratios p < .05). Last, logistic regressions also showed that boys engaged in greater MVPA compared with girls in areas that were unsupervised compared with supervised areas (all odds ratios and gender interactions significant at p < .05).

Table 4
Odds Ratios of Activity Levels for Time Periods and Area Contexts, Stratified by Gender


Areas provided organized activities during only 16.5% of observations. Boys had similar levels of PA in areas with and without organized activities, but girls tended to engage in more walking and MVPA in areas that had no organized activities. Logistic regressions confirmed that girls had lower odds of engaging in MVPA in areas with organized activities (Table 4) and that these associations significantly differed between genders (odds ratios and gender interactions significant at p < .05).


About one-third of the areas (34.7%) had loose equipment available for students to use, and in these areas children tended to be more physically active than in areas that were not equipped. Logistic regressions indicated that, for both girls and boys, the odds of engaging in MVPA were greater in areas with play equipment (Table 4; odds ratios significant at p < .05). The contribution of available equipment had a greater effect on boys being more physically active than girls (odds ratios and gender interactions significant at p < 0.05).


Children engaged in a substantial amount of PA during the 3 daily time periods. While in playground areas they engaged in MVPA about 63% of the time (walking 36%, plus vigorous 27%), an amount comparable to that found by Beighle et al20 using pedometers. The children were substantially more active at recess and lunch breaks than during before-school periods. However, discounting the before-school time and assuming schools met only the minimum recommendation of 30 minutes per day for recess,6,18 girls and boys would have accrued approximately 20 and 22 minutes, respectively, of the 60 minutes recommended for PA daily. These results are in line with the findings of Tudor-Locke et al35 who found that children accrued from 23% to 25% of their total daily steps at lunchtime and recess, thereby substantiating that unencumbered classroom time at schools is important for children’s accrual of PA.

It is important to recognize that participation in leisure-time activities is voluntary and that PA accrual is not distributed evenly among students. Children in many schools have a choice of whether or not to go to activity areas, and even if they go can choose whether or not to engage in strenuous activities. PA participation may also be related to opportunity, expectations from teachers and supervisors, and how children themselves view leisure time (eg, as a time to play actively or to socialize). Similar to the results of other recess studies,20,21,34,35 boys were found to be more physically active than girls. This gender difference was apparent during each time period and whether or not activity areas were supervised directly, contained organized activities, or had loose equipment available. This is a concern because girls, especially those living in poverty and from ethnic minorities, are at increased risk for low PA levels.36 Recess and other activity programs at schools, unlike most other PA and sport venues, are available to all children regardless of socioeconomic status. Boys and girls often have similar activity levels during PE in elementary schools,21 but it appears that steps need to be taken to ensure girls have equitable opportunities for accruing PA. This could involve numerous strategies, including promotions specifically targeting girls, offering more “girl-friendly” activities, ensuring girls have access to space and equipment, and that the equipment available addresses girls’ as well as boys’ preferences (eg, jump ropes and balls).

Studies sometimes show children are relatively more active at recess than during PE.21,37 Activity comparisons between these 2 settings should be made cautiously, because they differ by arrangement (ie, structured vs nonstructured) and purpose.13,16,17 Recess is unstructured and activity during it is voluntarily. On the other hand, PE is a highly structured curricular area that is required in most states and has national outcome standards, only one of which is PA engagement.38 There is some evidence of a recent reduction of both PE and recess in elementary schools,19 but children need PA in both environments if they are to accrue the recommended 60 minutes daily for health purposes. Recess should neither be discontinued nor be used as a replacement for PE. Additionally, “problem” students in the classroom are often those most in need of PA, so their access to PE and recess ought not to be made contingent upon their classroom behavior or academic standing.

A major finding of this study is the association between children’s PA and 3 modifiable contextual characteristics—supervision, organized activities, and loose equipment. First, children were less active in areas that were being directly supervised. This is not surprising because playground supervisors are trained primarily to ensure that children are safe, and this often means suppressing PA rather than promoting it. In earlier studies, we found playground supervisors promoted (ie, prompted and reinforced) young children to be physically active more frequently than older children34 and that playground supervisors could be trained to modify the playground environment to promote PA.39 Second, boys had similar PA levels in areas with and without organized activities, but girls were relatively more active in areas where there were no organized activities. This unexpected finding could have resulted from several factors, such as the organized coeducational games being dominated by boys and the possibility that the organized games that girls participated in were more sedentary in nature than those selected by boys.40 Nonetheless, it is possible to educate playground supervisors to modify games to make them more active and to ensure that girls’ preferences are addressed and that they get equitable opportunities to participate. Third, children were more active in areas that had loose equipment available, but the contribution of equipment had a greater effect on boys’ activity levels than girls’. A limitation to the study is that it did not record how much equipment was available and who (eg, a boy or girl) was using it. It is possible that boys dominated equipment use or that the equipment provided was less suitable or less attractive to girls (eg, footballs vs jump ropes). Equipment was available in areas only about one third of the time, so providing equipment more often and in greater quantities may be a valuable intervention as previously identified.24

Most studies of PA in schools have taken place during PE.6 Studies during leisure time have been limited, not only to the lack of standardization of the number, duration, location, and purposes of leisure time periods but also because of challenges in assessing children’s activity in open environments.30 The SOPLAY tool, which had been used previously in middle schools, proved to be valuable in assessing children’s PA and relevant modifiable contextual factors in elementary schools. It was able to move beyond the limitations of self-reports and to generate data on large numbers of children without interfering with their behavior in the natural setting.


The study is limited to observations in schools in Southern California where the leisure time periods are held outdoors year-round. The schools were volunteered to be part of the study by their district superintendents and principals and hence do not represent a scientific sample of schools. Additionally, the schools had student bodies that were heavily Latino; therefore, these results may not generalize to other ethnic groups.


We found that children in primarily Latino elementary schools in Southern California accrued substantial amounts of voluntary PA during leisure time periods at school. They were more physically active in areas that were not directly supervised and in those that had loose playground equipment (eg, balls) available. The results suggest that school playgrounds are important venues for providing MVPA and that factors such as supervision, organized activities, and availability of equipment might be modified to increase activity levels even more.


Free time at school provides an important opportunity for elementary school children to accrue important time spent in MVPA. These children may be more active in unsupervised than in directly supervised areas, particularly if these areas have multiuse and readily available balls and other loose playground equipment. Teachers and other staff should ensure that such equipment meets girls’ as well as boys’ preferences. PA would also likely be increased if supervisors were taught to provide active games and promote PA rather than suppress it. Therefore, school staff should find a balance between safety and control on the one hand while simultaneously providing supportive, unobtrusive supervision that facilitates activity on the other.

Human Subjects Approval Statement

This study was approved by San Diego State University’s institutional review board and consent for observations was provided by the school districts and individual school administrators.


1. US Department of Health and Human Services. 2008 Physical Activity Guidelines for Americans. Washington, DC: US Department of Health and Human Services; 2008.
2. US Department of Health and Human Services. Dietary Guidelines for Americans, 2005. Washington, DC: US Department of Health and Human Services, US Department of Agriculture; 2004. US Department of Agriculture.
3. Centers for Disease Control and Prevention. Guidelines for school and community programs to promote lifelong physical activity among young people. Morb Mortal Wkly Rep. 1997;46:366–366. [PubMed]
4. Centers for Disease Control and Prevention. Promoting Physical Activity: A Guide for Community Action. Champaign, IL: Human Kinetics; 1999.
5. Hayman LL, Williams CL, Daniels SR, et al. Cardiovascular health promotion in the schools: a statement for health and education professionals and child health advocates from the Committee on Atherosclerosis, Hypertension, and Obesity in Youth of the Council on Cardiovascular Disease in the Young. American Heart Association. Circulation. 2004;110:2266–2275. [PubMed]
6. Pate RR, Davis MG, Robinson TN, et al. Promoting physical activity in children and youth: a leadership role for schools. Circulation. 2006;114:1214–1224. [PubMed]
7. Koplan JP, Liverman CT, Kraak VA, editors. Preventing Childhood Obesity: Health in the Balance. Washington, DC: Institute of Medicine, National Academies Press; 2005. pp. 237–284.
8. National Association of State Boards of Education. Fit, Healthy, and Ready to Learn: A School Health Policy Guide. [Accessed October 12, 2007];2006 Available at: http://www.nasbe.org/HealthySchools/fithealthy.html.
9. Gordon-Larsen P, McMurray RG, Popkin BM. Determinants of adolescent physical activity and inactivity patterns. Pediatrics. 2000;105:83–90. [PubMed]
10. Kumanyika S, Grier S. Targeting interventions for ethnic minority and low-income populations. Future Child. 2006;16(1):187–218. [PubMed]
11. Ogden CL, Flegal KM, Carroll MD, Johnson CL. Prevalence and trends in overweight among US children and adolescents, 1999–2000. J Am Med Assoc. 2002;288:1728–1732. [PubMed]
12. 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;77:435–463. [PubMed]
13. McKenzie TL, Lounsbery MF. School physical education: the pill not taken. Am J Lifestyle Med. 2009;3(3):219–225.
14. Wechsler H, Devereaux R, Davis M, Collins J. Using the school environment to promote physical activity and healthy eating. Prev Med. 2000;31:S121–S137.
15. Burdette HL, Whitaker RC. Resurrecting free play in young children. Arch Pediatr Adolesc Med. 2005;159:46–50. [PubMed]
16. Pellegrini AD, Bohn CM. The role of recess in children’s cognitive performance and school adjustment. Educ Res. 2005;34:13–19.
17. Pellegrini AD, Blatchford P. The developmental and educational significance of recess in schools. Early Rep. 2002;29:1–7.
18. National Association for Sport and Physical Education. Recess for Elementary School Students. Reston, VA: National Association for Sport and Physical Education; 2006. [position paper]
19. Center on Educational Policy. Instructional Time in Elementary Schools: A Closer Look at Changes for Specific Subjects. Washington, DC: Center on Educational Policy; 2008. Feb, [Accessed April 12, 2009]. Available at: http://www.cep-dc.org/document/docWindow.cfm?fuseaction=document.viewDocument&documentid=234&documentFormatId=3713.
20. Beighle A, Morgan CF, LeMasurier G, Pangrazi RP. Children’s physical activity during recess and outside of school. J Sch Health. 2006;76(10):516–520. [PubMed]
21. Sarkin JA, McKenzie TL, Sallis JF. Gender differences in physical activity during fifth-grade physical education and recess periods. J Teach Phys Educ. 1997;17:99–106.
22. Mota J, Suva P, Santos MP, Ribeiro JC, Oliverira J, Duarte JA. Physical activity and school recess time: differences between the sexes and the relationship between children’s playground physical activity and habitual physical activity. J Sports Sci. 2005;23(3):269–275. [PubMed]
23. Ridgers ND, Stratton G, Fairclough SJ, Twisk JW. Long-term effects of a playground markings and physical structures on children’s recess physical activity levels. Prev Med. 2007;44(5):393–397. [PubMed]
24. Verstraete S, Cardon G, DeClercq D, Bourdeaudhuij I. Increasing children’s physical activity levels during recess periods in elementary schools: the effects of providing game equipment. Eur J Pub Health. 2006;16(4):415–419. [PubMed]
25. Stratton G, Mullan E. The effect of multicolor playground markings on children’s physical activity levels during recess. Prev Med. 2005;41:828–833. [PubMed]
26. Davison KK, Lawson CT. Do attributes in the physical environment influence children’s physical activity? A review of the literature. [Accessed May 12, 2009];Int J Behav Nutr Phys Act. 2006 3:19. [Online journal] [17 p] Available at: http://www.ijbnpa.org/ [PMC free article] [PubMed]
27. McKenzie TL, Sallis JF, Elder JP, et al. Physical activity levels and prompts in young children at recess: a two-year study of a bi-ethnic sample. Res Q Exerc Sport. 1997;68(3):195–202. [PubMed]
28. McKenzie TL, Baquero B, Crespo NC, Arredondo EM, Campbell NR, Elder JP. Environmental correlates of physical activity in Mexican American children at home. J Phys Act Health. 2008;5(4):579–591. [PMC free article] [PubMed]
29. Ridgers ND, Stratton G, McKenzie TL. Reliability and validity of the System for Observing Children’s Activity and Relationships during Play (SOCARP) J Phys Act Health. 2010;5:17–25. [PubMed]
30. McKenzie TL. The use of direct observation to assess physical activity. In: Welk G, editor. Physical Activity Assessments for Health-Related Research. Champaign, IL: Human Kinetics; 2002. pp. 179–195.
31. Arredondo EM, Elder JP, Ayala GX, Campbell N, Baquero B, Duerksen S. Is parenting style related to children’s healthy eating and physical activity in Latino families? Health Educ Res. 2006;21:862–871. [PubMed]
32. McKenzie TL, Marshall SJ, Sallis JF, Conway TL. Leisure-time physical activity in school environments: an observational study using SOPLAY. Prev Med. 2000;30:70–77. [PubMed]
33. 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;1:S203–S217. [PMC free article] [PubMed]
34. McKenzie TL, Sallis JF, Patterson TL, et al. BEACHES: an observational system for assessing children’ s eating and physical activity behaviors and associated events. J Appl Behav Anal. 1991;24:141–151. [PMC free article] [PubMed]
35. Tudor-Locke C, Lee SM, Morgan CF, Beighle A, Pangrazi RP. Children’s pedometer-determined physical activity patterns during the segmented school day. Med Sci Sports Exerc. 2006;38(10):1732–1738. [PubMed]
36. US Department of Health and Human Services. Healthy People 2010 (Conference Edition) Washington, DC: US Department of Health and Human Services; 2000.
37. Sleap M, Warburton P. Physical activity levels of 5–11-year-old children in England as determined by continuous observation. Res Q Exerc Sport. 1992;63:238–245. [PubMed]
38. National Association for Sport and Physical Education. Moving Into the Future: National Standards for Physical Education. 2nd ed. Boston: McGraw Hill; 2004.
39. Connolly P, McKenzie TL. Effects of a games intervention on the physical activity levels of children at recess. Res Q Exerc Sport. 1995;66 suppl:A60.
40. Braza F, Braza P, Carreras MR, Munos JM. Development of sex differences in preschool children: social behavior during an academic year. Psychol Rep. 1997;80:179–188. 1997.
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