AMERICAN JOURNAL OF HUMAN BIOLOGY 26:407–412 (2014)
Original Research Article
Correlates of Urban Children’s Leisure-Time Physical Activity and Sedentary Behaviors During School Days ~ MARTINS,3 JOSE DINIZ,1 AND FRANCISCO CARREIRO DA COSTA1,4 ADILSON MARQUES,1* JAMES F. SALLIS,2 JOAO Interdisciplinary Centre for the Study of Human Performance, Faculty of Human Kinetics, University of Lisbon, Lisbon, Portugal 2 University of California at San Diego, San Diego 3 Faculty of Human Kinetics, University of Lisbon, Lisbon, Portugal 4 Faculty of Physical Education and Sport, Lusophone University of Humanities and Technologies, Lisbon, Portugal
1
ABSTRACT: Objectives: Understanding correlates of physical activity and sedentary behaviors may contribute to fostering active lifestyles. This study aimed to identify correlates of physical activity and sedentary behaviors in leisure-time among Portuguese urban children, during school days. Methods: A cross-sectional survey was conducted with 802 students (416 boys), aged 10–12 years. A questionnaire was used to collect data of physical activity, sedentary behaviors, psychological and behavioral variables related to physical activity and sedentary behaviors. Analyses were run separately for boys and girls. Results: Television viewing occupied the most leisure-time of boys and girls, followed by computer usage, and video game playing. These behaviors occupied 259.7 min/day for boys and 208.6 for girls (P 5 0.002). Reported moderate-tovigorous physical activity was 23.7 min for boys and 12.8 min for girls (P < 0.001). Perception of competence and academic achievement were related to physical activity for the boys and girls. Computer use and playing video games with friends were only related to physical activity for the boys. On the other hand, parents’ physical activity participation was related with boys’ and girls’ physical activity. The correlates of sedentary behavior were outdoor play for the boys, age for the girls, and playing video games with friends for both. Conclusions: This finding suggests that interventions should be considered to replace joint video game time with C 2014 Wiley Periodicals, Inc. V joint physical activity time. Am. J. Hum. Biol. 26:407–412, 2014. Physical activity in childhood is associated with current and future health, specifically improved bone mineral density, cardiovascular risk factors, aerobic fitness, muscular strength and endurance, and mental health (Janssen and Leblanc, 2010). In spite of the health benefits, data from self-reports of children’s physical activity suggest that many children are not active enough to benefit their health (Currie et al., 2012; Hallal et al., 2012). Sedentary behaviors are distinct behaviors that have harmful health effects among adults, such as the early onset of cardiovascular heart disease, overweight, and obesity (Chinapaw et al., 2011). The health consequences of sedentary behaviors among youth are less clear (Ekelund et al., 2012). However, the prevalence of sedentary behaviors among young people has been increasing, and some children spend more than 4 h per day in recreational sedentary behaviors, such as watching television, playing video games, and using a computer for recreation (Biddle et al., 2009; Hamar et al., 2010). Understanding the correlates of physical activity and sedentary behaviors in children is important to support the development of effective interventions to promote a healthy and active lifestyle. Correlates of physical activity are well studied (Bauman et al., 2012; Sallis et al., 2000; Uijtdewilligen et al., 2011), but correlates of sedentary behaviors are poorly understood (Uijtdewilligen et al., 2011; Van Der Horst et al., 2007). Both have to be investigated, because it is now recognized that sedentary behaviors are somewhat independent of physical activity (Biddle et al., 2004). Taking into account that in the past decades Portugal has undergone a demographic transition, going from an eminently rural country to one where most of the population lives in urban areas, it is therefore important to understand the habits of the young people that now are C 2014 Wiley Periodicals, Inc. V
living in urban areas and the correlates of physical activity participation and sedentary behaviors. Therefore, the present study aimed to identify correlates of physical activity and sedentary behaviors in leisure-time among Portuguese children. METHODS Participants Seven public elementary schools, randomly selected, from the Lisbon Metropolitan Area participated in this study. From each school, 150 students who attended grades 5–6 were randomly selected. The criteria used for the choice of the participants were their participation in physical education classes, and not having any health problems that could limit their practice of sport. Of the 1050 students who received the questionnaire, 891 responded, for a response rate of 84.9%. Of these 891 questionnaires, 23 had missing data for the gender, and 66 were more than 12 years old, thus were dropped from the analysis. The final sample was 802 students (boys n 5 416, girls n 5 386) aged 10–12 years (mean age 10.6 6 0.7). Data collection took place from January to March 2010. The study was conducted according to ethical standards in sport and exercise science research (Harriss and Atkinson, 2009), and received approval from both the Portuguese Minister of Education and the Ethics Committee *Correspondence to: Adilson Marques, Faculty of Human Kinetics, University of Lisbon, Estrada da Costa, 1499-002 Cruz Quebrada, Portugal. E-mail: [email protected] Received 21 December 2013; Revision received 6 February 2014; Accepted 14 February 2014 DOI: 10.1002/ajhb.22535 Published online 4 March 2014 in Wiley Online Library (wileyonlinelibrary.com).
408
A. MARQUES ET AL.
of Faculty of Human Kinetics of the University of Lisbon. All school administrators gave their consent, legal guardians gave written informed consent, and students provided assent. Physical activity To assess physical activity, participants were asked to list the activities they practiced during the past 5 school days, used previously in another study (Dumith et al., 2010). If children had engaged in some leisure-time physical activity, information on frequency (days per week) and duration (hours and minutes per day) were collected. The total leisure-time physical activity reported was then calculated by multiplying frequency by duration of the activities in which children participated. A test–retest reliability of the physical activities reported was carried out within a one-week interval across 90 children. Using intraclass correlation coefficient (ICC), the reliability was high (ICC 5 0.84–0.90). Sedentary behaviors Three recreational sedentary behaviors were assessed, including television/video/DVD watching, playing a games console or other video game devices, and using the computer for leisure. Participants were asked to indicate the customary time (hours per week) they spent on each activity during weekdays. With this information, a new variable was computed, adding total time for television/ video/DVD watching, playing video games, and using the computer. The test–retest reliability of these recreational sedentary behaviors was carried out within a one-week interval across 80 participants. The reliability was good, ranging between ICC 0.81 and 0.85. Potential correlates of physical activities and sedentary behaviors Demographic and biological variables included age, gender, body mass index (BMI), and socioeconomic status (SES). Weight and height were measured by physical education teachers. Subjects were weighed wearing shorts and a t-shirt, without shoes. Weight was recorded to the nearest 0.5 kg. Stature was measured using a stadiometer (recorded to the nearest 0.5 cm). BMI was calculated using the Quetelet index [weight (kg)/height (m)2]. The BMI was standardized and the BMI z-score was obtained. SES was calculated using the Family Affluence Scale (FAS) as described elsewhere (Currie et al., 2008). This scale is calculated based on information from the familyowned car, a child having his/her own bedroom, the number of family computers, and the number of family holidays. With this information, each child has an FAS score (ranged 0–9 points). Psychological variables were assessed using subscales from the Self-Perception Profile for Children (SPPC) (Harter, 1985). Acceptable to good consistency of reliability was found for the perception of physical competence subscale (a 5 0.8), satisfaction with physical appearance subscale (a 5 0.8), and self-competence in the domain of academic skills subscale (a 5 0.7). Behavioral variables analyzed were commuting to school, outdoor play, participation in clubs, and academic performance. Commuting to and from school was assessed using a four-item scale, including walking, cycling, public transportation, and being picked-up by parents. For each American Journal of Human Biology
item, the participants were asked for the frequency and time of traveling. Time commuted by walking and cycling was added and called active transportation; time commuted in public transportation and car was added and called passive transportation. Participants were asked if they play outdoors using a 1-item 5-point scale (1 5 never to 5 5 daily). One yes/no question asked children to indicate whether or not they attended a sport club. With the grades from mathematics, maternal language, and physical education, an index was computed named academic achievement. In the Portuguese education system, grades are from 1 (lowest mark) to 5 (highest mark). Based on the social cognitive model (Bandura, 1986), it can be conjectured that parental behavior represents a model for children’s behavior. Thus, parents’ physical activity practice was reported using a 1-item 5-point scale for each parent (1 5 never to 5 5 daily). Doing activity with the child can be considered a combination of modelling and social support. Children reported how often parents and friends watched television with them, used the computer with them, played video games with them on a 6-point scale (1 5 never to 6 5 daily). Children also reported whether their parents practiced physical activity with them rarely or regularly. Statistical analysis Descriptive statistics (means, standard deviation, and percents) were calculated for all variables. Continuous variables were examined for normality, and frequency distributions were examined for categorical variables. To analyze the singular effects of correlates of physical activity and sedentary behaviors, the following coefficients were used: Pearson’s correlation, Spearman’s rank correlation, and Point-biserial correlation. Considering all the potential correlates, a multilevel mixed effect regression was used, taking into account that children could nest within schools. Ordered categorical variables were treated as continuous. Dichotomous variables were coded as dummy variables. All analyses were run separately for boys and girls, because consistent findings have shown that gender influences participation in physical activity (Sallis et al., 2000; Van Der Horst et al., 2007) and in sedentary behaviors (Marshall et al., 2006). All statistical analyses were performed using IBM SPSS Statistics 20.0. RESULTS Descriptive statistics of all the variables are summarized in Table 1. Television viewing occupied the most leisure-time of boys and girls, followed by computer usage and video game playing. Boys spent significantly more time using the computer (t(776.875) 5 2.031, P 5 0.043), and playing video games than girls did (t(665.418) 5 7.999, P < 0.001). Television viewing, computer use, and playing video games occupied 259.7 min/ day for boys and 208.6 min/day for girls, and the difference was significant (t(798.644) 5 3.071, P 5 0.002). The time reported in moderate-to-vigorous physical activity in leisure-time was 23.7 min/day for boys and 12.8 min/ day for girls, and the difference was significant (t(773.271) 5 6.177, P < 0.001). Boys and girls who commuted actively to school engaged in active commuting for 5.2 and 4.4 min/day, respectively. On the other hand, passive commuting daily was, on average, 11.7 min for boys and 10.4 min for girls.
409
CORRELATES OF PHYSICAL ACTIVITY AND SEDENTARY BEHAVIORS TABLE 1. Descriptive statistic and chi-square tests and t-tests of gender differences in physical activity, sedentary behaviors, and potential correlates of physical activities and sedentary behaviors Variables
Boys
Girls
Physical activity (min/day)a 23.7 6 27.9 12.8 6 21.4 Sedentary behaviors (min/day)a Television viewing 99.6 6 95.0 103.9 6 87.0 Computer use 84.1 6 84.5 72.1 6 80.3 Video games 76.0 6 91.2 32.6 6 54.7 Total sedentary behaviors 259.7 6 162.3 208.6 6 144.5 a Demographic Age 10.7 6 0.7 10.6 6 0.7 BMI z-score 0.01 6 1.1 20.01 6 0.9 FAS 5.6 6 2.0 5.9 6 2.0 Psychologicala Perception of physical 3.4 6 0.6 3.0 6 0.7 competence Satisfaction with appearance 3.3 6 0.7 3.2 6 0.7 Self-competence in 3.0 6 0.6 3.0 6 0.6 academic skills Behavioral 5.2 6 11.2 4.4 6 10.6 Active transportationa 11.7 6 22.9 10.4 6 11.3 Passive transportationa b 54.1 45.3 Outdoor play (%) 3.5 6 0.7 3.6 6 0.7 Academic achievementa Social 54.1 45.3 Father’s physcial activity (%)a 36.7 38.3 Mothers’ physical activity (%)a b 4.7 6 1.4 4.7 6 1.4 Television viewing with parents b 2.7 6 1.6 2.7 6 1.6 Television viewing with friends 3.0 6 1.7 2.9 6 1.6 Computer use with parentsb b 2.9 6 1.7 2.6 6 1.5 Computer use with friends 2.2 6 1.5 2.0 6 1.5 Playing video games b with parents 3.0 6 1.7 2.3 6 1.5 Playing video games with friendsb a b
P
Original Research Article
Correlates of Urban Children’s Leisure-Time Physical Activity and Sedentary Behaviors During School Days ~ MARTINS,3 JOSE DINIZ,1 AND FRANCISCO CARREIRO DA COSTA1,4 ADILSON MARQUES,1* JAMES F. SALLIS,2 JOAO Interdisciplinary Centre for the Study of Human Performance, Faculty of Human Kinetics, University of Lisbon, Lisbon, Portugal 2 University of California at San Diego, San Diego 3 Faculty of Human Kinetics, University of Lisbon, Lisbon, Portugal 4 Faculty of Physical Education and Sport, Lusophone University of Humanities and Technologies, Lisbon, Portugal
1
ABSTRACT: Objectives: Understanding correlates of physical activity and sedentary behaviors may contribute to fostering active lifestyles. This study aimed to identify correlates of physical activity and sedentary behaviors in leisure-time among Portuguese urban children, during school days. Methods: A cross-sectional survey was conducted with 802 students (416 boys), aged 10–12 years. A questionnaire was used to collect data of physical activity, sedentary behaviors, psychological and behavioral variables related to physical activity and sedentary behaviors. Analyses were run separately for boys and girls. Results: Television viewing occupied the most leisure-time of boys and girls, followed by computer usage, and video game playing. These behaviors occupied 259.7 min/day for boys and 208.6 for girls (P 5 0.002). Reported moderate-tovigorous physical activity was 23.7 min for boys and 12.8 min for girls (P < 0.001). Perception of competence and academic achievement were related to physical activity for the boys and girls. Computer use and playing video games with friends were only related to physical activity for the boys. On the other hand, parents’ physical activity participation was related with boys’ and girls’ physical activity. The correlates of sedentary behavior were outdoor play for the boys, age for the girls, and playing video games with friends for both. Conclusions: This finding suggests that interventions should be considered to replace joint video game time with C 2014 Wiley Periodicals, Inc. V joint physical activity time. Am. J. Hum. Biol. 26:407–412, 2014. Physical activity in childhood is associated with current and future health, specifically improved bone mineral density, cardiovascular risk factors, aerobic fitness, muscular strength and endurance, and mental health (Janssen and Leblanc, 2010). In spite of the health benefits, data from self-reports of children’s physical activity suggest that many children are not active enough to benefit their health (Currie et al., 2012; Hallal et al., 2012). Sedentary behaviors are distinct behaviors that have harmful health effects among adults, such as the early onset of cardiovascular heart disease, overweight, and obesity (Chinapaw et al., 2011). The health consequences of sedentary behaviors among youth are less clear (Ekelund et al., 2012). However, the prevalence of sedentary behaviors among young people has been increasing, and some children spend more than 4 h per day in recreational sedentary behaviors, such as watching television, playing video games, and using a computer for recreation (Biddle et al., 2009; Hamar et al., 2010). Understanding the correlates of physical activity and sedentary behaviors in children is important to support the development of effective interventions to promote a healthy and active lifestyle. Correlates of physical activity are well studied (Bauman et al., 2012; Sallis et al., 2000; Uijtdewilligen et al., 2011), but correlates of sedentary behaviors are poorly understood (Uijtdewilligen et al., 2011; Van Der Horst et al., 2007). Both have to be investigated, because it is now recognized that sedentary behaviors are somewhat independent of physical activity (Biddle et al., 2004). Taking into account that in the past decades Portugal has undergone a demographic transition, going from an eminently rural country to one where most of the population lives in urban areas, it is therefore important to understand the habits of the young people that now are C 2014 Wiley Periodicals, Inc. V
living in urban areas and the correlates of physical activity participation and sedentary behaviors. Therefore, the present study aimed to identify correlates of physical activity and sedentary behaviors in leisure-time among Portuguese children. METHODS Participants Seven public elementary schools, randomly selected, from the Lisbon Metropolitan Area participated in this study. From each school, 150 students who attended grades 5–6 were randomly selected. The criteria used for the choice of the participants were their participation in physical education classes, and not having any health problems that could limit their practice of sport. Of the 1050 students who received the questionnaire, 891 responded, for a response rate of 84.9%. Of these 891 questionnaires, 23 had missing data for the gender, and 66 were more than 12 years old, thus were dropped from the analysis. The final sample was 802 students (boys n 5 416, girls n 5 386) aged 10–12 years (mean age 10.6 6 0.7). Data collection took place from January to March 2010. The study was conducted according to ethical standards in sport and exercise science research (Harriss and Atkinson, 2009), and received approval from both the Portuguese Minister of Education and the Ethics Committee *Correspondence to: Adilson Marques, Faculty of Human Kinetics, University of Lisbon, Estrada da Costa, 1499-002 Cruz Quebrada, Portugal. E-mail: [email protected] Received 21 December 2013; Revision received 6 February 2014; Accepted 14 February 2014 DOI: 10.1002/ajhb.22535 Published online 4 March 2014 in Wiley Online Library (wileyonlinelibrary.com).
408
A. MARQUES ET AL.
of Faculty of Human Kinetics of the University of Lisbon. All school administrators gave their consent, legal guardians gave written informed consent, and students provided assent. Physical activity To assess physical activity, participants were asked to list the activities they practiced during the past 5 school days, used previously in another study (Dumith et al., 2010). If children had engaged in some leisure-time physical activity, information on frequency (days per week) and duration (hours and minutes per day) were collected. The total leisure-time physical activity reported was then calculated by multiplying frequency by duration of the activities in which children participated. A test–retest reliability of the physical activities reported was carried out within a one-week interval across 90 children. Using intraclass correlation coefficient (ICC), the reliability was high (ICC 5 0.84–0.90). Sedentary behaviors Three recreational sedentary behaviors were assessed, including television/video/DVD watching, playing a games console or other video game devices, and using the computer for leisure. Participants were asked to indicate the customary time (hours per week) they spent on each activity during weekdays. With this information, a new variable was computed, adding total time for television/ video/DVD watching, playing video games, and using the computer. The test–retest reliability of these recreational sedentary behaviors was carried out within a one-week interval across 80 participants. The reliability was good, ranging between ICC 0.81 and 0.85. Potential correlates of physical activities and sedentary behaviors Demographic and biological variables included age, gender, body mass index (BMI), and socioeconomic status (SES). Weight and height were measured by physical education teachers. Subjects were weighed wearing shorts and a t-shirt, without shoes. Weight was recorded to the nearest 0.5 kg. Stature was measured using a stadiometer (recorded to the nearest 0.5 cm). BMI was calculated using the Quetelet index [weight (kg)/height (m)2]. The BMI was standardized and the BMI z-score was obtained. SES was calculated using the Family Affluence Scale (FAS) as described elsewhere (Currie et al., 2008). This scale is calculated based on information from the familyowned car, a child having his/her own bedroom, the number of family computers, and the number of family holidays. With this information, each child has an FAS score (ranged 0–9 points). Psychological variables were assessed using subscales from the Self-Perception Profile for Children (SPPC) (Harter, 1985). Acceptable to good consistency of reliability was found for the perception of physical competence subscale (a 5 0.8), satisfaction with physical appearance subscale (a 5 0.8), and self-competence in the domain of academic skills subscale (a 5 0.7). Behavioral variables analyzed were commuting to school, outdoor play, participation in clubs, and academic performance. Commuting to and from school was assessed using a four-item scale, including walking, cycling, public transportation, and being picked-up by parents. For each American Journal of Human Biology
item, the participants were asked for the frequency and time of traveling. Time commuted by walking and cycling was added and called active transportation; time commuted in public transportation and car was added and called passive transportation. Participants were asked if they play outdoors using a 1-item 5-point scale (1 5 never to 5 5 daily). One yes/no question asked children to indicate whether or not they attended a sport club. With the grades from mathematics, maternal language, and physical education, an index was computed named academic achievement. In the Portuguese education system, grades are from 1 (lowest mark) to 5 (highest mark). Based on the social cognitive model (Bandura, 1986), it can be conjectured that parental behavior represents a model for children’s behavior. Thus, parents’ physical activity practice was reported using a 1-item 5-point scale for each parent (1 5 never to 5 5 daily). Doing activity with the child can be considered a combination of modelling and social support. Children reported how often parents and friends watched television with them, used the computer with them, played video games with them on a 6-point scale (1 5 never to 6 5 daily). Children also reported whether their parents practiced physical activity with them rarely or regularly. Statistical analysis Descriptive statistics (means, standard deviation, and percents) were calculated for all variables. Continuous variables were examined for normality, and frequency distributions were examined for categorical variables. To analyze the singular effects of correlates of physical activity and sedentary behaviors, the following coefficients were used: Pearson’s correlation, Spearman’s rank correlation, and Point-biserial correlation. Considering all the potential correlates, a multilevel mixed effect regression was used, taking into account that children could nest within schools. Ordered categorical variables were treated as continuous. Dichotomous variables were coded as dummy variables. All analyses were run separately for boys and girls, because consistent findings have shown that gender influences participation in physical activity (Sallis et al., 2000; Van Der Horst et al., 2007) and in sedentary behaviors (Marshall et al., 2006). All statistical analyses were performed using IBM SPSS Statistics 20.0. RESULTS Descriptive statistics of all the variables are summarized in Table 1. Television viewing occupied the most leisure-time of boys and girls, followed by computer usage and video game playing. Boys spent significantly more time using the computer (t(776.875) 5 2.031, P 5 0.043), and playing video games than girls did (t(665.418) 5 7.999, P < 0.001). Television viewing, computer use, and playing video games occupied 259.7 min/ day for boys and 208.6 min/day for girls, and the difference was significant (t(798.644) 5 3.071, P 5 0.002). The time reported in moderate-to-vigorous physical activity in leisure-time was 23.7 min/day for boys and 12.8 min/ day for girls, and the difference was significant (t(773.271) 5 6.177, P < 0.001). Boys and girls who commuted actively to school engaged in active commuting for 5.2 and 4.4 min/day, respectively. On the other hand, passive commuting daily was, on average, 11.7 min for boys and 10.4 min for girls.
409
CORRELATES OF PHYSICAL ACTIVITY AND SEDENTARY BEHAVIORS TABLE 1. Descriptive statistic and chi-square tests and t-tests of gender differences in physical activity, sedentary behaviors, and potential correlates of physical activities and sedentary behaviors Variables
Boys
Girls
Physical activity (min/day)a 23.7 6 27.9 12.8 6 21.4 Sedentary behaviors (min/day)a Television viewing 99.6 6 95.0 103.9 6 87.0 Computer use 84.1 6 84.5 72.1 6 80.3 Video games 76.0 6 91.2 32.6 6 54.7 Total sedentary behaviors 259.7 6 162.3 208.6 6 144.5 a Demographic Age 10.7 6 0.7 10.6 6 0.7 BMI z-score 0.01 6 1.1 20.01 6 0.9 FAS 5.6 6 2.0 5.9 6 2.0 Psychologicala Perception of physical 3.4 6 0.6 3.0 6 0.7 competence Satisfaction with appearance 3.3 6 0.7 3.2 6 0.7 Self-competence in 3.0 6 0.6 3.0 6 0.6 academic skills Behavioral 5.2 6 11.2 4.4 6 10.6 Active transportationa 11.7 6 22.9 10.4 6 11.3 Passive transportationa b 54.1 45.3 Outdoor play (%) 3.5 6 0.7 3.6 6 0.7 Academic achievementa Social 54.1 45.3 Father’s physcial activity (%)a 36.7 38.3 Mothers’ physical activity (%)a b 4.7 6 1.4 4.7 6 1.4 Television viewing with parents b 2.7 6 1.6 2.7 6 1.6 Television viewing with friends 3.0 6 1.7 2.9 6 1.6 Computer use with parentsb b 2.9 6 1.7 2.6 6 1.5 Computer use with friends 2.2 6 1.5 2.0 6 1.5 Playing video games b with parents 3.0 6 1.7 2.3 6 1.5 Playing video games with friendsb a b
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