Neighborhood environment and physical activity among Urban and Rural Schoolchildren in Taiwan

ARTICLE IN PRESS Health & Place 16 (2010) 470–476

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Neighborhood environment and physical activity among Urban and Rural Schoolchildren in Taiwan Sheu-jen Huanga,n, Wen-chi Hungb, Patricia A. Sharpec, Jackson P. Waid a

Department of Health Promotion and Health Education, National Taiwan Normal University, 162,1 Section, Ho-ping East Road,Taipei, Taiwan, ROC Department of Health Beauty, Cardinal Tien College of Healthcare & Management, Taiwan c Department of Exercise Science and Prevention Research Center, University of South Carolina d Laboratory for Exercise Physiology Research, Institute of Sport Science, National Taiwan Sport University, Taiwan b

a r t i c l e in f o

a b s t r a c t

Article history: Received 27 May 2009 Received in revised form 27 November 2009 Accepted 1 December 2009

The purpose of this study is to investigate the influence of perceived neighborhood environment on physical activity among schoolchildren in urban and rural areas in Taiwan. Five hundred and twenty three children of grades five and six selected from ten primary schools in urban and rural areas participated in the study. A modified International Physical Activity questionnaire short form was used to estimate the children’s physical activity level. A Chinese translation of the Neighborhood Environment Walkability Scale assessed environmental attributes. Data analysis included descriptive statistics and analysis of variance and multiple regression models. No significant difference in walkability was found between the urban and rural areas. There was a difference in accessibility to places for physical activity between urban and rural areas, with urban children reporting greater accessibility. The urban children reported more physical activity after school, on holidays and weekends, and also in total amount of physical activity compared with the rural children. In conclusion, accessibility to facilities had a significant impact on the children’s physical activity. & 2009 Elsevier Ltd. All rights reserved.

Keywords: Physical activity Schoolchildren Environmental factors Walkability Urban–rural differences Exercise

1. Introduction There has been substantial scientific evidence indicating that physical activity produces a number of major health benefits for people of all ages (Sallis and Owen, 1999; Warburton et al., 2006). Advances in energy-saving technology and changing work patterns have at the same time resulted in a decrease in physical activity. In modern society, the adverse effects of an inactive lifestyle start in childhood. Results from studies on children’s health and exercise behaviors (Biddle et al., 2004; Andersen et al., 2006) suggested that cardiovascular disease, hypertension, high total cholesterol, and obesity were linked to childhood unhealthy behavioral patterns such as high-fat diet and physical inactivity. The same pattern is true in Taiwan, where the chronic and degenerative diseases of middle and old age are believed to be related to unhealthy childhood behaviors (Taiwan Department of Health, 2006). Although both lifestyle physical activity and structured exercise are promoted as beneficial to both individuals and society, the proportion of physically active people in Taiwan is relatively small. The prevalence of Taiwanese elementary school students engaged in regular exercise was 18.8% (Taiwan Sport

n

Corresponding author. Tel.: + 886 2 77341732; fax: + 886 2 23630326. E-mail address: [email protected] (S.-J. Huang).

1353-8292/$ - see front matter & 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.healthplace.2009.12.004

Affairs Council, 2009), which does not compare favorably to their counterparts in other countries. For example, Troiano et al. (2008) found that 42% of American children exercised 60 minutes per day. Research into the factors that influence Taiwanese children’s physical activity is important, so that those children who are physically inactive may be targeted for special interventions. In order to develop effective intervention programs to promote physical activity among children, there is a need to identify variables that influence activity levels (Caspersen et al., 1998). Physical activity is a multifactorial behavior influenced by a set of variables ranging from intrapersonal and interpersonal factors to environmental factors; among these, environmental variables are particularly important. Considerable recent evidence documents a correlation between neighborhood environment and physical activity (Bogaert et al., 2003; Cunningham and Michael, 2004; Frank et al., 2003; Heath et al., 2006; Saelens et al., 2003). In a comprehensive literature review on the relationship between built environments and physical activity of children aged 3–18, Davison and Lawson (2006) found that physical activity was correlated to an area’s public recreational infrastructure, such as access to recreational facilities and schools, the presence of sidewalks and controlled intersections, access to destinations and public transportation. By contrast, factors such as the number of roads to cross and high traffic density and traffic speed, as well as the prevalence of crime and area deprivation, were negatively correlated with children’s physical activity. Humpel et al. (2002)

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identified 19 quantitative studies that assessed the relationship of physical activity with perceived and objectively determined physical environment attributes, and found that environmental attributes such as accessibility, opportunity, and aesthetic attributes had significant effect on physical activity levels. Similarly, Pikora et al. (2003) developed a framework of potential environmental influences on outdoor activities like walking and cycling based on policy literature and expert interview, to include four features: function, safety, aesthetics and destination. Environmental factors that support physical activity have been identified in Western society; however, few studies in Asian countries have explored this issue, especially in terms of children’s physical activity. Many experts have suggested that targeted intervention for specific subpopulations is needed to increase the overall physical activity of these groups. Thus, the examination of specific subpopulations within a socioecological framework is clearly warranted. Researchers (Pratt et al., 1999; Sallis et al., 2000) have pointed out that much additional work is needed on geographical differences as compared with other social factors such as gender, race and ethnicity in studies of physical activity. Findings from studies of urbanization and children’s physical activity in the US and other areas are inconsistent and appear to vary across studies conducted in different regions (Damore, 2002; Loucaides et al., 2004; Joens-Matre et al., 2008; Sirard et al., 2005; Felton et al., 2002), and the potential impact of rural and urban residence should be subjected to more detailed study to design more effective programs. Therefore, the objectives of this study are (1) to examine differences between Taiwan urban and rural areas in physical activity levels of primary schoolchildren; (2) to compare differences in environmental attributes between the two areas; and (3) to explore the environmental determinants of physical activity by taking geographical differences into consideration. These findings could be used in the design of intervention programs aimed at raising children’s physical activity levels.

2. Methods 2.1. Subjects The sample consisted of 726 Taiwanese primary schoolchildren aged 11–12 and their caretakers, mostly parents or grandparents, who completed the daily teacher–parent communication log and had the children take it back to school the next day. Five hundred and twenty three students provided complete data for the analysis with a response rate of 72.0%. Children came from five urban schools (n= 200) and five rural schools (n = 323). The selection of schools was based on the diverse geographic areas of the island and an urbanization index (Tseng and Wu, 1986). All five urban schools were in Taipei City, the capital of Taiwan, and at the top of eight levels of the urbanization index. The schools were randomly selected from five of the twelve districts in the eastern, western, southern, northern and middle parts of Taipei City. The five rural schools were in Ho-long Township, in the middle of the island. These five schools were in fact the only schools in Ho-long Township, a typical Taiwan village setting. Ho-long Township is at the bottom-second level of the eight levels on the urbanization index. Informed consent was obtained from the caretakers prior to participation. One class of 5th and 6th grade students in each school was randomly selected for participation. The students took questionnaires home to their caretakers to collect data for the household. The goals and procedures of the study were explained to the students by their school teachers and researchers during

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class time, and students were assured their responses would be kept confidential. Most students complied well with the study. 2.2. Instrument translation and evaluation The Neighborhood Environment Walkability Scale (NEWS) was used to measure perceived environmental attributes (Owen et al., 2007). Authorization was obtained for translation into Chinese. The Guidelines for Back Translation of the World Health Organization were followed (World Health Organization, 2007). For the first step, a qualified English–Chinese translator translated NEWS into correct and readable Chinese. Additionally, ten in-depth interviews with residents in each district were conducted. A community windshield assessment was followed to collect information to assess the actual environmental conditions and cultural characteristics of the areas so that they could be reflected in the questionnaire. In the second step, a bilingual (English and Chinese) expert panel composed of three professors in public health, urban planning and physical education as well as two elementary school principals was convened to identify and resolve any inadequacies of expression or concepts in the translation. The third step was for a native English speaker to backtranslate the questionnaire into English. Discrepancies were discussed with the research team. Further evaluation of the questionnaire followed three principles, as follows: (1) clarity of concepts; (2) common and comprehensible wording; and (3) conceptual equivalence with the original questionnaire. Two doctoral students in sport science compared the original with the back-translated versions and edited the Chinese version if necessary. Next, a pretest was administered to one hundred and eleven 5th and 6th grade schoolchildren in one urban and one rural elementary school which were not part of the main study. Pretest respondents were asked what they thought the questions were asking, if there were any words they did not understand, and whether they found any words or expressions unacceptable or offensive. The children could suggest alternative wording. A retest of the questionnaire was held two weeks later. Internal consistency (Cronabch’s a) and test–retest reliability (r) were calculated to identify any poorly performing items for omission from the final version. 2.3. Measures The outcome variables of physical activity were assessed by two instruments. We adopted Wu and Pender (2002) modified Chinese version of Child/Adolescent Activity Log (CAAL) (Garcia et al., 1997) to assess the prevalence of various physical activities. Participants recalled their daily physical activities and the associated duration (in minutes) guided by a list of 25 activities for four days including one weekend or holiday. Test–retest reliability was between 0.3 and 0.8 (Wu and Pender, 2002). Another questionnaire used was adapted from the short form of the International Physical Activity Questionnaire (IPAQ) (http:// www.ipaq.ki.se) to estimate the amount of physical activity undertaken by respondents. We categorized physical activity into periods: ‘‘between classes,’’ ‘‘after school,’’ and ‘‘weekends and holidays,’’ as well as the perceived degree of exertion by respondents. Students were asked to report in each category the frequency (bout per week) and amount of time spent in the past week or previous two weeks in exercise that made their ‘‘heart beat rapidly,’’ or made them ‘‘breathe or sweat heavily’’ (for vigorous exercise); or ‘‘some increase in breathing and sweating’’ (for moderate exercise); or ‘‘breathe easily, feel normal’’ (light

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exercise). The response alternatives were ‘‘none,’’ ‘‘1–2 days’’, ‘‘3–4 days’’, or ‘‘every day’’ for weekdays; and ‘‘none’’, ‘‘once’’, or ‘‘twice’’ for ‘‘weekends and holidays’’ in the previous two weeks. The duration question asked students ‘‘How many minutes did you spend each time (bout)?’’ Energy consumption for each physical activity category was calculated using formula: energy consumption (MET
h per week) =MET x duration (min per bout)  frequency (bouts per week). METs are metabolic equivalents referenced from data by Harrell et al. (2005) for children of similar age groups (8–12 years old for boys; 8–11 years old for girls). We chose 11.0 (running, 8.0 kph or 5.0 mph), 7.0 (brisk walking, 5.6 kph or 3.5 mph), and 5.2 MET (walking, 4.0 kph or 2.5 mph) to correspond to our vigorous, moderate, and light activity levels, respectively, as these were the best choices from the available literatures to approximate our definition of the intensity levels. For weekday frequency rates of ‘‘none,’’ ‘‘1–2 days’’, ‘‘3–4 days’’, or ‘‘every day’’, the values used were ‘‘0’’, ‘‘1.5’’, ‘‘3.5’’ and ‘‘5.0’’ days, respectively. The total amount of energy consumption was the sum of energy consumed for all light, moderate, and vigorous activities in between classes, after school and on weekends and holidays. The test–retest reliability of IPAQ is 0.6–0.8. Independent variables included perceived environment and accessibility of physical activity facilities and places. The questionnaire was adapted from the Neighborhood Environment Walkability Scale (NEWS). Also, it was modified as described in the previous section. The perceived environment included 11 items measuring the aspects of aesthetics (4 items), convenience (4 items), and safety (3 items). The response alternatives ranged from ‘‘strongly disagree’’ to ‘‘strongly agree’’, as indicated from ‘‘1–5’’. A summed score was created. Higher numbers indicated a more pro-activity environment except on the issue of safety, where a safer environment was indicated by a lower value. The students then indicated on a checklist their choices among 22 places where it would be convenient for them to get exercise, like on the way home, or within a 15 min walking distance from home. The places included parks, baseball fields, creeks or pools, and bicycle tracks. The researchers assigned 1 point to each place checked. A summed score of accessibility was computed. 2.4. Sociodemographic variables As for sociodemographic variables, the educational level of the caretakers and household monthly income were included in our study. The four response alternatives for educational level were elementary school, junior high school, senior high school, and college and above. Monthly income was categorized into 5 levels: below NT$20,000; NT$20,000–40,000 (around US$660–US$1330); NT$ 40,000–60,000 (around US$1300–US$2000); NT$ 60,000– 100,000 (around US$2000–US$3300); and above NT$100,000. These were the only data in this study collected from the students’ caretakers.

questionnaires were not included in the final analysis. The IPAQ was administered once at the time when CAAL was given at the first day. The National Science Council in Taiwan approved the procedures used in the study and provided funding for the research. Statistical Package for the Social Science (SPSS) 16.0 was used for data editing and analysis. Descriptive statistics including means and standard deviations were calculated for all physical activities and perceived environment measures. Because of the nested nature of the data (individuals within school), intraclass correlation coefficients (ICC) were computed to determine the degree of school-level clustering. Group contrasts between Taiwanese urban and rural children were tested by analysis of variance (ANOVA) with the significance level set at 0.05. Researchers also controlled for sociodemographic variables according to the ANOVA model to detect possible influences or effects from confounding factors. Data were then analyzed with multiple regression method to identify the influence of environment on physical activity. We assessed the fit of the model and R-squared statistics. We also included sociodemographic variables in a multiple regression model as covariates to adjust for possible confounders.

3. Results 3.1. Sociodemographic variables The response rate of the sample was 72.2%. The average response rates of urban and rural areas were 70.3% and 74.2%, respectively. The minimum and maximum for the response rates for urban area schools were 60.6% and 88.5%, while for rural areas the rates were 67.0% and 78.5%. The final data analyzed consisted of 200 schoolchildren in 5 urban schools and 323 schoolchildren in 5 rural schools (total N= 523). In comparing contextual characteristics (Table 1), urban schoolchildren were more likely to be older and from families whose head of household had higher income and a higher educational achievement. Comparing the difference between missing cases and those who remained in the study, it was found that there was no difference in distribution for geographic location, (w2(1, N = 523) = 0.21, p =0.65) gender (w2(1, N = 523) = 0.08, p =0.78) and grade (w2(1, N = 523) =0.04, p = 0.85) of the students as well as educational level of the house head (w2(4, N = 523) =0.15, p =0.99)and household monthly income (w2(5, N = 523) =0.03, p =1.0). The researchers also conducted a goodness of fit chi-square analysis to compare gender and grade of the participants and the population in both urban and rural areas, and found no difference. For urban areas: w2gender (1, N = 523) = 0.00, p =1.00, w2grade (1, N = 523) = 0.72, p = 0.40; for rural areas: w2gender (1, N = 523) = 0.32, p =0.57, w2grade (1, N = 523) = 1.29, p = 0.26).

2.5. Procedures 3.2. Physical activity items Children’s activity levels were measured for four days in the fall term of 2004. The principals of the schools were contacted to gain approval to conduct the study. Following a brief set of instructions, students were asked to fill out the self-administered questionnaires. Over the next three days, each student completed the physical activity recall sheet which was collected each morning by the classroom teacher. The caretakers filled out the questionnaire and had the students take it back to school the next day. The completeness of the questionnaires was inspected upon collection. Questionnaire with missing data was given back for a second trial. Incomplete

The test–retest reliability of the items on the four daily recall logs was between 0.6–0.8. Urban and rural test–retest reliability was between 0.58–0.78 and 0.57–0.81, respectively, and was without difference. The items most often engaged in by the students were walking, followed by jogging, stair-climbing and chasing games. There was a difference between rural and urban students in activities such as bicycling and hiking. Twenty three percent of the city students compared with 53% of their rural counterparts rode bicycles frequently. (w2(1, N = 523) = 164.8, p= 0.000). As for hiking, 4.8% of urban respondents and 11.1% of

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Table 1 Sociodemographic comparison between urban and rural areas. Urban (n= 200)

w2

Rural (n =323)

Gender

Male Female

104 96

52.0% 48.0%

152 171

47.1% 52.9%

1.15 d.f. = 1, p =0.29

Grade

5th Grade 6th Grade

112 88

56.0% 44.0%

135 156

41.8% 48.2%

4.68 d.f. = 1, p = 0.03

Monthly household income

o NT$20,000 NT$20,000–40,000 NT$40,000–60,000 NT$60,000–100,000 4NT$100,000 Unknown

12 18 51 65 46 18

6.0% 9.0% 20.5% 32.5% 23.0% 9.0%

44 87 102 42 17 31

13.6% 26.9% 31.6% 13.0% 5.3% 9.6%

84.58 d.f. = 5, p =0.00

Educational level of head of household

Elementary school Junior high school Senior high school College and above Unknown

11 14 49 118 35

5.5% 7.0% 24.5% 59.0% 10.8%

65 104 97 8 22

20.1% 32.2% 30.0% 4.0% 6.8%

146.85 d.f. = 4, p =0.00

Table 2 Proportion of children who participated in various physical activities by area (%). Physical activity

Urban (n= 200)

Rural (n= 323)

Total (n = 523)

Walking (for exercise) Jogging Stair-climbing Playing chase Bicycling Basketball Tennis Rope-jumping Baseball Situps Gymnastics Hula hoop Swimming Football Hiking Tae Kwan Do Ice-skating Dancing Table tennis Bowling Volleyball Dumbbell lifting Snooker Rollerskating

75.0 57.2 56.8 45.9 22.6 22.6 12.7 12.3 10.3 9.9 9.2 7.5 6.8 5.1 4.8 4.5 3.8 3.1 3.1 2.7 2.4 2.1 2.1 0.7

71.9 60.9 54.5 43.2 53.4 26.9 11.5 17.0 7.7 10.2 6.3 11.5 4.1 7.5 11.1 5.2 5.0 6.3 3.2 3.8 4.3 2.5 2.5 2.0

73.2 59.4 55.4 44.3 41.1 25.2 12.0 15.1 8.7 10.1 7.5 9.9 5.2 6.5 8.6 4.9 4.5 5.0 3.1 3.4 3.5 2.3 2.3 1.5

rural respondents indicated that they engaged in this activity frequently (w2(1, N = 523) = 10.7, p =0.001) (Table 2).

3.3. The amount of physical activity The test–retest reliability of the amount of physical activity ‘‘between classes,’’ ‘‘outside school hours’’ and ‘‘on weekends and holidays’’ were 0.35, 0.91, and 0.75, respectively. A multi-level data analysis using the hierarchical liner regression was conducted to detect possible school clustering of students to avoid underestimating the standard errors. No difference was found in the amount of physical activity among students at different schools (ICC= 0.00022, p40.05), We then processed the data analysis with a complete case analysis. Comparing the urban and rural children as to the amount of physical activity they had between classes, after school and during holidays revealed that the students from urban areas had more physical activity after

Table 3 Children’s physical activity (MET h) in the last week by location Mean

S.D.

Variance

MSE

F-value

p-value

1. Between class Urban 13.53 Rural 13.90

5.51 6.08

Between Within

23.40 34.23

0.68

0.41

2. After school Urban 47.05 Rural 40.45

41.19 38.67

Between Within

6984.22 1579.34

4.37

0.04

3. During holidays Urban 28.17 Rural 21.71

21.45 17.16

Between Within

6544.26 365.36

17.91

0.00

4. Total amount Urban 89.00 Rural 78.25

56.54 52.99

Between Within

17511.00 2980.39

5.88

0.02

school, during holidays and on weekends than those from rural areas. There was also a difference in the total amount of physical activity (see Table 3). One important finding is that the difference between urban and rural children’s overall physical activity was not observed when controlled for monthly household income and the educational level of the head of household.

3.4. Perceived environment and its comparison between urban and rural areas Cronbach’s a of the walkability scale for the total group in this study was 0.73. For the urban and rural student groups, Cronbach’s a were 0.73 and 0.71, respectively, indicating very little difference between urban and rural areas. The sample was further stratified into groups based on students’ gender and grade, as well as on the educational level of the head of household and family monthly income, to detect any confounding factors of reliability in the walkability scale. Cronbach’s a were between 0.68 and 0.77 with only one exception at 0.59 for the head of household with an elementary school education, indicating that there were no confounding factors in the walkability index as these results were similar across subcategories. There was no significant difference in walkability between urban and rural areas in our study (F(1,522) = 2.49, p= 0.12). Cronbach’s a of the accessibility was 0.71. for urban, rural and total groups. The sample was further stratified into groups based

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on students’ gender and grade, as well as the educational levels of the head of household and family monthly income, to detect any confounding factors in the reliability of accessibility. The figures were between 0.65 and 0.74 with only one exception at 0.58 for a head of household with an elementary school education indicating that there were no confounding factors in the accessibility index as these results were similar across subcategories. There was a difference in accessibility of places for physical activity between the urban and rural areas, with urban children reporting better accessibility (F(1, 522) =80.30, po0.001). The researchers precluded the effect of confounding factors as the results remained unchanged after controlling for sociodemographic variables Table 4. 3.5. Association of environment to physical activity By examining skewness and kurtosis of the amount of physical activity, the researchers found that the data were suitable for using the statistic under the assumption of normal distribution. The skewness was between 0.38 and 1.36, which was less than the extreme value of 3; also, the kurtosis was between 0.43 and 2.29, which was far less than the extreme value of 10 (Kline, 1998). Furthermore, histogram of studentized residuals and normal probability plot of the total amount of physical activity residual also indicated that the assumption of normality was not violated. The variables of gender, grade, and geographic location in either rural or urban areas, education level of the head of household, monthly family income, walkability and accessibility were regressed on the total amount of physical activity. The significant variables were gender and accessibility. Location in urban or rural areas did not make a difference. Neither did the interactive term of the environmental variables by urban/rural variable. Other assumptions of multiple regression were also tested. Tolerance of the independent variables of the equation was around 0.9 and 0.8, except for the interactive terms, indicating that multicollinearity was not a problem. A scatter plot of the Table 4 Children’s perceptions of the environment. Mean

S.D.

Variance

MSE

F-value

1. Walkability. Urban. 57.91 Rural 56.76

8.86 8.97

Between Within

198.49 79.64

2.49

2. Accessibility Urban 7.04 Rural 4.96

2.90 3.12

Between Within

739.05 9.21

80.30

p-value

0.12

0.000

Table 5 Multiple regression of children’s total amount of physical activity (MET
h) in the past one week Model Male 6th Grade Urban Education Income Walkability Accessibility Walkability x urban/rural Accessibility x urban/rural Constant

B 15.73 7.62 63.01 2.50 .51 1.59 6.21 1.34 1.83 48.70

Std. Error

p

5.95 6.00 39.24 3.34 2.73 1.05 2.79 .69 2.03 47.11

0.01 0.21 0.11 0.45 0.85 0.13 0.03 0.37 0.05 0.30

Note: R2 = .081. Reference categories: Gender- Female; Grade-5th grade; Area- rural

residual against the predicted value was examined, and there was no indication of violation of homoscedasticity of residual. The variance explained by these variables was 8.1%. Additionally, the statistical power was greater than 0.995, confirming the adequacy of the sample size for these analyses Table 5.

4. Discussion The finding of a statistically significant difference in the amount and types of physical activity between urban and rural students indicates that residence plays a role in the physical activity of elementary school students. The urban students exercised more after school, during weekends and on holidays, and had a higher total amount of physical activity. Also, there was a difference in the types of physical activity students reported. The rural students bicycled and hiked more than urban students, which may be explained by the spacious rural area of their homes in proximity to the mountains. Our findings are consistent with studies from other countries. Loucaides et al. (2004) studied elementary school students and their parents in Cyprus and found that the urban areas had more facilities for physical activity and more gardens and spaces for activity than in the rural areas. Moreover, the rural students exercised more in the summer while those in urban areas exercised more in the winter, as the activities these urban children engaged in could be conducted indoors. These data suggest that intervention studies for promoting children’s physical activity need to take geographical factors into consideration. Also, in the present study urban schoolchildren exercised more after school, and on holidays and weekends. A possible reason may be that during these times urban schoolchildren used their school’s play facilities, but such facilities were not as accessible in rural areas. We also found that the urban/rural differences in amount of physical activity after school and during holidays disappeared when controlling for family socioeconomic variables. Rural children from more affluent families and those whose caretakers had a higher educational achievement showed less difference compared with their counterparts in urban schools. The finding that family’s socioeconomic status and parental educational background were associating health-promoting behavior was also seen in another study in Taiwan (Huang et al., 2003). It could be that families with higher socioeconomic status have better knowledge of where recreation facilities were located or they made better use of this equipment for exercise. This issue warrants further exploration in future research. Perceived accessibility was the only environmental variable that was associated with physical activity among both urban and rural students. This finding has been reported by others (Liu, 1998; Chen et al., 2007). The results were also consistent with the on-site observation and official figures. On average, every urban elementary schoolchild had 6.51 m2 for play including green land, garden, play space and facilities (National Statistics, 2007). In Holong Township there were few spaces specially designed for exercise and physical activity. Most of the students used natural areas for physical activity, such as the seashore, ponds, open fields or school playgrounds. The average space available for each rural student was 1.79 m2, much less than the space allotted to the urban school students (National Statistics, 2007). Evidently, the accessibility of physical activity facilities is much better in urban areas. Scholars in Western countries have also found that accessibility is associated with children’s physical activity. Timperio et al. (2004) found that children who reported a lack of parks or sports grounds near their home went on fewer walking and cycling trips; also, parents’ reports of few sporting arenas in the area had similar results, but only for girls. Carver et al. (2005)

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found that in Australia, parents’ reports of the presence of good sporting facilities nearby for their children were associated with higher self-reported walking or cycling among young adolescent girls and boys. Hume et al. (2005) also found that Australian girls who had a greater number of opportunities for physical activity in their neighborhood exhibited higher physical activity, specifically low-intensity physical activity. Similar results were found in England (Broderson et al., 2005) and the United States (Warburton et al., 2006). Pikora et al. (2003) found that higher physical activity levels were correlated with an environment’s aesthetic qualities, safety and convenience. However, the present study did not have similar findings. One of the reasons might be that physical activity measures did not match the environmental measures, e.g., neighborhood-specific physical activity associated with neighborhood walkability. The total amount of physical activity calculated included various activities of light, moderate and vigorous intensity; however, the NEWS captures mainly neighborhood environmental features supportive of walking, in particular utilitarian walking, rather than those for other types of physical activity or walking for exercise. Activities in addition to walking, such as jogging, chasing games, and bicycling, were popular among these schoolchildren in our study, but were not associated with the walkability of the environment. The other possible reason was that only ten communities participated in this study; furthermore, the rural communities were not far apart from each other. The variance caused by the differences in variables among these communities might not be large enough to be significant. The strength of this study was its sophisticated process of constructing walkability and accessibility measurements by conducting in-depth interviews and on-site observations of the environment. Integrating both the qualitative and quantitative data makes the tool adaptive to the cultural context, increasing its validity. Also, this was a pilot study exploring the influence of urbanization on children’s perceived environment and physical activity. We acknowledge several limitations in interpreting our study results. First, because of the cross-sectional data in this study, the temporal criterion of causality could not be met. For instance, greater accessibility may lead to greater use of facilities for physical activities; however, the possibility exists that a higher prevalence of physical activity in the population affects resource allocation and availability of facilities. Second, the sampling method does not meet the criterion of random sampling as only five schools from Taipei districts were selected. Also, the response rate varied to a greater extent among these urban schools compared with rural schools. Moreover, the variables of educational level of the head of household and household income were not tested for generalizability because of lack of data. Furthermore, as the test of the reliability of measurements on perceived environment revealed that the heads of household with lower educational attainment had lower internal consistency, educational level and family income might act as potential confounders and affect the robustness of the study. We dealt with this problem by incorporating sociodemographic variables into the independent variables in multivariate analysis; still, the representativeness of the sample needs to be taken into account when interpreting the results. Furthermore, lower internal consistency reliability was found on measures of the perceived environment among heads of household with lower educational attainment, indicating a potential measurement effect associated with education. Sociodemographic variables, therefore, were included as covariates in the multivariate analysis. Third, in a clustered sample, the standard errors could have been underestimated, thereby leading to spurious significant findings. Although the among-school variation was not statisti-

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cally significant, interpretation of findings must consider that the sample was not random. Fourth, the study measured subjective perceptions of the built environment rather than objectively assessing the environment; however, perceptions may in fact be more likely to motivate behavior than the objective environmental attributes. Finally, it was not possible to determine the precise intensity levels for some activities, particularly as some are traditional Chinese physical activities for which energy expenditure is unknown. The energy cost of walking (light), brisk walking (moderate), and running (vigorous) were chosen to represent three levels of physical activity, and the assumption was made that close approximations for our population. Walking, running, stair-climbing/walking, and playing chase were the four most frequently reported forms of physical activity (44–73% of total), and their associated METs were age-adjusted. There was no significant difference between boys and girls within an age group. However, the research team did not assess the puberty stages of Taiwanese children, which may introduce a slight underestimation as the maturity of Asians in general lags behind Caucasians and energy expenditure decreases proportionally with maturity. Despite these limitations, this study adds important insights to an emerging literature on the differences between rural and urban settings and provides a rationale for continued investigation into the interplay of environment and physical activity. The study also highlights the importance of a focus on environmental variables at different urbanization levels in designing intervention programs.

4.1. Implications As in other developed countries, Taiwan has faced the trend of increasing urbanization in recent decades. Unbalanced distribution of resources among different areas of the country has become common over these years. Physical activity as one important part of a healthy lifestyle is affected by increasing urbanization and inequitable distribution of resources that support an active lifestyle. A survey conducted by Common Health magazine in Taiwan (2003) to review the most physical activity-friendly county in Taiwan concluded that environment played an important role in a county’s evaluation. Less developed and remote counties were strong in natural environment, while cities such as Taipei were advantageous in having a build of exercise facilities and equipment (Lee and Hsu, 2003). Data from the present study indicates that not only availability but also accessibility is significant. Children in city schools reported greater accessibility of resources for physical activity than did rural children, and their total amount of physical activity was higher than those living in rural schools. The rural children reported less physical activity after school, on holidays and on weekends, reflecting the scarcity of facilities for physical activity in rural areas. The study confirms that perceived accessibility to facilities and places for physical activity has a significant correlation with children’s physical activity. Attention to accessibility issues may improve the effectiveness of intervention programs aimed at promoting children’s physical activity levels. Interventions should also take geographic factors into consideration. In the present study city schoolchildren are more likely to feel that sports facilities and equipment are convenient for them to use. Tailored interventions are likely to be more effective than intervention programs that target the same variables in all subgroups of the population. Intervention components need to be specifically designed to make the most of the existing spatial environment. The resources have to be prioritized to disadvantaged children in

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rural areas since they have the lowest accessibility to exercise facilities. As the study focused only on 5th and 6th grade students in elementary school, future studies can extend to different age groups and include more communities throughout the country for a more representative sample and larger variance. More objective measurement of environmental variables, such as assessment with Geographic Information Systems and the Global Positioning Systems, can be employed to improve the reliability of measurement. Such objective measures provide a comparison for subjective perceptions of the environment and allow for an exploration of the relative importance of objective versus subjective environmental measures in predicting physical activity among children. Also, future testing of accessibility and walkability with longitudinal data could increase our understanding of the most effective strategies to increase physical activity among Taiwanese children. Other categories of variables such as social environment should also be included to explore the relationship between the physical and social environments. References Andersen, L.B., Harro, M., Sardinha, L.B., Froberg, K., Ekelund, U., Brage, S., Anderssen, S.A., 2006. Physical activity and clustered cardiovascular risk in children: a cross-sectional study (The European Youth Heart Study). The Lancet 368, 299–304. Biddle, S.J., Gorely, T., Stensel, D.J., 2004. Health-enhancing physical activity and sedentary behaviour in children and adolescents. Journal of Sports Sciences 22, 679–701. Bogaert, N., Steinbeck, K.S., Baur, L.A., Brock, K., Bermingham, M.A., 2003. Food, activity and family-environmental vs. biochemical predictors of weight gain in children. European Journal of Clinical Nutrition 57, 1242–1249. Broderson, N.H., Steptoe, A., Williamson, S., Wardle, J., 2005. Sociodemographic, developmental, environmental, and psychological correlates of physical activity and sedentary behavior at age 11 to 12. Annals of Behavioral Medicine 29, 2–11. Carver, A., Salmon, J., Campbell, K., Baur, L., Garnett, S.C.D., 2005. How do perceptions of local neighborhood relate to adolescents’ walking and cycling? American Journal of Health Promotion 20, 139–147. Caspersen, C., Nixon, P., Duran, R., 1998. Physical activity epidemiology applied to children and adolescents. Exercise and Sport Sciences Reviews 26, 341–403. Chen, L., Haase, A.M., Fox, K.R., 2007. Physical activity among adolescents in Taiwan. Asia Pacific Journal of Clinical Nutrition 16, 354–361. Cunningham, G.O., Michael, Y.L., 2004. Concepts guiding the study of the impact of the built environment on physical activity for older adults: A review of the literature. American Journal of Health Promotion 18, 435–443. Damore, D.T., 2002. Preschool and school age activities: Comparison of urban and suburban populations. Journal of Community Health 27, 203–211. Davison, K.K., Lawson, C.T., 2006. Do attributes in the physical environment influence children’s physical activity? A review of the literature. The International Journal of Behavior Nutrition and Physical Activity 3, 19. Felton, G.M., Dowda, M., Ward, D.S., Dishman, R.K., Trost, S.G., Saunders, R., Pate, R.R., 2002. Differences in physical activity between black and white girls living in rural and urban areas. Journal of School Health 72, 250–255. Frank, L.D., Engelke, P.O., Schmid, T.L., 2003. In: Health and Community Design: The Impacts of the Built Environment on Physical Activity. Island Press, Washington, D.C. Garcia, A.W., George, T.R., Coviak, C., Antonakos, C., Pender, N.J., 1997. Development of the child/adolescent activity log: A comprehensive and feasible measure of leisure-time physical activity. International Journal of Behavioral Medicine 4, 323–338. Harrell, J.S., McMurray, R.G., Baggett, C.D., Pennell, M.L., Pearce, P.F., Bangdiwala, S.I., 2005. Energy costs of physical activities in children and adolescents. Medicine and Science in Sports and Exercise 37, 329–336.

Heath, G.W., Brownson, R.C., Kruger, J., Miles, R., Powell, K.E., Ramsey, L.T., The Task Force on Community Preventive Service, 2006. The effectiveness of urban design and land use and transportation practices to increase physical activity: A systematic review. Journal of Physical Activity and Health 3, S55–S76. Huang, S., Chiang, I., Lai, H., Hung, W., Chen, H., Chiu, Y., Yuan, B., 2003. The health behavior and related sociodemographic factors of the elementary schoolchildren in Taiwan. Journal of Health Education, 155–177 in Chinese. Hume, C., Salmon, J., Ball, K., 2005. Children’s perceptions of their home and neighborhood environments, and their association with objectively measured physical activity: a qualitative and quantitative study. Health Education Research 20, 1–13. Humpel, N., Owen, N., Leslie, E., 2002. Environmental factors associated with adults’ participation in physical activity: A review. American Journal of Preventive Medicine 22, 188–199. Joens-Matre, R.R., Welk, G.J., Calabro, M.A., Russell, D.W., Nicklay, E., Hensley, L.D., 2008. Rural–urban differences in physical activity, physical fitness, and overweight prevalence of children. The Journal of Rural Health 24, 50–54. Kline, P., 1998. The New Psychometrics: Science, Psychology, and Measurement. Routledge. Lee, Y., Hsu, F., 2003. Which city has the most friendly exercise environment in Taiwan? Common Health Magazine 6, 141–159. Liu, J., 1998. The influence of gender, grade, life-style on physical activity of elementary school children. Peing-dong Technology University Bulletin 7, 81–91. Loucaides, C.A., Chedzoy, S.M., Bennett, N., 2004. Differences in physical activity levels between urban and rural school children in Cyprus. Health Education Research 19, 138–147. National Statistics, 2007. Statistical yearbook of the Republic of China. National Statistics, Taipei. Owen, N., Cerin, E., Leslie, E., duToit, L., Coffee, N., Frank, L.D., Bauman, A.E., Hugo, G., Saelens, B.E., Sallis, J.F., 2007. Neighborhood walkability and the walking behavior of Australian adults. American Journal of Preventive Medicine 33, 387–395. Pikora, T., Giles-Corti, B., Bull, F., Jamrozik, K., Donovan, R., 2003. Developing a framework for assessment of the environmental determinants of walking and cycling. Social Science & Medicine 56, 1693–1703. Pratt, M., Macera, C., Blanaton, C., 1999. Levels of physical activity and inactivity in children and adults in the United States: current evidence and research issues. Medicine and Science in Sports and Exercise 68, 241–248. Saelens, B.E., Sallis, J.F., Frank, L.D., 2003. Environmental correlates of walking and cycling: findings from the transportation, urban design, and planning literatures. Annals of Behavioral Medicine 25, 80–91. Sallis, J.F., Owen, N., 1999. In: Physical Activity & Behavioral Medicine. SAGE publications, CA. Sallis, J.F., Prochaska, J., Taylor, W., 2000. A review of correlates of physical activity of children and adolescents. Medicine and Science in Sports and Exercise 32, 963–975. Sirard, J.R., Ainsworth, B.E., Mclver, K.L., Pate, R.P., 2005. Prevalence of active commuting at urban and suburban elementary schools in Columbia, SC. American Journal of Public Health 95, 236–237. Taiwan Department of Health, 2006, Survey on Health Behaviors of Junior High School Students. Taipei, Taiwan. Taiwan Sport Affairs Council, 2009. 2008 Sport Statistics of Republic of China. Taipei, Taiwan. Timperio, A., Crawford., D., Telford., A., Salmon., J., 2004. Perceptions about the local neighborhood and walking and cycling among children. Preventive Medicine 38, 39–47. Troiano, R.P., Berrigan, D., Dodd, K.W., Masse, L.C., Tilert, T., McDowell, M., 2008. Physical activity in the United States measured by accelerometer. Medicine and Science in Sports and Exercise 40, 181–188. Tseng, K.H., Wu, T.Y., 1986. Characteristics of urbanization level in Taiwan districts. Geographical Research 12, 287–323. Warburton, D.E.R., Nicol, C.W., Bredin, S.S.D., 2006. Health benefits of physical activity: the evidence. Canadian Medical Association Journal 174, 801–809. World Health Organization, 2007. Process of translation and adaptation of instruments: The current Situation Retrieved October 9, 2007 from /http:// www.who.int/susbstance_abuse/research_tools/translation/en/index.htmlS. Wu, T.Y., Pender, N., 2002. Determinants of physical activity among Taiwanese adolescents: An application of the health promotion model. Research Nursing Health 25, 25–36.