Journal of Physical Activity and Health, 2015, 12, 618 -627 http://dx.doi.org/10.1123/jpah.2013-0188 © 2015 Human Kinetics, Inc.
ORIGINAL RESEARCH
Factors Influencing Break-Time Physical Activity of South African Primary School Learners From Low-Income Communities Monika Uys, Catherine Elizabeth Draper, Sharief Hendricks, Anniza de Villiers, Jean Fourie, Nelia Steyn, and Estelle Victoria Lambert Background: The purpose of this study was to assess factors that influence physical activity (PA) levels during break-times in South African primary school children. Methods: The System for Observing Play and Leisure Activities in Youth (SOPLAY) was used to observe PA levels during break-times at low-income schools (4 intervention, 4 control). The intervention was based on action-planning including: school environment, curriculum, and family involvement. Categories of observed activity included Sedentary, Eating, Walking, or Vigorous PA. Contextual factors assessed included teacher supervision, equipment, and crowding. Chi-square tests were used to determine associations between PA levels and contextual factors. Results: In the 970 observations made, 31% of learners were sedentary, 14% were eating, 29% were walking, and 26% were engaged in vigorous PA. There were no differences in break-time PA between intervention and control groups (NS). With supervision, children were more likely to eat and less likely to do vigorous PA (P = .035). Playground crowding was associated with lower levels of vigorous activity and more sedentary behavior (P = .000). Conclusions: PA during break-time was adversely affected by over-crowding and lower with supervision. The results suggest that interventions may be targeted at the school policy environment to reduce these barriers to PA. Keywords: environment, supervision, learner density, voluntary activity
Noncommunicable diseases (NCD) have increased globally1 to such an extent that it is the cause of 60% of deaths worldwide.2 Eighty percent of these deaths occur in low- and middle-income countries (LMIC).3 One of the major risk factors for NCD is physical inactivity.4 At the same time, children have become less active5 and more overweight.6 Undeniably, physical activity (PA) plays an important role in the physical, social and emotional development of the child.7 Evidence also suggests that childhood PA behavior,8 as well as obesity,9,10 tracks into adulthood with PA levels decreasing as children grow.11 The school environment provides a setting which is suitable for promoting PA participation for 2 reasons. Firstly, children spend a significant amount of their time at school.12 Secondly, the school environment provides an opportunity for children to be physically active who may otherwise not engage in PA in their home environment due to the presence of PA barriers. In low-income settings these barriers include, but are not limited to, family obligations, the lack of safe areas to play, the lack of facilities and cost of participating in different activities.13 Typically, there are 2 main opportunities for children to be physically active during the school day: during physical education (PE) and at break-time (recess).14 Most PA studies in school settings look at PA during PE.15 In recent years however, time allocated to PE have been reduced in South Africa16 and other countries.17 Currently in South Africa, 1 hour per week is allocated to PE for intermediate phase learners (Grade 4 to 6) as part of the Life Orientation curriculum. This means that children cannot reach their daily target requirement of 60 minutes of PA per day18 through PE alone. Uys ([email protected]), Draper, Hendricks, and Lambert are with the UCT/MRC Research Unit for Exercise Science and Sports Medicine, University of Cape Town, Cape Town, Western Cape, South Africa. Fourie and de Villiers are with the Chronic Diseases of Lifestyle, Medical Research Council of South Africa, Cape Town, Western Cape, South Africa. Steyn is with the Centre for the Study of Social and Environmental, Human Sciences Research Council, Cape Town, Western Cape, South Africa. 618
Outside of PE, break-times provide children with daily PA opportunities at school.19 Since the recommended 60 minutes of PA per day can be accumulated throughout the day, break-time is an ideal opportunity to encourage children to contribute to their daily PA target requirement.14 With that said, PA during break periods are discretionary, therefore it is important to understand the factors influencing children’s PA behavior during these periods. Ridgers et al suggests that factors related to the school built environment and policies may contribute to, or discourage, PA participation in children.12 For example, renovation of playgrounds20 and playground markings,21 as well as teacher supervision and the availability of loose equipment (such as balls and skipping ropes)21 have all been shown to affect children’s participation in PA. Still, there is little data on the influences of the school built and policy environment on break-time PA in children from LMIC settings, where obesity and related health risks are greatest and resources are least available.22 The aims of the current study were to a) objectively measure voluntary PA of learners during break-times, b) investigate whether these PA levels differs between schools taking part in the HealthKick intervention, and c) assess the impact of contextual factors on these PA levels.
Methods HealthKick HealthKick is a school-based dietary and PA health intervention in low-income communities, aimed at reducing diabetes risk factors.23 Schools were drawn from the second and third lowest economic quintiles, based on ranking by the Western Cape Education Department. The HealthKick study included 8 intervention and 8 control schools, from urban and rural areas. The HealthKick intervention was designed in such a manner that the intervention schools had to take the lead in implementing the intervention, with the research team present in a facilitating role. For this reason, the intervention
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schools were referred to as coimplementation schools and the control schools as self-implementation schools, with the intervention itself being referred to as a ‘low-intensity’ intervention (limited contact).23 Coimplementation schools nominated a HealthKick ‘champion’ to be in charge of health promotion in their school and act as the contact person dealing with the research team. To assist coimplementation schools with implementing selected strategies, they received a HealthKick toolkit (containing an educator’s manual, a resource guide, a resource box, and a PA resource bin). The selfimplementation schools only received a resource guide and none of the other components of the HealthKick toolkit (educator’s manual, resource box, or PA resource bin) and did not receive any assistance from the research team in implementing suggested strategies23 (Table 1). HealthKick encompasses all levels of the Social Ecological model: intra- and interpersonal, organizational, and community level.24 Areas where the intervention had the potential to impact the PA policy and environment at school level include resources for PA and sport, opportunities for PA and sport, support for teachers to be agents of change, and implementation of the curriculum.23 As part of the intervention, schools were required to identify specific strategies they would use to achieve their HealthKick goals within the stipulated zones. The 4 action zones were 1) school food and nutrition environment, 2) school PA and sport environment, 3) staff health, and 4) chronic disease and diabetes awareness. A formative assessment of 100 schools was made before the start of the HealthKick intervention to evaluate the school health environment.25 The results of this assessment showed that the condition of the playgrounds was as follows: the majority had mostly grass, there were a lot of cemented areas available and that the playgrounds were generally free of glass and other dangerous objects.25
Participants Eight schools (4 coimplementation and 4 self-implementation) from the urban areas participating in the HealthKick intervention were used for this study (described elsewhere: Draper et al, 2010). This was a quasi-experimental, posttest only study conducted during the second year of the HealthKick intervention to determine if there were any measurable differences in break-time PA levels between the co- and self-implementation schools. We hypothesized that break-time PA in the coimplementation schools may have increased, in part, as a result of the availability of loose equipment (balls, skipping ropes, cones, etc.) provided in the PA resource bins (for example to hand out the loose equipment during break-times for the learners to use), implementation of the recommended PA actions suggested in the educators manual (for example to set-up a playground circuit with the loose equipment provided) or ideas from the resource guide. The study was approved by the Research Ethics Committee of the Health Sciences Faculty of the University of Cape Town (HREC REF: 486 to 2005).
Research Tools Formative Assessment. A formative assessment was conducted at all HealthKick schools before the onset of the observation.25 This comprised of a situational analysis and an observational schedule. The situational analysis was in the form of a structured interview with the school principal or designee. The purpose of the situational analysis was to gather information about the school’s policies and practices around healthy eating and PA as well as general demographic information (eg, number of learners). The observational
schedule was conducted by research team. This involved the technician walking around on the school premises to gather information about the school physical environment (eg, number of sport fields, available equipment, facilities and safety). Observational Tool: System for Observing Play and Leisure Activities in Youth (SOPLAY). The System for Observing Play
and Leisure Activities in Youth (SOPLAY) was used to observe PA levels during break-times. SOPLAY is a technique developed to take systematic and periodic scans of individuals and external factors in preselected target areas.26 Each scan records the activity of each individual within a target area as being: sedentary (lying down, sitting or standing), walking, or very active. Amendments to Standard SOPLAY Protocol. According to the SOPLAY protocol,26 observations should be performed by scanning each target area from left to right, with girls being scanned first, and then the boys. In our settings, it was not possible to scan girls and boys separately, as school uniforms look similar and many girls have short hair, which meant that they could easily be mistaken for a boy. To avoid misclassifying genders, we did a single scan from left to right, which included both girls and boys. We introduced an additional category called ‘eating.’ In contrast to American schools (where the SOPLAY technique was developed) in which learners have a separate lunch period (for eating) and a break for playing (recess), break-time in South African schools provides the opportunity for eating and playing. Another amendment made to the SOPLAY protocol was to use a dictaphone to record the different activity categories during the observations instead of a 3-buttoned counter (due to lack of availability). Time to perform the scans was brief, therefore short key words to code for the different categories: ‘sit’ or ‘stand’ for sedentary, ‘walk’ for walking, ‘play’ for vigorous activity, and ‘eat’ for eating while engaged in sedentary behavior or walking were created. Upon completion of data collection, the recordings were transcribed onto paper by the researcher. The transcriptions were then used to enter the school, time, break number, observer, and activity into a spreadsheet. Lastly, the size of each target area at every school was measured using a measuring wheel. This was to determine the learner density (the number of learners per area in meters squared) of each target area. Target areas were determined by obtaining an aerial view map of each school’s playground from Google Maps.27 Thereafter, a construct of the school’s playground was developed (Figure 1). Using this construct, target areas were mapped out during break depending on the number of learners occupying an area. Areas that were off-limits to learners during break-times were not selected as target areas. Care was taken to ensure that the 4 main target areas included grounds with and without markings, as well as different surfaces such as grass, sand and tar. Each target area was further subdivided into 2 areas (A and B), to allow the relevant fieldworker to do focused observations over a smaller area.
Observations Five fieldworkers were trained to do the observations. Observations began 3 minutes after the school bell rang for break-time to allow learners enough time to disperse out of their classrooms and into the play areas. A scan was then performed every 3 minutes; alternating between A and B within a target area until the end of break (Figure 2). The fieldworker had to ensure that both areas A and B were scanned an equal number of times during each break. Observations were done during both the first and second break at all schools, except for 2 schools that did not have a second break on that particular observation day.
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- Vendors
- Tuck shop
1. School food and nutrition environment
- Vegetable garden
- National School Nutrition Program
- Nutrition education
- Food as a reward for good behavior
- Lunch boxes
2. School physical activity and sport environment
1. School food and nutrition environment
- A physical activity resource bin
2. Eat more different kinds of fruit and vegetables every day
1. Eat a variety of foods every day
Action zones
- Fundraising or foods for special events
• Nominate a HealthKick champion to be in charge of implanting the intervention
Goals
Areas of action within each zone
- A resource box
- A resource guide
- An educator’s manual
HealthKick toolkit containing:
Received
Implementation of the intervention by the schools
Coimplementation schools (intervention schools)
Self-implementation schools did not receive any support from the research team No workshops were held for educator from self-implementation schools
Area: Family and community involvement Strategy: Physical activity or sporting event: Arrange a fun walk in which learners’ families and community members can participate to promote the importance of physical activity for health
‘HealthKick tips for healthy schools’ and the resource guide, but no other components of the HealthKick toolkit (such as the educator’s manual, resource box or physical activity resource bin)
Received
(continued)
None. The selfimplementation schools served as the control schools and were not part of the intervention. We provided the self-implementation schools with some printed materials and resources to try and overcome some of the challenges of doing schoolbased research
Implementation of the intervention by the schools
Self-implementation schools (control schools)
Action zone: School physical activity and sport environment
Example of process to reach physical activity related goal
Table 1 Detailed Description of Coimplementation and Self-Implementation Schools
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Workshops were held with educators from coimplementation schools to explain the relevance of HealthKick to Life Orientation, and indicate ways in which HealthKick supports its implementation
Support from the research team
Received
• Implement chosen strategies
• Action planning: identify specific strategies to achieve HealthKick goals within the specified areas for action (with some options provided for certain zones), the individuals within the school who would take responsibility for these strategies and a timeframe for completion - Break-time
3. Staff Health
4. Chronic disease and Diabetes awareness
3. Eat less fat and oily food 4. Eat less sugar and sweet foods, such as cakes, doughnuts, sweets, etc.
3. Staff health
6. Bring healthy lunchboxes to school as a daily routine
- Lesson plans
8. Be more physically active after school
- Parent talks
- Health checks
- National awareness days and activities
- Learner takehome activities
- Posters
4. Chronic disease and diabetes awareness
7. Be more physically active during school time
- Role modeling
- Physical activity behaviors
- Food and nutrition behaviors
- Staff health awareness and health promotion
- Family and community involvement
5. Eat a regular healthy breakfast daily
- Improve sport and extramural sport
- Physical education classes
2. School physical activity and sport environment
Action zones
Goals
Areas of action within each zone
Coimplementation schools (intervention schools)
Implementation of the intervention by the schools
Table 1 (continued) Example of process to reach physical activity related goal Received
Implementation of the intervention by the schools
Self-implementation schools (control schools)
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Figure 1 — School playground construct. An aerial map of the school grounds were used to create a construct of the school playgrounds. This construct was used to map out the target areas during a visit to the schools at break-time prior to data collection.
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Break-Time Physical Activity Correlates 623
Figure 2 — Procedure for observing a target area. For target area 1, subdivide area in 2 equal areas: 1A and 1B. Start in area 1A. After bell has gone, wait 3 minutes before beginning with the first scan. When scan is completed, move to area 1B. At 6 minutes into the break, begin the scan. This will be the first scan for area 1B, but the second overall scan for area 1. Thereafter, return to area 1A and continue the same steps until the end of break is reached. An equal amount of scans should be done in both area 1A and 1B, therefore, if you are done with area 1B and need to return to area 1A with less than 6 minutes left of break, scans should be stopped at that moment.
Environmental Conditions The environmental conditions of target areas were also assessed using the following criteria according to the SOPLAY protocol: • Accessibility: learners are able and allowed to access the area during break-times • Usability: area is usable for PA (eg, not too wet for play) • Area improvements: addition of line markings, netball hoops or painted games • Supervision: a teacher or prefect is available to react in the case of an emergency • Surface type: hard surface (tar, paving or concrete) or soft surface (grass or sand) • Loose equipment: equipment learners brought to school from home (jumping ropes, rugby or soccer balls, and cricket bats).
Data Analysis All analyses were done using STATA 11 (StataCorp). The primary outcome measured was level of PA. Chi-square tests were used to
determine whether there were any differences in environmental characteristics relating to PA between co- and self-implementation schools. Chi-square tests were also used to calculate differences in the proportions of PA levels with and without certain environmental conditions (the presence or absence of area improvements, supervision and loose equipment) as well as with different area densities. Different areas were classified according to learner density quartiles: low learner density areas (first quartile: 0.06 to 0.43 learners per 100 m2), low-to-medium learner density areas (second quartile: > 0.43 to 1.05 learners per 100 m2), medium-to-high learner density (third quartile: > 1.05 to 2.34 learners per 100 m2) and high learner density areas (fourth quartile: > 2.34 to 28.7 learners per 100 m2). Significance was set at P < .05.
Results General School Characteristics Results from the formative assessment showed that the characteristics of the school environment relating to PA (which included: having a school health committee, having daily break-time of at least 20 minutes, offering at least 3 different types of sport, separating different
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grades during break-times, having more than 3 playgrounds available, the condition of the playgrounds, prohibiting using PA as punishment, having at least 30 minutes of structured PA per week in the timetable as well as the number of sport equipment available) were not significantly different between co- and self-implementation schools (Table 2).
Environmental Factors
Differences in Break-Time Physical Activity Levels Between Coimplementation and SelfImplementation Schools Who Took Part in the HealthKick Intervention
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differ between the first and the second break (Table 4). More learners were eating during the first break than during the second break (16% during the first break and 11% during the second break), although this was not significant (χ2 = 5.580, df = 3, P = .134).
A total of 970 scans were made across the 8 schools during breaktimes. Three hundred and forty-two scans were not analyzed as there were no children in the area at the time of the scan. Overall, 31% of observed learners were sedentary, 14% were eating, 29% were classified as walking and 26% were engaging in vigorous PA during break-times (Table 3). The main aim of this study was to get a sense of how active South African children are during break-times as there are limited data available in a South African context. This was a quasi-experimental posttest only study with the observations done only during the intervention, without pre- and posttest measurements. There was no significant difference between any of the PA levels between the designated groups (co- and self-implementation schools) at this stage of the intervention and as such data were combined form hereon forward.
Break-Times The first break ranged in length from 15 to 30 minutes (average of 21 minutes) and the second break from 15 to 25 minutes (average of 18 minutes). The proportions of different activity levels did not
Area Improvements. The proportion of different levels of PA in areas with some improvements did not differ from areas without any improvements (χ2 = 0.503, df = 3, P = .918). There was no significant difference between coimplementation and self-implementation schools. Supervision. Only 55% of schools had supervision during
break-times. Chi-square tests showed that the presence or absence of supervision had an overall effect on activity levels (χ2 = 8.620, df = 3, P = .035). In the areas that did not have any supervision, a greater proportion of learners were being sedentary (32% vs. 30%). Areas being supervised had a higher proportion of learners eating than unsupervised areas (17% vs. 11%). In the areas where supervisors were present, the proportion of learners participating in vigorous PA was lower than in unsupervised areas (24% vs. 28%). There was no significant difference between coimplementation and self-implementation schools.
Surface Type. The surface type did not have any effect on PA levels (Table 5). The proportion of different levels of PA in areas with hard surfaces did not differ from areas with soft surfaces (χ2 = 1.213, df = 3, P = .750). There was no significant difference between coimplementation and self-implementation schools. Loose Equipment. The proportion of learners participating in vigorous PA were higher when loose equipment was available (27%
Table 2 General Characteristics of the School Environment of Participating Schools (n = 16 Schools) Self-implementation schools (n = 8)
School characteristic
Coimplementation schools (n = 8)
P-value 1.000
Presence of school health committee
88
88
Daily break-time of at least 20 minutes
100
100
Offer at least 3 different types of sport
63
75
0.590
Different grades separated during break-times
37
63
0.317
More than 2 playgrounds available
38
50
0.614
Playgrounds are generally free of litter and glass or dangerous objects
50
25
0.302
Prohibit using physical activity as punishment
88
63
0.248
Weekly structured physical activity in timetable of at least 30 min
63
88
0.248
Sport equipment available
13
38
0.248
* Values reported as percentages.
Table 3 Proportion of Children Participating in Different Levels of Physical Activity During Leisure Time at School Self-implementation
Coimplementation
Min
Max
%
Mean
SD
Overall
%
Mean
SD
Min
Max
%
Sedentary
31.74
13
12
1
49
30.37
17
15
1
93
30.93
Eating
13.85
7
9
1
48
14.49
10
14
1
56
14.23
Walking
30.23
9
7
1
37
28.45
10
10
1
53
29.18
Vigorous
24.18
10
8
1
37
26.70
8
8
1
52
25.67
100
10
10
1
49
100
12
13
1
93
100
Total
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Break-Time Physical Activity Correlates 625
vs. 19%), although not significantly (χ2 = 6.631, df = 3, P = .085) (Table 5). No significant difference between coimplementation and self-implementation schools was found.
may not be sufficient to change overall PA behavior. Although this finding is not ideal, it is still an important finding as it can inform future research to design interventions that would be more appropriate in a South African setting. Some of the ‘actions’ recommended to the coimplementation schools were to have an activity track laid out on the playground as well as to have colorful markings on the playground. Unfortunately none of the schools chose to implement these actions at the time of the observations. (One school did have newly painted playground markings, but it was in an area that was off limits to learners during break-times.). There were no significant differences between the proportions of leaners who were physically active between the first and the second break. The proportion of learners eating during break-time (14%) highlights the importance of this additional ‘eating’ category. Performing the observations without this category could mean that a small portion of learners might not be counted, or could be counted as being sedentary which implies that they are choosing to be sedentary. Instead, break-time is also the only time learners can eat their snack or lunches. Overall, South African learners have much less time to play than their overseas counterparts. Although their first
Learner Density of Scan Areas Learner density had an overall effect on the proportions of the different activity levels (χ2 = 90.950, df = 9, P = .000). The proportion of learners engaging in sedentary behavior in areas with a low learner density was 18%, compared with 50% in areas with a high learner density. In areas with a low learner density, the proportion of learners engaging in vigorous activity was much higher than in areas with a high learner density (28% and 13%, respectively) (Table 6).
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Discussion The main aim of this study was to objectively measure voluntary PA of South African learners during break-times. Although the majority of learners were active during break-times, a large proportion was engaged in sedentary activities. By being more active during break-times, learners may increase their chances of reaching their recommended PA daily target of 60 minutes. As such, school break-times could be used as a settings for PA interventions to increase learners’ PA levels. The second aim of this study was to investigate whether the proportion of learners engaging in PA during break-times differ between co- and self-implementation schools participating in the HealthKick intervention. The intervention was designed in such a way that the schools were able to choose the strategies they would use to reach the HealthKick goals (through the action planning process) and it was their responsibility to implement the chosen strategies. It was found that there were no significant differences between the proportions of different levels of PA between the selfimplementation and the coimplementation schools at this stage of the intervention, demonstrating that a low-intensity intervention
Table 4 Proportion of Children Participating in Different Levels of Physical Activity During the First Break Compared With the Second Break First break
Second break*
Sedentary (%)
29.98
32.58
Eating (%)
16.21
10.76
Walking (%)
28.36
30.59
Vigorous (%)
25.45
26.06
* Two of self-implementation schools did not have a second break on the day of the observations.
Table 5 Proportion of Children Participating in Different Levels of Physical Activity by Environmental Conditions Area improvements
Supervision*
Surface type
Loose equipment
Yes
No
Yes
No
Hard surface
Soft surface
Yes
No
Sedentary (%)
29.91
31.43
29.88
32.42
30.24
31.91
30.90
31.06
Eating (%)
14.33
14.18
16.87
10.47
15.21
12.81
13.23
19.25
Walking (%)
30.53
28.51
29.17
29.18
29.20
29.15
28.92
30.43
Vigorous (%)
25.23
25.89
24.08
27.93
25.35
26.13
26.95
19.25
* Significant overall effect, P < .05.
Table 6 Proportion of Children Participating in Different Levels of Physical Activity by Learner Density of Scan Areas Low learner density (Large area, few children)
Low-to-medium learner density (Medium area, few children)
Medium-to-high learner density (Medium area, a lot of children)
High learner density (Small area, a lot of children)
Sedentary (%)
17.55
25.91
31.25
49.58
Eating (%)
24.90
11.34
8.75
11.76
Walking (%)
29.39
32.39
29.58
25.21
Vigorous (%)
28.16
30.36
30.42
13.45
Note. Learner density = learners per
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break is more or less the same duration (15 to 30 minutes) as recess in the UK (15 to 20 minutes)31 and Canada (15 to 25 minutes),32 their second break (15 to 25 minutes) is much shorter than the lunch break in overseas schools which ranges from 35 to 50 minutes in Canada32 to 45 to 65 minutes in the UK.31 The addition of the ‘eating’ category could therefore provide a more realistic perspective of what is happening on the playground during break-times in settings, such as South Africa, where break-time is much shorter and used for eating and playing. Furthermore, although school feeding schemes have been successful at reducing underweight in learners, there is now an increase in the prevalence of overweight/obesity among South African learners,33 demonstrating the need to shift the focus toward promoting PA, along with addressing under-nutrition. The final aim of this study was to assess the impact of environmental factors on PA levels during break-times. The presence of supervision is normally thought to enhance PA participation;21,28 however, our study demonstrated similar findings to McKenzie,29 who showed a decrease in vigorous activity levels in areas that were supervised. With that said, the role of the supervisor during break-time is not fully understood, and highlights an area for future research. It is possible that South African teachers’ main priority would be to ensure that there is order on the playgrounds since there is, in general, a lot of unruly behavior (such as bullying) on schools grounds. In a 2007 study, over one-third of South African adolescents were involved in bullying behavior.30 Teachers could instead be trained to promote PA on the playground. Evidently, learner density was a very strong determinant for break-time PA. In small areas with a great number of learners, half of the learners were participating in sedentary behavior and very few were participating in vigorous activity. Less dense areas (even medium-to-high densities) had more than double the proportion of learners engaging in vigorous activity demonstrating that overcrowding on playgrounds has a negative impact on PA levels. An intervention study which decreased playground density in preschools resulted in a decrease in sedentary time and an increase in both light-to-vigorous and moderate-to-vigorous PA.34 This was achieved by dividing classes in 2 groups and scheduling different times for recess for the 2 groups. Lastly, measuring the target areas and determining the learner density is not part of the standard SOPLAY protocol, but could be a useful additional tool.
Limitations Two of the self-implementation schools did not have a second break on the day of the observations. Ideally, we should have done the observations on a day with 2 breaks, but practically this was not possible due to the challenge of scheduling data collection with the schools. However, it is very common for South African schools to finish the school day earlier than normal on 1 day during the week. On these days, administrators will adjust the schedule by taking away the second break to maximize teaching time. Furthermore, the observations were done at a small number of schools, all from low-income areas. This was a quasi-experimental posttest only study. This means that we took a ‘snap shot’ of break-time PA levels at the HealthKick schools at a certain point in time. We were thus not able to determine an intervention effect on break-time PA levels. However, future studies should include both pre- and posttest measurements and should also be done on a greater number of schools, incorporating schools from middle and high income areas to compare between low- and high-income areas. Furthermore, our self-implementation group received some
resources, which means that they were not a true control group, but given the challenges of school-based research, it was not possible to have a true control group.
Practical Implications As mentioned above, teachers could be trained to promote PA on the playground to increase PA participation. However, to avoid adding additional responsibility to a teacher’s already full schedule, another option could be to train senior learners to become ‘Play Leaders’ as recommended in the HealthKick educators’ manual. It would be their responsibility to encourage play on the playground by demonstrating different activities and providing new game ideas to the rest of the learners and encouraging learners who might not normally be active to join in the games. This approach has been used previously in a project called “Healthy Buddies”35 a health promotion program in primary schools based on older children teaching younger children. After the intervention, both older and younger learners showed an increase in healthy-living knowledge, behavior and attitude, as well as smaller increases in weight. The authors suggest that this type of student-led teaching may be an efficient and feasible way of promoting healthy lifestyles.
Conclusion We found that learners in primary schools in low-income areas of the Western Cape were active during break-times, but that a substantial number of children engaged in sedentary behavior. PA during break-time in these South African primary schools was adversely affected by over-crowding and teacher supervision. The results suggest that educators were more involved in ‘crowd control’ than the promotion of PA during break-time, and that interventions may be targeted at the school policy environment to reduce these barriers to PA.
References 1. Yach D, Hawkes C, Gould CL, Hofman KJ. The global burden of chronic diseases: overcoming impediments to prevention and control. JAMA. 2004;291:2616–2622. PubMed doi:10.1001/jama.291.21.2616 2. World Health Organization. Global health risks: mortality and burden of disease attributable to selected major risks. Geneva: World Health Organization, December 2009. http://www.who.int/healthinfo/ global_burden_disease/GlobalHealthRisks_report_full.pdf. Accessed on April 20, 2012. 3. World Health Organization. 2008. 2008-2013 action plan for the global strategy for the prevention and control of non-communicable diseases. Geneva: World Health Organization. http://www.who.int/ nmh/Actionplan-PC-NCD-2008.pdf. Accessed on December 10, 2011. 4. Venkat Narayan KM, Ali MK, Koplan JP. Global noncommunicable diseases—where worlds meet. N Engl J Med. 2010;363(13):1196– 1198. PubMed doi:10.1056/NEJMp1002024 5. Dollman J, Norton K, Norton L. Evidence for secular trends in children’s physical activity behaviour. Br J Sports Med. 2005;39(12):892– 897, discussion 897. PubMed doi:10.1136/bjsm.2004.016675 6. Kosti RI, Panagiotakos DB. The epidemic of obesity in children and adolescents in the world. Cent Eur J Public Health. 2006;14(4):151– 159. PubMed 7. Milteer RM, Ginsburg KR. The importance of play in promoting healthy child development and maintaining strong parent-child bond: focus on children in poverty. Pediatrics. 2012;129:204–213. PubMed doi:10.1542/peds.2011-2953
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8. Malina RM. Adherence to physical activity from childhood to adulthood: a perspective from tracking studies. Quest. 2001;53:346–355. doi:10.1080/00336297.2001.10491751 9. Burke V. Obesity in childhood and cardiovascular risk. Clin Exp Pharmacol Physiol. 2006;33(9):831–837. PubMed doi:10.1111/j.14401681.2006.04449.x 10. Clarke WR, Lauer RM. Does childhood obesity track into adulthood? Crit Rev Sci Nutr. 1993;33(4-5):423–430. PubMed doi:10.1080/10408399309527641 11. Sallis JF. Epidemiology of physical activity and fitness in children and adolescents. Pediatr Exerc Sci. 1993;33:403–408. PubMed 12. Ridgers ND, Saint-Maurice PF, Welk GJ, Siahpush M, Huberty J. Differences in physical activity during school recess. J Sch Health. 2011;81:545–551. PubMed doi:10.1111/j.17461561.2011.00625.x 13. Humbert ML, Chad KE, Spink KS, et al. Factors that influence physical activity participation among high- and lowSES youth. Qual Health Res. 2006;16(4):467–483. PubMed doi:10.1177/1049732305286051 14. Ridgers ND, Stratton G, Fairclough SJ. Assessing physical activity during recess using accelerometry. Prev Med. 2005;41:102–107. PubMed doi:10.1016/j.ypmed.2004.10.023 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. Circulation. 2006;114:1214–1224. PubMed doi:10.1161/ CIRCULATIONAHA.106.177052 16. Van Deventer K. Perspectives of teachers on the implementation of life orientation in grades R-11 from selected Western Cape schools. S Afr J Edu. 2009;29:127–145. 17. Hardman K, Marshall J. The state and status of physical education inschools in international context. Eur Phys Educ Rev. 2000;6(3):203– 229. doi:10.1177/1356336X000063001 18. WHO. Global recommendations on physical activity for health. Geneva: World Health Organisation; 2010. 19. Beighle A, Morgan CF, Le Masurier G, Pangrazi RP. Children’s physical activity during recess and outside of school. J Sch Health. 2006;76(10):516–520. PubMed doi:10.1111/j.17461561.2006.00151.x 20. Colabianchi N, Kinsella AE, Coulton CJ, Moore SM. Utilization and physical activity levels at renovated and unrenovated school playgrounds. Prev Med. 2009;48:140–143. PubMed doi:10.1016/j. ypmed.2008.11.005 21. Willenberg LJ, Ashbolt R, Gibbs L, et al. Increasing school playground physical activity: a mixed methods study combining environmental measures and learners’s perspectives. J Sci Med Sport. 2010;13:210– 216. PubMed doi:10.1016/j.jsams.2009.02.011 22. Day K. Active living and social justice: planning for physical activity in low-income, Black, and Latino Communities. J Am Plann Assoc. 2006;72(1):88–99. doi:10.1080/01944360608976726
23. Draper CE, de Villiers A, Lambert EV, et al. HealthKick: a nutrition and physical activity intervention for primary schools in low-income settings. BMC Public Health. 2010;10:398. PubMed doi:10.1186/14712458-10-398 24. Sallis JF, Owen N. Ecological models of health behavior. In: Glanz K, Lewis FM, BK R, eds. Health behavior and health education: theory, research and practice. 3rd ed. San Francisco: JosseyBass;2002:462–484. 25. de Villiers A, Steyn NP, Draper CE, et al. “HealthKick”: formative assessment of the health environment in low-resource primary schools in the Western Cape Province of South Africa. BMC Public Health. 2012;12:794. PubMed doi:10.1186/1471-2458-12-794 26. McKenzie TL. The system for observing play and leisure time activity in youth protocol (protocol/vers 1.10.06).http://www. activelivingresearch.org/files/SOPLAY_Protocols.pdf. Accessed on April 20, 2012. 27. http://maps.google.co.za/maps?hl=en&tab=wl 28. Sallis JF, Conway TL, Prochaska JJ, McKenzie TL, Marshall SJ, Brown M. The association of school environments with youth physical activity. Am J Public Health. 2001;91(4):618–620. PubMed doi:10.2105/AJPH.91.4.618 29. McKenzie TL, Crespo NC, Baquero B, Elder JP. Leisure-time physical activity in elementary schools: analysis of contextual conditions. J Sch Health. 2010;80:470–477. PubMed doi:10.1111/j.17461561.2010.00530.x 30. Liang H, Flisher AJ, Lombard CJ. Bullying, violence, and risk behaviour in South African school students. Child Abuse Negl. 2007;31:161–171. PubMed doi:10.1016/j.chiabu.2006.08.007 31. Bailey DP, Fairclough SJ, Savory LA, et al. Accelerometry-assessed sedentary behaviour and physical activity levels duringthe segmented school day in 10-14-year-old children: the HAPPY study. Eur J Pediatr. 2012;171(12):1805–1813. doi:10.1007/s00431-012-1827-0 32. Nettlefold L, McKay HA, Warburton DER, McGuire KA, Bredin SSD, Naylor PJ. The challenge of low physical activity during the school day: at recess, lunch and in physical education. Br J Sports Med. 2011;45:813–819. PubMed doi:10.1136/bjsm.2009.068072 33. Armstrong ME, Lambert MI, Lambert EV. Secular trends in the prevalence of stunting, overweight and obesity among South African children (1994-2004). Eur J Clin Nutr. 2011;65(7):835–840. PubMed doi:10.1038/ejcn.2011.46 34. Van Cauwenberghe E, De Bourdeaudhuij I, Maes L, Cardon G. Efficacy and feasibility of lowering playground density to promote physical activity and to discourage sedentary time during recess at preschool: A pilot study. Prev Med. 2012;55(4):319–321. PubMed doi:10.1016/j.ypmed.2012.07.014 35. Stock S, Miranda C, Evans S, et al. Healthy Buddies: a novel, peer-led health promotion program for the prevention of obesity and eating disorders in children in elementary school. Pediatrics. 2007;120(4):e1059–e1068. PubMed doi:10.1542/peds.2006-3003
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ORIGINAL RESEARCH
Factors Influencing Break-Time Physical Activity of South African Primary School Learners From Low-Income Communities Monika Uys, Catherine Elizabeth Draper, Sharief Hendricks, Anniza de Villiers, Jean Fourie, Nelia Steyn, and Estelle Victoria Lambert Background: The purpose of this study was to assess factors that influence physical activity (PA) levels during break-times in South African primary school children. Methods: The System for Observing Play and Leisure Activities in Youth (SOPLAY) was used to observe PA levels during break-times at low-income schools (4 intervention, 4 control). The intervention was based on action-planning including: school environment, curriculum, and family involvement. Categories of observed activity included Sedentary, Eating, Walking, or Vigorous PA. Contextual factors assessed included teacher supervision, equipment, and crowding. Chi-square tests were used to determine associations between PA levels and contextual factors. Results: In the 970 observations made, 31% of learners were sedentary, 14% were eating, 29% were walking, and 26% were engaged in vigorous PA. There were no differences in break-time PA between intervention and control groups (NS). With supervision, children were more likely to eat and less likely to do vigorous PA (P = .035). Playground crowding was associated with lower levels of vigorous activity and more sedentary behavior (P = .000). Conclusions: PA during break-time was adversely affected by over-crowding and lower with supervision. The results suggest that interventions may be targeted at the school policy environment to reduce these barriers to PA. Keywords: environment, supervision, learner density, voluntary activity
Noncommunicable diseases (NCD) have increased globally1 to such an extent that it is the cause of 60% of deaths worldwide.2 Eighty percent of these deaths occur in low- and middle-income countries (LMIC).3 One of the major risk factors for NCD is physical inactivity.4 At the same time, children have become less active5 and more overweight.6 Undeniably, physical activity (PA) plays an important role in the physical, social and emotional development of the child.7 Evidence also suggests that childhood PA behavior,8 as well as obesity,9,10 tracks into adulthood with PA levels decreasing as children grow.11 The school environment provides a setting which is suitable for promoting PA participation for 2 reasons. Firstly, children spend a significant amount of their time at school.12 Secondly, the school environment provides an opportunity for children to be physically active who may otherwise not engage in PA in their home environment due to the presence of PA barriers. In low-income settings these barriers include, but are not limited to, family obligations, the lack of safe areas to play, the lack of facilities and cost of participating in different activities.13 Typically, there are 2 main opportunities for children to be physically active during the school day: during physical education (PE) and at break-time (recess).14 Most PA studies in school settings look at PA during PE.15 In recent years however, time allocated to PE have been reduced in South Africa16 and other countries.17 Currently in South Africa, 1 hour per week is allocated to PE for intermediate phase learners (Grade 4 to 6) as part of the Life Orientation curriculum. This means that children cannot reach their daily target requirement of 60 minutes of PA per day18 through PE alone. Uys ([email protected]), Draper, Hendricks, and Lambert are with the UCT/MRC Research Unit for Exercise Science and Sports Medicine, University of Cape Town, Cape Town, Western Cape, South Africa. Fourie and de Villiers are with the Chronic Diseases of Lifestyle, Medical Research Council of South Africa, Cape Town, Western Cape, South Africa. Steyn is with the Centre for the Study of Social and Environmental, Human Sciences Research Council, Cape Town, Western Cape, South Africa. 618
Outside of PE, break-times provide children with daily PA opportunities at school.19 Since the recommended 60 minutes of PA per day can be accumulated throughout the day, break-time is an ideal opportunity to encourage children to contribute to their daily PA target requirement.14 With that said, PA during break periods are discretionary, therefore it is important to understand the factors influencing children’s PA behavior during these periods. Ridgers et al suggests that factors related to the school built environment and policies may contribute to, or discourage, PA participation in children.12 For example, renovation of playgrounds20 and playground markings,21 as well as teacher supervision and the availability of loose equipment (such as balls and skipping ropes)21 have all been shown to affect children’s participation in PA. Still, there is little data on the influences of the school built and policy environment on break-time PA in children from LMIC settings, where obesity and related health risks are greatest and resources are least available.22 The aims of the current study were to a) objectively measure voluntary PA of learners during break-times, b) investigate whether these PA levels differs between schools taking part in the HealthKick intervention, and c) assess the impact of contextual factors on these PA levels.
Methods HealthKick HealthKick is a school-based dietary and PA health intervention in low-income communities, aimed at reducing diabetes risk factors.23 Schools were drawn from the second and third lowest economic quintiles, based on ranking by the Western Cape Education Department. The HealthKick study included 8 intervention and 8 control schools, from urban and rural areas. The HealthKick intervention was designed in such a manner that the intervention schools had to take the lead in implementing the intervention, with the research team present in a facilitating role. For this reason, the intervention
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schools were referred to as coimplementation schools and the control schools as self-implementation schools, with the intervention itself being referred to as a ‘low-intensity’ intervention (limited contact).23 Coimplementation schools nominated a HealthKick ‘champion’ to be in charge of health promotion in their school and act as the contact person dealing with the research team. To assist coimplementation schools with implementing selected strategies, they received a HealthKick toolkit (containing an educator’s manual, a resource guide, a resource box, and a PA resource bin). The selfimplementation schools only received a resource guide and none of the other components of the HealthKick toolkit (educator’s manual, resource box, or PA resource bin) and did not receive any assistance from the research team in implementing suggested strategies23 (Table 1). HealthKick encompasses all levels of the Social Ecological model: intra- and interpersonal, organizational, and community level.24 Areas where the intervention had the potential to impact the PA policy and environment at school level include resources for PA and sport, opportunities for PA and sport, support for teachers to be agents of change, and implementation of the curriculum.23 As part of the intervention, schools were required to identify specific strategies they would use to achieve their HealthKick goals within the stipulated zones. The 4 action zones were 1) school food and nutrition environment, 2) school PA and sport environment, 3) staff health, and 4) chronic disease and diabetes awareness. A formative assessment of 100 schools was made before the start of the HealthKick intervention to evaluate the school health environment.25 The results of this assessment showed that the condition of the playgrounds was as follows: the majority had mostly grass, there were a lot of cemented areas available and that the playgrounds were generally free of glass and other dangerous objects.25
Participants Eight schools (4 coimplementation and 4 self-implementation) from the urban areas participating in the HealthKick intervention were used for this study (described elsewhere: Draper et al, 2010). This was a quasi-experimental, posttest only study conducted during the second year of the HealthKick intervention to determine if there were any measurable differences in break-time PA levels between the co- and self-implementation schools. We hypothesized that break-time PA in the coimplementation schools may have increased, in part, as a result of the availability of loose equipment (balls, skipping ropes, cones, etc.) provided in the PA resource bins (for example to hand out the loose equipment during break-times for the learners to use), implementation of the recommended PA actions suggested in the educators manual (for example to set-up a playground circuit with the loose equipment provided) or ideas from the resource guide. The study was approved by the Research Ethics Committee of the Health Sciences Faculty of the University of Cape Town (HREC REF: 486 to 2005).
Research Tools Formative Assessment. A formative assessment was conducted at all HealthKick schools before the onset of the observation.25 This comprised of a situational analysis and an observational schedule. The situational analysis was in the form of a structured interview with the school principal or designee. The purpose of the situational analysis was to gather information about the school’s policies and practices around healthy eating and PA as well as general demographic information (eg, number of learners). The observational
schedule was conducted by research team. This involved the technician walking around on the school premises to gather information about the school physical environment (eg, number of sport fields, available equipment, facilities and safety). Observational Tool: System for Observing Play and Leisure Activities in Youth (SOPLAY). The System for Observing Play
and Leisure Activities in Youth (SOPLAY) was used to observe PA levels during break-times. SOPLAY is a technique developed to take systematic and periodic scans of individuals and external factors in preselected target areas.26 Each scan records the activity of each individual within a target area as being: sedentary (lying down, sitting or standing), walking, or very active. Amendments to Standard SOPLAY Protocol. According to the SOPLAY protocol,26 observations should be performed by scanning each target area from left to right, with girls being scanned first, and then the boys. In our settings, it was not possible to scan girls and boys separately, as school uniforms look similar and many girls have short hair, which meant that they could easily be mistaken for a boy. To avoid misclassifying genders, we did a single scan from left to right, which included both girls and boys. We introduced an additional category called ‘eating.’ In contrast to American schools (where the SOPLAY technique was developed) in which learners have a separate lunch period (for eating) and a break for playing (recess), break-time in South African schools provides the opportunity for eating and playing. Another amendment made to the SOPLAY protocol was to use a dictaphone to record the different activity categories during the observations instead of a 3-buttoned counter (due to lack of availability). Time to perform the scans was brief, therefore short key words to code for the different categories: ‘sit’ or ‘stand’ for sedentary, ‘walk’ for walking, ‘play’ for vigorous activity, and ‘eat’ for eating while engaged in sedentary behavior or walking were created. Upon completion of data collection, the recordings were transcribed onto paper by the researcher. The transcriptions were then used to enter the school, time, break number, observer, and activity into a spreadsheet. Lastly, the size of each target area at every school was measured using a measuring wheel. This was to determine the learner density (the number of learners per area in meters squared) of each target area. Target areas were determined by obtaining an aerial view map of each school’s playground from Google Maps.27 Thereafter, a construct of the school’s playground was developed (Figure 1). Using this construct, target areas were mapped out during break depending on the number of learners occupying an area. Areas that were off-limits to learners during break-times were not selected as target areas. Care was taken to ensure that the 4 main target areas included grounds with and without markings, as well as different surfaces such as grass, sand and tar. Each target area was further subdivided into 2 areas (A and B), to allow the relevant fieldworker to do focused observations over a smaller area.
Observations Five fieldworkers were trained to do the observations. Observations began 3 minutes after the school bell rang for break-time to allow learners enough time to disperse out of their classrooms and into the play areas. A scan was then performed every 3 minutes; alternating between A and B within a target area until the end of break (Figure 2). The fieldworker had to ensure that both areas A and B were scanned an equal number of times during each break. Observations were done during both the first and second break at all schools, except for 2 schools that did not have a second break on that particular observation day.
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- Vendors
- Tuck shop
1. School food and nutrition environment
- Vegetable garden
- National School Nutrition Program
- Nutrition education
- Food as a reward for good behavior
- Lunch boxes
2. School physical activity and sport environment
1. School food and nutrition environment
- A physical activity resource bin
2. Eat more different kinds of fruit and vegetables every day
1. Eat a variety of foods every day
Action zones
- Fundraising or foods for special events
• Nominate a HealthKick champion to be in charge of implanting the intervention
Goals
Areas of action within each zone
- A resource box
- A resource guide
- An educator’s manual
HealthKick toolkit containing:
Received
Implementation of the intervention by the schools
Coimplementation schools (intervention schools)
Self-implementation schools did not receive any support from the research team No workshops were held for educator from self-implementation schools
Area: Family and community involvement Strategy: Physical activity or sporting event: Arrange a fun walk in which learners’ families and community members can participate to promote the importance of physical activity for health
‘HealthKick tips for healthy schools’ and the resource guide, but no other components of the HealthKick toolkit (such as the educator’s manual, resource box or physical activity resource bin)
Received
(continued)
None. The selfimplementation schools served as the control schools and were not part of the intervention. We provided the self-implementation schools with some printed materials and resources to try and overcome some of the challenges of doing schoolbased research
Implementation of the intervention by the schools
Self-implementation schools (control schools)
Action zone: School physical activity and sport environment
Example of process to reach physical activity related goal
Table 1 Detailed Description of Coimplementation and Self-Implementation Schools
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Workshops were held with educators from coimplementation schools to explain the relevance of HealthKick to Life Orientation, and indicate ways in which HealthKick supports its implementation
Support from the research team
Received
• Implement chosen strategies
• Action planning: identify specific strategies to achieve HealthKick goals within the specified areas for action (with some options provided for certain zones), the individuals within the school who would take responsibility for these strategies and a timeframe for completion - Break-time
3. Staff Health
4. Chronic disease and Diabetes awareness
3. Eat less fat and oily food 4. Eat less sugar and sweet foods, such as cakes, doughnuts, sweets, etc.
3. Staff health
6. Bring healthy lunchboxes to school as a daily routine
- Lesson plans
8. Be more physically active after school
- Parent talks
- Health checks
- National awareness days and activities
- Learner takehome activities
- Posters
4. Chronic disease and diabetes awareness
7. Be more physically active during school time
- Role modeling
- Physical activity behaviors
- Food and nutrition behaviors
- Staff health awareness and health promotion
- Family and community involvement
5. Eat a regular healthy breakfast daily
- Improve sport and extramural sport
- Physical education classes
2. School physical activity and sport environment
Action zones
Goals
Areas of action within each zone
Coimplementation schools (intervention schools)
Implementation of the intervention by the schools
Table 1 (continued) Example of process to reach physical activity related goal Received
Implementation of the intervention by the schools
Self-implementation schools (control schools)
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Figure 1 — School playground construct. An aerial map of the school grounds were used to create a construct of the school playgrounds. This construct was used to map out the target areas during a visit to the schools at break-time prior to data collection.
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Figure 2 — Procedure for observing a target area. For target area 1, subdivide area in 2 equal areas: 1A and 1B. Start in area 1A. After bell has gone, wait 3 minutes before beginning with the first scan. When scan is completed, move to area 1B. At 6 minutes into the break, begin the scan. This will be the first scan for area 1B, but the second overall scan for area 1. Thereafter, return to area 1A and continue the same steps until the end of break is reached. An equal amount of scans should be done in both area 1A and 1B, therefore, if you are done with area 1B and need to return to area 1A with less than 6 minutes left of break, scans should be stopped at that moment.
Environmental Conditions The environmental conditions of target areas were also assessed using the following criteria according to the SOPLAY protocol: • Accessibility: learners are able and allowed to access the area during break-times • Usability: area is usable for PA (eg, not too wet for play) • Area improvements: addition of line markings, netball hoops or painted games • Supervision: a teacher or prefect is available to react in the case of an emergency • Surface type: hard surface (tar, paving or concrete) or soft surface (grass or sand) • Loose equipment: equipment learners brought to school from home (jumping ropes, rugby or soccer balls, and cricket bats).
Data Analysis All analyses were done using STATA 11 (StataCorp). The primary outcome measured was level of PA. Chi-square tests were used to
determine whether there were any differences in environmental characteristics relating to PA between co- and self-implementation schools. Chi-square tests were also used to calculate differences in the proportions of PA levels with and without certain environmental conditions (the presence or absence of area improvements, supervision and loose equipment) as well as with different area densities. Different areas were classified according to learner density quartiles: low learner density areas (first quartile: 0.06 to 0.43 learners per 100 m2), low-to-medium learner density areas (second quartile: > 0.43 to 1.05 learners per 100 m2), medium-to-high learner density (third quartile: > 1.05 to 2.34 learners per 100 m2) and high learner density areas (fourth quartile: > 2.34 to 28.7 learners per 100 m2). Significance was set at P < .05.
Results General School Characteristics Results from the formative assessment showed that the characteristics of the school environment relating to PA (which included: having a school health committee, having daily break-time of at least 20 minutes, offering at least 3 different types of sport, separating different
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grades during break-times, having more than 3 playgrounds available, the condition of the playgrounds, prohibiting using PA as punishment, having at least 30 minutes of structured PA per week in the timetable as well as the number of sport equipment available) were not significantly different between co- and self-implementation schools (Table 2).
Environmental Factors
Differences in Break-Time Physical Activity Levels Between Coimplementation and SelfImplementation Schools Who Took Part in the HealthKick Intervention
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differ between the first and the second break (Table 4). More learners were eating during the first break than during the second break (16% during the first break and 11% during the second break), although this was not significant (χ2 = 5.580, df = 3, P = .134).
A total of 970 scans were made across the 8 schools during breaktimes. Three hundred and forty-two scans were not analyzed as there were no children in the area at the time of the scan. Overall, 31% of observed learners were sedentary, 14% were eating, 29% were classified as walking and 26% were engaging in vigorous PA during break-times (Table 3). The main aim of this study was to get a sense of how active South African children are during break-times as there are limited data available in a South African context. This was a quasi-experimental posttest only study with the observations done only during the intervention, without pre- and posttest measurements. There was no significant difference between any of the PA levels between the designated groups (co- and self-implementation schools) at this stage of the intervention and as such data were combined form hereon forward.
Break-Times The first break ranged in length from 15 to 30 minutes (average of 21 minutes) and the second break from 15 to 25 minutes (average of 18 minutes). The proportions of different activity levels did not
Area Improvements. The proportion of different levels of PA in areas with some improvements did not differ from areas without any improvements (χ2 = 0.503, df = 3, P = .918). There was no significant difference between coimplementation and self-implementation schools. Supervision. Only 55% of schools had supervision during
break-times. Chi-square tests showed that the presence or absence of supervision had an overall effect on activity levels (χ2 = 8.620, df = 3, P = .035). In the areas that did not have any supervision, a greater proportion of learners were being sedentary (32% vs. 30%). Areas being supervised had a higher proportion of learners eating than unsupervised areas (17% vs. 11%). In the areas where supervisors were present, the proportion of learners participating in vigorous PA was lower than in unsupervised areas (24% vs. 28%). There was no significant difference between coimplementation and self-implementation schools.
Surface Type. The surface type did not have any effect on PA levels (Table 5). The proportion of different levels of PA in areas with hard surfaces did not differ from areas with soft surfaces (χ2 = 1.213, df = 3, P = .750). There was no significant difference between coimplementation and self-implementation schools. Loose Equipment. The proportion of learners participating in vigorous PA were higher when loose equipment was available (27%
Table 2 General Characteristics of the School Environment of Participating Schools (n = 16 Schools) Self-implementation schools (n = 8)
School characteristic
Coimplementation schools (n = 8)
P-value 1.000
Presence of school health committee
88
88
Daily break-time of at least 20 minutes
100
100
Offer at least 3 different types of sport
63
75
0.590
Different grades separated during break-times
37
63
0.317
More than 2 playgrounds available
38
50
0.614
Playgrounds are generally free of litter and glass or dangerous objects
50
25
0.302
Prohibit using physical activity as punishment
88
63
0.248
Weekly structured physical activity in timetable of at least 30 min
63
88
0.248
Sport equipment available
13
38
0.248
* Values reported as percentages.
Table 3 Proportion of Children Participating in Different Levels of Physical Activity During Leisure Time at School Self-implementation
Coimplementation
Min
Max
%
Mean
SD
Overall
%
Mean
SD
Min
Max
%
Sedentary
31.74
13
12
1
49
30.37
17
15
1
93
30.93
Eating
13.85
7
9
1
48
14.49
10
14
1
56
14.23
Walking
30.23
9
7
1
37
28.45
10
10
1
53
29.18
Vigorous
24.18
10
8
1
37
26.70
8
8
1
52
25.67
100
10
10
1
49
100
12
13
1
93
100
Total
Abbreviations: SD, standard deviation. JPAH Vol. 12, No. 5, 2015
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vs. 19%), although not significantly (χ2 = 6.631, df = 3, P = .085) (Table 5). No significant difference between coimplementation and self-implementation schools was found.
may not be sufficient to change overall PA behavior. Although this finding is not ideal, it is still an important finding as it can inform future research to design interventions that would be more appropriate in a South African setting. Some of the ‘actions’ recommended to the coimplementation schools were to have an activity track laid out on the playground as well as to have colorful markings on the playground. Unfortunately none of the schools chose to implement these actions at the time of the observations. (One school did have newly painted playground markings, but it was in an area that was off limits to learners during break-times.). There were no significant differences between the proportions of leaners who were physically active between the first and the second break. The proportion of learners eating during break-time (14%) highlights the importance of this additional ‘eating’ category. Performing the observations without this category could mean that a small portion of learners might not be counted, or could be counted as being sedentary which implies that they are choosing to be sedentary. Instead, break-time is also the only time learners can eat their snack or lunches. Overall, South African learners have much less time to play than their overseas counterparts. Although their first
Learner Density of Scan Areas Learner density had an overall effect on the proportions of the different activity levels (χ2 = 90.950, df = 9, P = .000). The proportion of learners engaging in sedentary behavior in areas with a low learner density was 18%, compared with 50% in areas with a high learner density. In areas with a low learner density, the proportion of learners engaging in vigorous activity was much higher than in areas with a high learner density (28% and 13%, respectively) (Table 6).
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Discussion The main aim of this study was to objectively measure voluntary PA of South African learners during break-times. Although the majority of learners were active during break-times, a large proportion was engaged in sedentary activities. By being more active during break-times, learners may increase their chances of reaching their recommended PA daily target of 60 minutes. As such, school break-times could be used as a settings for PA interventions to increase learners’ PA levels. The second aim of this study was to investigate whether the proportion of learners engaging in PA during break-times differ between co- and self-implementation schools participating in the HealthKick intervention. The intervention was designed in such a way that the schools were able to choose the strategies they would use to reach the HealthKick goals (through the action planning process) and it was their responsibility to implement the chosen strategies. It was found that there were no significant differences between the proportions of different levels of PA between the selfimplementation and the coimplementation schools at this stage of the intervention, demonstrating that a low-intensity intervention
Table 4 Proportion of Children Participating in Different Levels of Physical Activity During the First Break Compared With the Second Break First break
Second break*
Sedentary (%)
29.98
32.58
Eating (%)
16.21
10.76
Walking (%)
28.36
30.59
Vigorous (%)
25.45
26.06
* Two of self-implementation schools did not have a second break on the day of the observations.
Table 5 Proportion of Children Participating in Different Levels of Physical Activity by Environmental Conditions Area improvements
Supervision*
Surface type
Loose equipment
Yes
No
Yes
No
Hard surface
Soft surface
Yes
No
Sedentary (%)
29.91
31.43
29.88
32.42
30.24
31.91
30.90
31.06
Eating (%)
14.33
14.18
16.87
10.47
15.21
12.81
13.23
19.25
Walking (%)
30.53
28.51
29.17
29.18
29.20
29.15
28.92
30.43
Vigorous (%)
25.23
25.89
24.08
27.93
25.35
26.13
26.95
19.25
* Significant overall effect, P < .05.
Table 6 Proportion of Children Participating in Different Levels of Physical Activity by Learner Density of Scan Areas Low learner density (Large area, few children)
Low-to-medium learner density (Medium area, few children)
Medium-to-high learner density (Medium area, a lot of children)
High learner density (Small area, a lot of children)
Sedentary (%)
17.55
25.91
31.25
49.58
Eating (%)
24.90
11.34
8.75
11.76
Walking (%)
29.39
32.39
29.58
25.21
Vigorous (%)
28.16
30.36
30.42
13.45
Note. Learner density = learners per
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break is more or less the same duration (15 to 30 minutes) as recess in the UK (15 to 20 minutes)31 and Canada (15 to 25 minutes),32 their second break (15 to 25 minutes) is much shorter than the lunch break in overseas schools which ranges from 35 to 50 minutes in Canada32 to 45 to 65 minutes in the UK.31 The addition of the ‘eating’ category could therefore provide a more realistic perspective of what is happening on the playground during break-times in settings, such as South Africa, where break-time is much shorter and used for eating and playing. Furthermore, although school feeding schemes have been successful at reducing underweight in learners, there is now an increase in the prevalence of overweight/obesity among South African learners,33 demonstrating the need to shift the focus toward promoting PA, along with addressing under-nutrition. The final aim of this study was to assess the impact of environmental factors on PA levels during break-times. The presence of supervision is normally thought to enhance PA participation;21,28 however, our study demonstrated similar findings to McKenzie,29 who showed a decrease in vigorous activity levels in areas that were supervised. With that said, the role of the supervisor during break-time is not fully understood, and highlights an area for future research. It is possible that South African teachers’ main priority would be to ensure that there is order on the playgrounds since there is, in general, a lot of unruly behavior (such as bullying) on schools grounds. In a 2007 study, over one-third of South African adolescents were involved in bullying behavior.30 Teachers could instead be trained to promote PA on the playground. Evidently, learner density was a very strong determinant for break-time PA. In small areas with a great number of learners, half of the learners were participating in sedentary behavior and very few were participating in vigorous activity. Less dense areas (even medium-to-high densities) had more than double the proportion of learners engaging in vigorous activity demonstrating that overcrowding on playgrounds has a negative impact on PA levels. An intervention study which decreased playground density in preschools resulted in a decrease in sedentary time and an increase in both light-to-vigorous and moderate-to-vigorous PA.34 This was achieved by dividing classes in 2 groups and scheduling different times for recess for the 2 groups. Lastly, measuring the target areas and determining the learner density is not part of the standard SOPLAY protocol, but could be a useful additional tool.
Limitations Two of the self-implementation schools did not have a second break on the day of the observations. Ideally, we should have done the observations on a day with 2 breaks, but practically this was not possible due to the challenge of scheduling data collection with the schools. However, it is very common for South African schools to finish the school day earlier than normal on 1 day during the week. On these days, administrators will adjust the schedule by taking away the second break to maximize teaching time. Furthermore, the observations were done at a small number of schools, all from low-income areas. This was a quasi-experimental posttest only study. This means that we took a ‘snap shot’ of break-time PA levels at the HealthKick schools at a certain point in time. We were thus not able to determine an intervention effect on break-time PA levels. However, future studies should include both pre- and posttest measurements and should also be done on a greater number of schools, incorporating schools from middle and high income areas to compare between low- and high-income areas. Furthermore, our self-implementation group received some
resources, which means that they were not a true control group, but given the challenges of school-based research, it was not possible to have a true control group.
Practical Implications As mentioned above, teachers could be trained to promote PA on the playground to increase PA participation. However, to avoid adding additional responsibility to a teacher’s already full schedule, another option could be to train senior learners to become ‘Play Leaders’ as recommended in the HealthKick educators’ manual. It would be their responsibility to encourage play on the playground by demonstrating different activities and providing new game ideas to the rest of the learners and encouraging learners who might not normally be active to join in the games. This approach has been used previously in a project called “Healthy Buddies”35 a health promotion program in primary schools based on older children teaching younger children. After the intervention, both older and younger learners showed an increase in healthy-living knowledge, behavior and attitude, as well as smaller increases in weight. The authors suggest that this type of student-led teaching may be an efficient and feasible way of promoting healthy lifestyles.
Conclusion We found that learners in primary schools in low-income areas of the Western Cape were active during break-times, but that a substantial number of children engaged in sedentary behavior. PA during break-time in these South African primary schools was adversely affected by over-crowding and teacher supervision. The results suggest that educators were more involved in ‘crowd control’ than the promotion of PA during break-time, and that interventions may be targeted at the school policy environment to reduce these barriers to PA.
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