Health Education Research, Vol. 19, No. 2, 138-147,
April 1, 2004
© 2004 Oxford University Press
Differences in physical activity levels between urban and rural school children in Cyprus
1 School of Education and Lifelong Learning, University of Exeter, Exeter EX1 2LU, UK 2 Correspondence to: C. A. Loucaides, 77 Larnaca Avenue, Aglanjia 2102, Nicosia, Cyprus. e-mail: conlou{at}avacom.net
 |
Abstract |
|---|
 Top Abstract IntroductionMethodsResultsDiscussionReferences |
|---|
This study attempted to examine differences in physical activity levels between urban and rural primary school children. The sample consisted of 256 Greek-Cypriot children and their parents from two schools representing urban areas and three schools representing rural areas. Children’s activity levels were assessed for 4 weekdays in the winter and for 4 weekdays in the summer using a pedometer (DW-200; Yamax, Tokyo, Japan). Daily step counts were used to describe children’s activity levels. Parents completed a questionnaire assessing environmental variables in both seasons. Two-way ANOVAs indicated that urban school children were significantly more active in winter than rural school children (means = 13 583 ± 4313 versus 12 436 ± 3610, P < 0.001) and that rural school children were significantly more active in the summer (means = 16 450 ± 5134 versus 14 531 ± 4901, P < 0.001). Parents of children in rural schools reported more space available in the garden and in the neighbourhoods, and safer neighbourhoods than parents of children in urban schools, whereas children in urban schools had more exercise equipment available at home and were transported more frequently to places where they could be physically active. Results of this study suggest that intervention programmes to promote physical activity need to consider seasonal and geographical location differences in physical activity levels.
|
Introduction |
|---|
|
TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
There is now substantial research evidence indicating that physical activity during adulthood produces a number of major health benefits. A consensus committee concluded that physical activity is associated with a reduction in all-cause mortality, fatal and non-fatal total cardiovascular disease, and a reduction in the incidence of obesity, Type 2 diabetes mellitus, colon cancer and osteoporosis (Kesaniemi et al., 2001
). Recent reviews also indicated that physical activity can have beneficial effects on young people’s psychological well-being, self-esteem, overweight and obesity, and chronic disease risk factors (Cavill et al., 2001). The most consistent association seems to exist between physical activity and body fatness (Twisk, 2001). The role of physical activity on the reduction of obesity is becoming of increasing importance, as obesity has emerged as the most common paediatric chronic illness in Western countries (LeMura and Maziekas, 2002). Despite the evidence linking physical activity with health, sustained periods of moderate to vigorous physical activity are not characteristic of young peoples’ physical activity patterns (Armstrong et al., 1996; Sallis et al., 2000) and activity levels seem to decline after the age of 12 (Pratt et al., 1999). Nevertheless, more recent evidence seems to suggest that young people may be more active when cumulative activity and less stringent intensity thresholds are considered (Sleap and Tolfrey, 2001).
In order to develop effective intervention programmes 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 behaviour influenced by psychological, social, environmental and demographic variables (Welk, 1999). Environmental variables are particularly important in studying physical activity since this behaviour needs to take place in appropriate settings with available space. This category is especially important for children as parents typically control access to play spaces (Sallis et al., 1997). Furthermore, environmental variables in potential interventions to increase physical activity hold particular promise as they are designed to influence large groups of people (Sallis et al., 1998). However, environmental variables are among the least-studied correlates of physical activity (Sallis et al., 2000), although a number of studies have identified associations between children’s physical activity and environmental variables.
In a study of 40, 6- to 8-year-old Hong Kong children (Johns and Ha, 1999), limited availability of outdoor play areas during the afternoon hours resulted in children spending 72.4% of their time sitting and lying down, and only 10.4% of their time being active. Time and frequency spent playing outside were significant correlates of physical activity in studies of 4-year-old children (Baranowski et al., 1993; Sallis et al., 1993). In the latter study, 25% of the variance in physical activity was explained by demographic, social and environmental variables, and in the former study gender, month and location (inside and outside the house) explained 75% of the variance in physical activity. Time spent outdoors and availability of space in close proximity may be especially important for younger children, as they need to depend on other people for their transportation to places where they can be physically active. For example, Sallis et al. (Sallis et al., 1992, 1999) found that parents transporting their children to exercise facilities was a variable significantly associated with physical activity.
Variables such as participation in sports clubs (Trost et al., 1997), availability of exercise equipment at home (Stucky-Ropp and DiLorenzo, 1993; Pate et al., 1997) and television watching (Pate et al., 1997) have also been found to be significant correlates of physical activity in primary school children. In the study by Trost et al. (Trost et al., 1997), community sports team participation accounted for 10 and 6% in girls’ and boys’ moderate to vigorous activity participation, respectively, and psychological variables including self-efficacy and beliefs regarding activity outcomes added 7 and 11% of the variance in gender-specific analyses. These studies clearly indicate that a host of factors are related to physical activity participation.
The study of the determinants of physical activity is also important as subgroups of children that are physically inactive may be targeted for special interventions. For example, recent reviews have indicated that girls need to be targeted for special intervention programmes as they are consistently found to be less active than boys (Sallis et al., 2000; Cavill et al., 2001). Other subgroups of children within a population may also be at risk of being physically inactive, e.g. children coming from urban or rural areas. According to Pratt et al. (Pratt et al., 1999), geographical differences in physical activity is an area where much additional work is needed. In a recent review of correlates of physical activity, Sallis et al. (Sallis et al., 2000) argued that inconsistent evidence exists regarding milieu and its association with physical activity, and suggested that this variable should be subjected to more detailed study. To provide further evidence in this area, this study attempted to examine urban versus rural differences in physical activity levels in primary school children. Additionally, differences in a number of environmental variables were assessed between the two locations.
|
Methods |
|---|
|
TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
Participants
The sample consisted of 256 Greek-Cypriot primary school children (aged 11–12) and their parents (mother or father). Children came from two urban schools (n = 144, boys = 73 and girls = 71) and from three rural schools (n = 112, boys = 56 and girls = 56). The selection of the schools was based on the diverse geographical areas of the island. One of the urban schools came from the capital of Cyprus, Nicosia, that is inland and the other from Limassol, a town located on the south coast. The three village schools represented typical locations where villages are found on the island, including a school in the mountains, a school inland and a school close to the coast. Informed-consent forms were obtained from both the parents and the children prior to participation. This paper reports results from a larger study conducted on children’s activity levels and determinants of activity in Cyprus.
Measures
Measurement of physical activity
The Yamax Digiwalker (DW-200; Yamax, Tokyo, Japan) pedometer was used as a measure of physical activity. Pedometers provide an objective indicator of step counts, a marker of total volume or duration of activity (Welk et al., 2000). One of the main advantage of these devices is that their objectivity and low cost makes them suitable for use in large population studies (Sirard and Pate, 2001). In a study with a sample of 30 English boys and girls aged 8.2–10.8 years (Eston et al., 1998), the correlation between the Yamax DW-200 when worn on the hip and oxygen uptake with respect to a range of activities (walking, running, hopping, catching, and sitting and crayoning) was r = 0.81. When only unregulated play activities (hopping, catching, and sitting and crayoning) were considered together, the correlation between the hip pedometer and energy expenditure increased to r = 0.92. Similar results were also obtained in another study in Hong Kong adopting the same methods with Chinese boys aged 8–10 (Louie et al., 1999), with correlations ranging from r = 0.86 to r = 0.93 for all activities combined and for unregulated play activities, respectively. Findings from these studies suggest that, when only total activity is of interest, the inexpensive pedometer provides an accurate alternative for objectively assessing children’s activity levels (Eston et al., 1998; Louie et al., 1999).
Measures of environmental variables
Items were written for parents of children to assess a number of environmental and social variables based on the existing literature. Possible responses and the suitability of the items were established in two pilot studies conducted with both children and their parents. In a first pilot study, focus group interviews with children were used to establish the suitability of responses with regard to the range of hours per day and the frequency per week they engaged in different activities. In a second pilot study, questionnaires were given to parents in order to ascertain the suitability of the items and of the possible responses. Items assessing the child’s use of afternoon time included: (1a) time spent watching television, (1b) time spent playing video games and (1c) time spent playing outside. Responses for these six-point scale items ranged from ‘0 hours per day’ to ‘4 hours plus per day’. Two more variables measured the number of times that children attended sports clubs during the week (2a), as well as the number of times per week they attended private sedentary lessons after school (2b). In Cyprus, as school finishes at 13:05, children attend a number of lessons in the afternoon such as music and foreign languages. Responses for these six-point scale items were from ‘0 times per week’ to ‘4 times plus per week’. Two further items, using the same scale, assessed the number of times per week that parents transported their children to places where they can engage in physical activity (2c) and the number of times per week that they spent with their children engaging in physical activity (2d). An additional item assessed the number of different exercise equipment available at home (3a).
Three additional items assessed the physical characteristics of the garden and neighbourhood. Two items assessed the space available in the garden (4a) and in the neighbourhood (4b). These items were scored on a four-point scale ranging from ‘no space to be active’ to ‘a lot of space to be active’. The third item assessed the safety of the neighbourhood (4c). The four-point scale of this item ranged from ‘very unsafe’ to ‘very safe’.
Procedures
Children’s activity levels were measured for 4 weekdays in the winter (January and February) and for 4 weekdays in the summer (May and June). Children fitted the pedometers in the midline of the thigh at waist level, either on the left or the right, as research has shown that it does not matter in which side of the body they are fitted (Bassett et al., 1996). Children wore the pedometers from the morning when they woke up, until the evening when they went to bed. They were also given a recording sheet in order to record the pedometer-derived step counts by the end of the school day (13:05) and for the entire day (before they went to bed). At the start of a new day children reset the pedometers to zero.
Questionnaires were also given to children’s parents in order to assess potential differences in environmental factors between urban and rural schools. Parents completed the questionnaires in both summer and winter, in order to account for potential seasonal differences. The summer version of the questionnaire did not include the items assessing the physical characteristics of the neighbourhood and garden, the different sports equipment available at home, and the number of times per week children attended private lessons. Changes in these variables were not expected to take place during the summer measurement.
Analyses
Analyses were conducted using SPSS (version 9). The total daily step counts over the 4 days were averaged to obtain children’s physical activity scores. Raw pedometer data (total daily step count) is the most accurate descriptor of ambulatory activity obtained from pedometers (Tudor-Locke and Myers, 2001). Means and SDs were calculated for total steps per day (composite score of the 4 days) as well as for the environmental variables. One-week test–re-test reliabilities using one-way intra-class correlation coefficients were conducted for the variables assessing environmental differences in a subsample of 32 parents. Intra-class correlation coefficients were also calculated for the 4 days of pedometer data to estimate whether this monitoring frame provides reliable estimates of children’s physical activity (Tudor-Locke and Myers, 2001). Data are presented only for weekdays, as weekend days are less structured and therefore children’s activities are likely to differ (mainly because children do not attend school or other after school activities). In order to examine possible differences in the means of the variables between urban and rural schools, two-way ANOVAs 2 (school location) x 2 (season) and t-tests were employed. Firstly, a two-way ANOVA (school location x season) with repeated measures on the second factor was calculated to assess differences in physical activity levels between urban and rural areas. Secondly, six two-way ANOVAs (school location x season) with repeated measures on the second factor were performed to assess differences in the means of the environmental variables measured in both winter and summer. Finally, five independent sample t-tests were conducted to test for differences in the means of the environmental variables assessed only in winter. Because of the multiple tests conducted, significance levels for all analyses were set at P < 0.01, rather than the less conservative level of P < 0.05, in order to reduce the possibility of Type I error.
|
Results |
|---|
|
TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
Reliability coefficients of the environmental variables ranged from 0.79 for the weekly frequency that parents exercised with their child, to 0.99 for the weekly frequency of private lessons (see Table I). The 4 days of pedometer data in winter and summer provided a reliability of 0.74 and 0.69, respectively. Descriptive statistics for daily step counts for urban and rural schools over the two seasons are shown in Table II. The smaller sample shown in Table II in comparison to the sample participating in this study was due to the fact that not all children had complete data for both measurement periods. The two-way ANOVA revealed a significant main effect for season, F(1, 210) = 75.26, P < 0.001, but failed to reveal a significant main effect for school location. However, the interaction between season and school location was significant, F(1, 210) = 28.73, P < 0.001. An inspection of the means (see Table II) indicated that in winter, children in urban schools obtained more mean daily step counts than children in rural schools (mean difference = 1147). However, in summer, children in rural schools obtained more mean daily step counts than urban school children (mean difference = 1919).
|
|
Independent samples t-tests conducted on the means of the variables measured only in winter (see Table III for means, SDs and significance levels), revealed that urban school children had significantly higher means on the variables assessing private lessons attendance (means = 2.9 versus 1.9) and number of exercise equipment available at home (means = 3.5 versus 3.0). On the other hand, rural school children had significantly higher means on the variables assessing space in the garden (means = 3.2 versus 2.6) and in the neighbourhood (means = 3.1 versus 2.7) and safety in the neighbourhood (means = 3.0 versus 2.7).
|
The two-way ANOVA conducted on the variable assessing television watching failed to reveal a significant main effect for school or for season. The interaction between school and season was also not significant. Mean daily hours of television watching between urban and rural schools were similar for both seasons (see Table IV). Looking at differences in the variable assessing hours per day playing video games, a significant main effect was revealed for school location, F(1, 188) = 16.01, P < 0.001, but the main effect for season, and the interaction between school location and season were not significant. Inspecting the means, children in the urban schools spent more time playing video games than children in the rural schools over winter (means = 0.6 versus 0.3) and summer (means = 0.5 versus 0.3).
|
Examining the variable assessing time spent outside, a significant main effect was revealed for season, F(1, 188) = 43.39, P < 0.001 and for school location, F(1, 188) = 14.29, P < 0.001. The interaction was not significant, F(1, 188) = 6.02, P < 0.05. Inspecting the mean scores (see Table IV), children in the rural schools spent significantly more time outside than children in the urban schools in both winter (means = 1.5 versus 1.1) and summer (means = 2.2 versus 1.4). In addition, children in both urban and rural schools spent significantly more time outside playing in the summer in comparison to winter. Neither the main effects for school location and season nor the interaction between the two were significant in the variable measuring frequency of sports club attendance per week. However, there was a significant main effect for school location on the variable measuring times per week that parents transported their children to places where they can be physically active, F(1, 188) = 18.08, P < 0.001. Parents of children in the town schools reported transferring their children significantly more frequently to places where they can be physically active than parents of children in the village schools for both winter (means = 1.4 versus 1.0) and summer (means = 1.4 versus 0.6). None of the main effects for season and school location, or the interaction between the two, were significant for the variable assessing the times per week that parents exercised with their children.
|
Discussion |
|---|
|
TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
The finding of a significant interaction between season and geographical location indicates both factors play a role in children’s activity levels. Whereas children from both locations were more active during summer than during winter, children in urban schools were more active in the winter and children in rural schools were more active in the summer. A study conducted in the US with 3- to 4-year-old children also indicated that physical activity levels differ by the time of the year, with children being more active during autumn and early winter (Baranowski et al., 1993
). These differences may also be expected in Cyprus, as when this study was conducted there was a mean difference of about 24°C between winter and summer. In addition, daylight difference between the two periods was about 2 hours. Interestingly, urban school children were more active than rural school children in the winter, but this finding was reversed in the summer. These data suggest that intervention studies attempting to promote physical activity levels among children need to consider not only the time of the year, but also the geographical location.
A number of environmental variables were found to be significantly different between urban and rural schools. Children in rural schools were found to spend significantly more time outside than children in urban schools over the two seasons. This finding is further enhanced by the significantly more space available to rural school children in both the garden and the neighbourhood, as well as the safer neighbourhood reported by parents. It may be assumed that more space available and safer neighbourhood characteristics are factors that help children spent more time outside playing. This is also supported in the study of Hong Kong children (Johns and Ha, 1999), where lack of space adjacent to the home environment restricted children’s playtime outdoors and thus their activity levels. Parental reports of availability of play spaces as well as frequency and time spent outside were also significant correlates of children’s physical activity levels in a study of 4-year-old children (Sallis et al., 1993). Furthermore, one of the primary factors in helping parents choose a play space for their pre-school children was safety in a study of 5-year-old children (Sallis et al., 1997). Although these studies were conducted on younger children than the ones in the current study, time spent outside, space availability and safety may be important characteristics for physical activity participation for older children as well.
Urban school children also exhibited higher means on a number of environmental variables. As reported by parents, children in urban schools had more exercise equipment available at home, and were more frequently transported to places where they could be physically active in both summer and winter. These variables were found to be significantly associated with physical activity in a number of studies. In a study of Grade 5 and 6 children (Stucky-Ropp and DiLorenzo, 1993), number of exercise-related equipment at home added 2% of the variance in girls’ physical activity, and in a study of rural Grade 5 children high active students, based on moderate activity, had more exercise equipment at home than students classified as low active (Pate et al., 1997). Parent transportation to places where children can engage in physical activity or sports was also associated with children’s physical activity in a cross-sectional study (Sallis et al., 1992), and in a prospective study of Grade 4 and 5 children (Sallis et al., 1999). Children in urban schools also attended more private lessons not involving physical activity after school and engaged in more time playing video games than rural school children in both winter and summer. No differences were found between the two locations on time spent watching television, frequency of sports club attendance and the frequency that parents exercised with their child. It is worth noting the low means (see Table IV) on the variable assessing weekly frequency that parents exercised with their child. This may suggest that a way to increase children’s activity levels might be to encourage parents to spend more time engaging in physical activity with their children. Evidence from a recent study (Sallis et al., 1999) indicated that whether parents played with the child was the only variable associated with boys’ physical activity at both baseline measurements and change scores, 20 months later.
Considering the safer neighbourhood characteristics, the more space available and the more time that children in rural schools spent outside playing, suggests that villages provide more physical activity friendly environments. Bearing in mind that time spent outdoors is a consistent correlate of children’s physical activity (Sallis et al., 2000), the results of this study, indicating that urban school children are more active in the winter than rural school children, is interesting. However, a number of factors need to be considered in interpreting these findings. First, villages are usually deprived of sports club facilities in comparison to towns. Whereas significant differences were not found in sports club attendance between the two locations, the means were higher for urban school children in both winter and summer (means = 1.7 versus 1.4). In villages there is usually a single sports club and all children attend this club, whereas in towns a plethora of sports clubs are available offering varied activities. Thus children in towns are more likely to participate in a sports club of their choice. This finding may partly account for the higher physical activity levels in urban school children in winter, given that sports club attendance is a consistent correlate of physical activity (Sallis et al., 2000). The finding that parents in towns transported their children to places where they can be physically active more frequently perhaps reflects the longer distances in the towns. Second, children in the towns were found to have more exercise equipment at home and thus were more likely to engage in physical activity when at home during winter. Third, limitations of the pedometer in assessing physical activity should also be considered.
Whereas pedometers are sensitive to walking behaviours or ambulatory activity (Bassett, 2000; Tudor-Locke and Myers, 2001) and they provide a useful indicator of daily step counts (Welk et al., 2000), they are not sensitive to changes in speed, and would under predict physical activity levels for activities such as bicycling and swimming (Welk et al., 2000; Bassett et al., 2000). Thus, pedometers do not provide information as to the intensity of the activity. In a recent study, physical activity energy expenditure obtained by pedometers underestimated physical activity energy expenditure obtained by doubly labeled water by 59% (Leenders et al., 2001). It may thus be argued that ambulatory activity measured by pedometers could not account for the range and intensity of activities engaged in by the children during this study. Nevertheless, because of their low cost (Leenders et al., 2001) and ease of administration, pedometers offer considerable promise for assessing daily physical activity patterns (Eston et al., 1998; Welk et al., 2000). More days of assessing physical activity could also provide a more complete insight into this sample’s activity behaviour; however, the 4 days of assessment in each season seem to provide acceptable monitoring frame based on reliability coefficients obtained in this study and in other studies (Vincent and Pangrazi, 2002).
To illustrate the significance of pedometer derived data, it would be interesting to compare the mean daily steps of children in this study with expected step counts for 8- to 10-year-old children. According to Tudor-Locke and Myers (Tudor-Locke and Myers, 2001), children of this age should be expected to attain between 12 000 and 16 000 steps per day. Looking at the percentages of children from the two locations that attained 14 000 steps per day (the average value of 12 000 and 16 000 steps per day), 46 and 33% of urban and rural school children, respectively, attained this value for winter. For the summer, 42% of urban school children and 69% of rural school children attained the value of 14 000 steps per day. Whereas the number of children attaining this value is disappointingly low, these expected values are preliminary and the optimal number of steps per day to produce various health benefits awaits additional empirical evidence (Tudor-Locke and Myers, 2001). Thus, caution should be exercised when interpreting these findings.
Looking at the summer measurement, the large increase in activity levels of rural school children may be attributed to the more favourable weather conditions during this time of the year. Whereas children in both locations increased their physical activity levels from winter to summer, the increase in urban schools was 7% in comparison to 32% in rural schools (see Table II). The larger and safer spaces available in rural communities, and the more time that these children spent outside, were perhaps more salient determinants of activity than the more exercise equipment available at home and the wider choice of sports clubs in the towns. Interestingly, sports club attendance was significantly associated with pedometer counts only in winter (r = 0.29, P < 0.001), whereas hours spent outside playing were significantly associated with pedometer counts in both winter (r = 0.20, P < 0.01) and summer (r = 0.26, P < 0.001). Furthermore, children in the towns spent more time in sedentary activities such as video game playing and private lessons attendance. Extensive homework obligations and private lessons attendance with an average of 3 times per week (see Table III) take children away from physically active pursuits. This is indicated by the negative correlation obtained between private lessons attendance and pedometer counts in the summer (r = –0.26, P < 0.001). Findings from this study suggest that intervention studies are likely to be more effective in the Cypriot context if factors such as creation of safe playground areas in towns and the creation of sports clubs in the villages are considered. Whereas the goal should be to apply intervention studies to increase physical activity levels all year round, these data suggest that rural school children’s activity levels may be especially targeted in the winter and urban school children’s activity targeted in the summer.
This study has confirmed that time spent outdoors playing, access to safe and spatial environments, and sports club attendance are variables that may improve the effectiveness of intervention programmes to promote activity levels in children. Most importantly, it has provided evidence that physical activity interventions should not only account for seasonal variations in physical activity, but also consider how levels of children’s activity from different geographical locations are affected throughout the year. These interventions are likely to be more effective than intervention programmes that target the same variables in all subgroups of the population. As the literature suggests that research in this area is limited, more evidence from other cultural contexts is needed to support the findings of the present study. Furthermore, as this study examined only environmental influences in physical activity, other studies should include variables from other categories including social and psychological influences.
|
Acknowledgements |
|---|
Special thanks are due to Karen Walshe for help with preparation of this manuscript. We are grateful to the head teachers, teachers and children of Akropoli, Chirokoitia, Kyperounta, Laniteio and Yermasogeia Primary Schools for participating in the present study.
|
References |
|---|
|
TopAbstractIntroductionMethodsResultsDiscussionReferences |
|---|
Armstrong, N., McManus, A., Welsman, J. and Kirby, B. (1996) Physical activity patterns and aerobic fitness among prepubescents. European Physical Education Review, 2, 19–29.[CrossRef]
Baranowski, T., Thompson, W., DuRant, R., Baranowski, J. and Puhl, J. (1993) Observations on physical activity in physical locations: age, gender, ethnicity and month effects. Research Quarterly for Exercise and Sport, 64, 127–133.[ISI][Medline]
Bassett, D. (2000) Validity and reliability issues in objective monitoring of physical activity. Research Quarterly for Exercise and Sport, 71, 30–36.
Bassett, D., Ainsworth, B., Leggett, S., Mathien, C., Main, J., Hunter, D. and Duncan, G. (1996) Accuracy of five electronic pedometers for measuring distance walked. Medicine and Science in Sports and Exercise, 28, 1071–1077.[ISI][Medline]
Bassett, D., Cureton, A. and Ainsworth, B. (2000) Measurement of daily walking distance—questionnaire versus pedometer. Medicine and Science in Sports and Exercise, 32, 1018–1023.[ISI][Medline]
Caspersen, C., Nixon, P. and DuRant, R. (1998) Physical activity epidemiology applied to children and adolescents. Exercise and Sport Sciences Reviews, 26, 341–403.[ISI][Medline]
Cavill, N., Biddle, S. and Sallis, J. (2001) Health enhancing physical activity for young people: statement of the United Kingdom Expert Consensus Conference. Pediatric Exercise Science, 13, 12–25.
Eston, R., Rowlands, A. and Ingledew, D. (1998) Validity of heart rate, pedometry and accelerometry for predicting the energy cost of children’s activities. Journal of Applied Physiology, 84, 362–371.
Johns, D. and Ha, A. (1999) Home and recess physical activity of Hong Kong children. Research Quarterly for Exercise and Sport, 70, 319–323.[ISI][Medline]
Kesaniemi, Y., Danforth, E., Jensen, M., Kopelman, P., Lefebvre, P. and Reeder, B. (2001) Dose–response issues concerning physical activity and health: an evidence-based symposium. Medicine and Science in Sports and Exercise, 33, S351–S358.[CrossRef][ISI][Medline]
LeMura, L. and Maziekas, M. (2002) Factors that alter body fat, body mass and fat-free mass in pediatric obesity. Medicine and Science in Sports and Exercise, 34, 487–496.[ISI][Medline]
Leenders, N., Sherman, W., Nagaraja, H. and Kien, C. (2001) Evaluation of methods to assess physical activity in free-living conditions. Medicine and Science in Sports and Exercise, 33, 1233–1240.[CrossRef][ISI][Medline]
Louie, L., Eston, R., Rowlands, A., Tong, K., Ingledew, D. and Fu, F. (1999) Validity of heart rate, pedometry and accelerometry for estimating the energy cost of activity in Hong Kong Chinese boys. Pediatric Exercise Science, 11, 229–239.
Pate, R., Trost, S., Felton, G., Ward, D., Dowda, M. and Saunders, R. (1997) Correlates of physical activity behaviour in rural youth. Research Quarterly for Exercise and Sport, 68, 241–248.[ISI][Medline]
Pratt, M., Macera, C. and Blanton, 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, 31, S526–S533.[ISI][Medline]
Sallis, J., Alcaraz, J., McKenzie, T., Hovell, M., Kolody, B. and Nader, P. (1992) Parental behavior in relation to physical activity and fitness in 9-year-old children. American Journal of Diseases in Childhood, 146, 1383–1388.
Sallis, J., Nader, P., Broyles, S., Berry, C., Elder, J., McKenzie, T. and Nelson, J. (1993) Correlates of physical activity at home in Mexican-American and Anglo-American preschool children. Health Psychology, 12, 390–398.[CrossRef][ISI][Medline]
Sallis, J., McKenzie, T., Elder, J., Broyles, S. and Nader, P. (1997) Factors parents use in selecting play spaces for young children. Archives of Pediatric Adolescent Medicine, 151, 414–417.[Abstract]
Sallis, J., Bauman, A. and Pratt, M. (1998) Environmental and policy interventions to promote physical activity. American Journal of Preventive Medicine, 15, 379–397.[CrossRef][ISI][Medline]
Sallis, J., Alcaraz, J., McKenzie, T. and Hovell, M. (1999) Predictors of change in children’s physical activity over 20 months. Variations by gender and level of adiposity. American Journal of Preventive Medicine, 16, 222–229.[CrossRef][ISI][Medline]
Sallis, J., Prochaska, J. and Taylor, W. (2000) A review of correlates of physical activity of children and adolescents. Medicine and Science in Sports and Exercise, 32, 963–975.[ISI][Medline]
Sirard, J. and Pate, R. (2001) Physical activity assessment in children and adolescents. Sports Medicine, 31, 439–454.[CrossRef][ISI][Medline]
Sleap, M. and Tolfrey, K. (2001) Do 9- to 12 yr-old children meet existing physical activity recommendations for health? Medicine and Science in Sports and Exercise, 33, 591–596.[CrossRef][ISI][Medline]
Stucky-Ropp, R. and DiLorenzo, T. (1993) Determinants of exercise in children. Preventive Medicine, 22, 880–889.[CrossRef][ISI][Medline]
Trost, S., Pate, R., Saunders, R., Ward, D., Dowda, M. and Felton, G. (1997) A prospective study of the determinants of physical activity in rural fifth-grade children. Preventive Medicine, 26, 257–263.[CrossRef][ISI][Medline]
Tudor-Locke, C. and Myers, A. (2001) Methodological considerations for researchers and practitioners using pedometers to measure physical (ambulatory) activity. Research Quarterly for Exercise and Sport, 72, 1–12.[ISI][Medline]
Twisk, J. (2001) Physical activity guidelines for children and adolescents. A critical review. Sports Medicine, 31, 617–627.[CrossRef][ISI][Medline]
Vincent, S. and Pangrazi, R. (2002) Does reactivity exist in children when measuring activity levels with pedometers? Pediatric Exercise Science, 14, 56–63.
Welk, G. (1999) The Youth Physical Activity Promotion Model: a conceptual bridge between theory and practice. Quest, 51, 5–23.
Welk, G., Differding, J., Thompson, R., Blair, S., Dziura, J. and Hart, P. (2000) The utility of the Digi-Walker step counter to assess daily physical activity patterns. Medicine and Science in Sports and Exercise, 32, S481–S488.[ISI][Medline]
Received on August 14, 2002; accepted on February 12, 2003
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  R. C. Plotnikoff, K. Bercovitz, R. E. Rhodes, C. A. Loucaides, and N. Karunamuni Testing a conceptual model related to weight perceptions, physical activity and smoking in adolescents Health Educ. Res., April 1, 2007; 22(2): 192 - 202. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||