800 007 970 (Gratuito para españa)
658 598 996
·WhatsApp·

22 sep 2011

Physical activity, participation in sports competition and school transport in spanish adolescents

/
Enviado por
/
Comentarios0
Different domains of physical activity (PA) are particularly important contributors to energy expenditure (Jurj, Wen, Gao, Matthews, yang, Li, et al., 2007) The purpose of this study was to describe the patterns and relationships between some PA domains, including participation in sports competition and transport, and PA levels.

 
Autor(es): Alberto Abarca – Sos, Alberto Aibar Solana, Berta Murillo Pardo, Javier Zaragoza Casterad, Eduardo Generelo Lanaspa
Entidades(es): University of Zaragoza
Congreso: VII CONGRESO DE LA ASOCIACIÓN INTERNACIONAL DE ESCUELAS SUPERIORES DE EDUCACIÓN FÍSICA (AIESEP)
A Coruña, 26-29 de Octubre de 2010
ISBN: 978-84-614-9946-5
Palabras claves: competition, transport, adolescents.

ABSTRACT

Different domains of physical activity (PA) are particularly important contributors to energy expenditure (Jurj, Wen, Gao, Matthews, yang, Li, et al., 2007) The purpose of this study was to describe the patterns and relationships between some PA domains, including participation in sports competition and transport, and PA levels. The sample comprised 82 adolescents (13.57 SD = 1.12 years) in Huesca (Spain), 53 boys (13.63 SD = 1.21 years) and 29 girls (13.45 DT = 0.97 years). Accelerometer (GT1M Actigraph) data were collected during seven days from 7:00 am until 12:00 pm to know PA levels in time of moderate to vigorous PA (MVPA). Participation in sports and school transport were evaluated using two items. We used the statistical program SPSS 15.0 to conduct the analysis.

Gender (β = .185, p < 0.01), age (β = .149, p < 0.05) and participation in sports competition (β = .736, p < 0.001) significantly predicted participation in PA, independently of BMI (p> 0.05) and type of school transport (p> 0.05). These three variables explained 71,9% of the variance in MVPA. In conclusion, participation in sports competition is related strongly, and personal variables, age and gender, are related lightly with PA levels.

INTRODUCTION

The role of Physical Activity (PA) in adolescents’ health and general well-being is clearly established. PA is associated with lower blood pressure levels, more favorable serum and lipoprotein levels and decreased adiposity (Dietz, 1998). But PA also favors beneficial social and psychological effects such as increased social acceptance (Weiss & Duncan, 1997), elevated self-esteem and feelings of well-being (Martinsen & Stephens, 1994). Although the health benefits of regular PA are increasingly documented, PA habits in western countries remain lower than the recommended levels (World Health Organization, 2004).

Participation in PA at early age can predict future practice (Blair et al. 1989, Mendoza, 2000), most studies certify a decrease in PA in adolescent period (e.g. Meusel, et al., 2007). In most studies with these age groups show a series of special features for PA levels (Sallis, et al., 2000): men are more active than women, physical activity declines with age and only a small percentage of adolescents fulfill PA for health guidelines.

The assessment of PA in young people is a complex task, hampered by methodological difficulties. The development of accurate methods for monitoring PA in children and adolescents continues to be a relevant research issue (Puyau, et al., 2002).  Accelerometers are recognised as one of the most effective ways to produce objective information (frequency, duration, intensity) on young people’s habitual PA, which is typically sporadic in nature (Baquet, et al., 2007).
In order to maximize the effectiveness of interventions programs to implement and promote this critical life habit, the complex arrangement of correlates of PA in adolescence needs to be understood.

Personal variables, age, gender and Body Mass Index (BMI) influence physical activity levels (PAL) in adolescence (Van Der Horst, et al., 2007).
As Physical Activity (PA) occurs in multiple social aspects, different domains of PA are particularly important contributors to energy expediture (Jurj, et al., 2007). Daily health habits, such as active transport to school or participation in sports competition can be an opportunity to increase PAL (Davison & Lawson, 2006).

Participation in sports competition is related strongly with PA and physical fitness (Nuponnen, et al., 2003). School transport was studied in different countries and different populations (Chillón, 2008), but there aren’t a consensus in results: Americans (Gordon-Larsen, 2005; Sirard et al., 2005), Asians (Tudor-Locke et al., 2003), Australians (Timperio, et al., 2006) and Europe (Cooper, et al., 2003; Cooper, et al., 2006).
The purpose of this study was to describe the patterns and relationships between some PA domains, including personal variables, participation in sports competition and transport, and MVPA.

METHODS

Study population.

Two urban state secondary schools in the District of Huesca (Aragon region) in Spain, participated in this study. The potential sample included 166 children (80 boys and 86 girls).  Schools were selected on the basis of willingness to cooperate in the study and absence of health- or nutrition-related interventions, which could bias the results. Before participating, all adolescents were informed of the nature of the study. The general inclusion criteria were: (1) all secondary schools were in the District of Huesca and participants had lived in this district for at least 3 years, (2) both a parent and student had to provide informed consent and (3) all students had to be enrolled in physical education (PE) because PE is mandatory in the Spanish.

Two criteria were established for the criterion validity: complete data for a period of 7 consecutive days (Monday-Sunday), except at night while sleeping or during water-based activities, and a minimum of 10 registered hours of data per day (Riddoch, et al., 2004). During the night when asleep, the instrument was still turned on but put aside with the sensors placed horizontally as this mimics the recording of lying still. The participants put the device back on when they got out of bed in the morning (Matthiessen, et al., 2008).

Due to the limited number of accelerometers available, a sub-sample of the study population, 92 adolescents (aged 13.85 ± 1.10 years-old), 59 males (body mass index, 19.99 ± 3.35 kg/m-2) and 33 females (body mass index, 19.66 ± 2.46 kg/m-2), were randomly selected to wear the instruments.  In line with previous studies (Craig, et al., 2003), 10 minutes or more of consecutive zero activity was considered time when the accelerometer was not worn and so 10 students (11.49%) were excluded (6 boys and 4 girls). These criteria decreased the sample from the initial accessible population of 82 adolescents (aged 13.57 ± 1.12 years-old): 53 males (BMI 20.00 ±  3.30 kg/m-2) and 29 females (BMI 19.54 ± 2,18 kg/m-2).

The University of Zaragoza provided ethical approval for this research protocol. Written informed consent was obtained from children’s parents and individual school participants. In order to ensure confidentiality, each subject’s data was coded and stored in a computer file.

Instruments.

Objective measurement of daily physical activity: Physical activity was measured during 7 consecutive days (Monday to Sunday) using the MTI accelerometer, model 7164 activity monitor, worn on the waist. Both verbal and written instructions for taking care of and correctly placing the monitor were given to the children and their parents. The accelerometer in question is a lightweight (27 grams), small (4.5*3.5*1.0 cm) and unobtrusive, single plane (vertical) accelerometer. Its acceleration signal is filtered by an analog bandpass filter (0.1-3.6Hz) and digitised by a 9-bit A/D converter rate of 10 samples per second, storing data at user-defined intervals.

Movement on the vertical plane is detected as a combined function of the frequency and intensity of the movement, while an electronic filter rejects motion outside the range of normal human movement. Validation studies examining this accelerometer suggest that it is a valid and reliable measurement of children’s PA and has a highly significant correlation (r= 0.86) with energy expenditure, assessed by indirect calorimetry, as well as a high degree of inter-instrument reliability (Ekelund, et al., 2000). The epoch duration or sampling period was set at 1′ for this study and the output was expressed as counts per minute (counts•min-1).

For each measurement period, the total number of counts was divided by the number of hours recorded in order to adjust unequal monitoring times, and then converted into counts.minute-1, in accordance with other studies (Sirard, et al., 2000). We chose to use the specific cut off points for young people developed by Treuth, et al. (2004), as they have been used in other studies of young populations in Europe (Andersen, et al., 2006). For the purpose of this study, moderate-to-vigorous physical activity (MVPA) (min day-1) provided by the accelerometer were used in the analysis.

Anthropometric measures: The height, weight and body mass index (BMI) of all children were recorded. Height was measured using a Holtain stadiometer, without shoes and recorded in metres to the nearest millimetre, using standard protocols recommended by the International Society for the Advancement of Kinanthropometry as described by Norton and Olds (1996). Weight was measured in light clothing to the nearest 0.1 kg with a calibrated, beam balance scale (Model 780, SECA). The BMI was calculated using the ratio weight/height2 (Kg.m-2).
Participation in sports and school transport were evaluated using two item,

Design and data collection procedure

The questionnaires were completed after taking the objective measurements. To assist children in their recall, interviewers made sure that vocabulary and language levels used were familiar to young people. Participants were instructed to wear the accelerometer every day from the time they got up in the morning at 7am until midnight, and only remove it when engaging in water activities. Each student was scheduled to wear the CSA for a whole week in order to provide a representative picture of weekly activity.

Statistical analysis

Two separate types of analyses were planned:

  1. Characteristics of participants and outcomes of the study are described as mean ± SD.
  2. Differences in Gender, age, type of school transport and participation in sport competition in PA were assessed by one-way analysis of variance (ANOVA).
  3. We conduct a linear regression analysis to know the relationships between PA and personal variables and the different domains

The level of statistical significance was set at p<0.05. The data was analyzed using SPSS (version 15.0).  

RESULTS

The physical characteristics of the participants are displayed in Table 1.

Table 1. Sample characteristics, mean scores and standard deviations (SD)

Table 1. Physical activity, participation in sports competition and school transport in spanish adolescents

Contenido disponible en el CD Colección Congresos nº 16

 

Also shown in table 1, boys presented higher scores than girls for weight, height and BMI, but only significant differences existed between boys and girls in terms of weight.
In table 2, descriptive data on objective PA measurements was show. There were significant gender differences in total activity (counts min day-1) and MVPA (min day-1). Males presented higher scores than girls for all the variables that were measured.

Table 2. Descriptive data for the Actigraph and self-report PA variables, mean scores and standard deviations (SD)

Table 2. Physical activity, participation in sports competition and school transport in spanish adolescents

Contenido disponible en el CD Colección Congresos nº 16

We weren’t found significant differences  in MVPA between cicle (Figue 1). First cicle (13 and 14 years old) registered 78,65 minutes and second cicle (15 and 16 years old) registered 72,09 minutes.

Figure 1. Physical activity, participation in sports competition and school transport in spanish adolescents

Contenido disponible en el CD Colección Congresos nº 16

Figure 1. MVPA by cycle
There were significant differences (p< ,001) in MVPA between adolescents who present active transport and passive transport. Adolescents with an active transport presented higher scores than passive transport, 84,02 minutes and 69,49 minutes respectively.

Figure 2. Physical activity, participation in sports competition and school transport in spanish adolescents

Contenido disponible en el CD Colección Congresos nº 16

Figure 2. MVPA by type of school transport

There weren’t significant differences (p> ,05) in MVPA between adolescents who participate or not participate in sports competition, 75 minutes and 71,74 minutes respectively.

Figure 3. Physical activity, participation in sports competition and school transport in spanish adolescents

Contenido disponible en el CD Colección Congresos nº 16

Figure 3. MVPA by participation in sports competition
Results about regression analysis are presented in Figure 4. Gender (β = .185, p < 0.01), age (β = .149, p < 0.05) and participation in sports competition (β = .736, p < 0.001) significantly predicted participation in PA, independently of BMI (β = 0.015, p> 0.05) and type of school transport (β = 0.066, p> 0.05). These three variables explained 71,9% of the variance in MVPA.

Figure 4. Physical activity, participation in sports competition and school transport in spanish adolescents

Contenido disponible en el CD Colección Congresos nº 16

Figure 4. Regression analysis

DISCUSSION

We found that gender and age as personal variables had a direct influence on the explanation of the variance in PA levels, excluding BMI. In this survey, studied domains of PA had different effect.  Participation in sports competition is related strongly with the PA levels, variable that determines the MVPA of the study population, but not active transport school. In relation with descriptive analysis, we found differences in MVPA by gender and type of transport, but not by age (cicle) and participation in sport competition.

Consistent with previous research, boys show higher PA levels than girls (Martínez-Gómez, et al., 2009; Springer, et al., 2009). In our study, PA decline with age and we found significant (p< ,001) relationships between age and PA levels. Knuth & Hallal (2009), in their review, emphasize that in young people, there is a decrease in PA levels in adolescence like in our study. Trost et al. (2002) and Butte et al. (2007) measured PA levels with accelerometers, also finds that the MVPA and VPA have a significant inverse relationship to age.

We don’t found significant differences in PA by BMI and not significant relationship between these two variables. These results are consistent with a review developed by Sallis et al. (2000), because only the 29% (6/21) of the analyzed studies found relationships between BMI and PA.
Regarding school transport, there is no consensus in the studies (Ferreira, et al., 2007), but our results are similar with another investigations in European population (Sirard, et al., 2005). On the other hand, other studies point out the negative aspects of participation in sports competition (Cruz-Feliú, 1997), like competitive stress or burnout, but in our case, the relationships was significant, strongly and positive.

There are a number of limitations associated with the accelerometer which could explain these results. The CSA is not sensitive to some activities like swimming and cycling (Freedson & Miller, 2000), which are common among both American (Sallis, et al., 1993) and European young people (Esculcas & Mota, 2000). They have a limited ability to assess cycling, locomotion on a gradient or other activity with limited torso movement, and they may not be sensitive to many of the complex movement patterns exhibited by children during free play (Trost et al., 1998).
In conclusion, participation in sports competition is related strongly, and personal variables, age and gender, are related lightly with PA levels.

REFERENCES:

Andersen, L. B., Harro, M. & Sardinha, L. B., Froberg, K., Ekelund, U., Brage, S., et al. (2006). Physical activity and clustered cardiovascular risk in children: a cross-sectional study (The European Youth Heart Study). Lancet, 368, 299-304.

Baquet, G., Stratton, G., Van Praagh, E., & Berthoin, S. (2007). Improving physical activity assessment in prepubertal children with high-frequency accelerometry monitoring: A methodological issue. Preventive Medicine, 44(2), 143-147.

Blair, S. N., Clark, D. G., Cureton, K. J., & Powell, K. E. (1989). Exercise and fitness in childhood: Implications for a lifetime of health. In, Gisolfi, C.V.and Lamb, D.R, (eds), Perspectives in Exercise Science and Sports Medicine, Vol. 2. Youth, Exercise, and Sport (pp 401-430). Indianapolis: Benchmark Pres.

Butte, N. F., Puyau, M. R., Adolph, A. L., Vohra, F. A., & Zakeri, I. (2007). Physical activity in nonoverweight and overweight hispanic children and adolescents. Medicine & Science in Sports & Exercise, 39(8), 1257-1266.

Chillón, P. (2008). Importancia del desplazamiento activo al colegio, en la salud de los escolares españoles: estudio AVENA. Deporte y actividad física para todos, 4, 94-101.
Cooper, A. R., Page, A. S., Foster, L. J., & Qahwaji, D. (2003). Commuting to school: Are children who walk more physically active? American Journal of Preventive Medicine, 25(4), 273-276.

Cooper, A. R., Wedderkopp, N., Wang, H., Andersen, L. B., Froberg, K., & Page, A. S. (2006). Active travel to school and cardiovascular fitness in danish children and adolescents. Medicine and Science in Sports and Exercise, 38(10), 1724-1731.

Craig, C. L., Marshall, A. L., Sjöström, M., Bauman, A.E., Booth, M.L., Ainsworth, B. E. et al. (2003). International physical questionnaire:12-country reliability and validity. Medicine & Science in Sports & Exercise, 35(8), 1381-1395.

Cruz-Feliú, J. (1997). Psicología del deporte. Madrid: Editorial Sintesis.
Davison, K.K. & Lawson, C.T. (2006). Do attributes in the physical activity environment influence children’s physical activity? A review of the literature. International Journal of Behavioral Nutrition and Physical Activity, 3(19).

Dietz, W. (1998). Health consequences of obesity in youth: childhood predictors of adult disease. Pediatrics, 101(Suppl), 518-525.

Ekelund, U., Yngve, A., Sjöström, M., & Westerterp, K. (2000). Field evaluation of the Computer Science and Application’s Inc. activity monitor during running and skating training in adolescent athletes. International Journal of Sports Medicine, 21, 586-592.

Esculcas, C. & Mota, J. (2000). Physical activity and leisure behaviors in adolescents. The influence of physical activity characteristics and the socio-economic status. International Journal of Behavioral Medicine, 7(1), 208-213.

Ferreira, I., Van Der Horst, K., WendelVos, W., Kremers, S., van Lenthe, F. J., & Brug, J. (2007). Environmental correlates of physical activity in youth – a review and update. Obesity Reviews, 8(2), 129-154.

Freedson, P. S., & Miller, K. (2000). Objective monitoring of physical activity using motion sensors and heart rate. Research Quartely for Exercise and Sport, 71(2), 21-29.

Gordon-Larsen, P., Nelson, M. C., & Beam, K. (2005). Associations among active transportation, physical activity, and weight status in young adults. Obesity Research, 13(5), 868-875.

Jurj, A. L., Wen, W., Gao, T. Y., Matthews, C. E., Yang, G., Li, H. L., et al. (2007). Patterns and correlates of physical activity: a cross-sectional study in urban Chinese women. BMC Public Health, 7(213).

Knuth, A. G., & Hallal, P. C. (2009). Temporal trends in physical activity: A systematic review. Journal of Physical Activity & Health, 6(5), 548-559.

Martínez-Gómez, D., Welk, G. J., Calle, M. E., Marcos, M., & Veiga, O. L. (2009). Preliminary evidence of physical activity levels measured by accelerometer in spanish adolescents; the AFINOS study. Nutrición Hospitalaria, 24(2), 226-232.

Martinsen, E. W., & Stephens, T. (1994). Exercise and mental health in clinical and free living populations. In: R.K. Dishman (Eds). Advances in exercise adherence pp (52-72).

Champaign, IL: Human Kinetics.
Matthiessen, J., Biltoft-Jensen, A., Rasmussen, L. B., Hels, O., Fagt, S., & Groth, M. V. (2008). Comparison of the Danish Physical Activity Questionnaire with a validated position and motion instrument. European Journal of Epidemiology, 23(5), 311-322.

Mendoza, R. (2000). Diferencias de género en los estilos de vida de los adolescentes españoles: Implicaciones para la promoción de la salud y para el fomento de la actividad físico-deportiva. Educación Física y Salud, 765-790.

Meusel, D., Ruiz, J. R., Ortega, F. B., Hagströmer, M., Bergman, P., & Sjöström, M. (2007). Assessing levels of physical activity in the european population – the ALPHA project. Selección, 16, 9- 12.

Norton, K., & Olds, T. (1996). Anthropometrica. Sydney, University of New South Wales Press.

Nupponen, H., Telama, R., Naul, R., Rychtecky, A., Arvisto, M., & Piéron M. (2002). Physical Activity as a Predictor of Motor Fitness among European Youth. In, L. Sena Lino, R. Trindade Ornelas, F. Carreiro da Costa & M. Piéron (Eds.), Innovation and New Technologies in Physical Education, Sport, Research and/on Teacher and Coach Preparation {CD Rom}. Proceedings of the AIESEP International Congress, Madeira 2001. Funchal: Universidade da Madeira.

Puyau, M. R., Adolph, A. L., Vohra, F. A., & Butte, N. F. (2002). Validation and calibration of physical activity monitors in children. Obesity Research, 10(3), 150-157.

Riddoch, C. J., Bo Andersen, L., Wedderkopp, N., Harro, M., Klasson-Heggebo, L., Sardinha, L. B., et al. (2004). Physical activity levels and patterns of 9- and 15-yr-old european children. Medicine and Science in Sports and Exercise, 36(1), 86-92.

Sallis, J. F., Condon., S. A., Goggin., K. J., Roby, J. J., Kolody, B.,  &  Alcaraz, J. E. (1993). The development of self-administered physical activity surveys for the 4th grade students. Research Quaterly in Exercise & Sport, 64, 25-31.

Sallis, J.F., Owen, N., & Fotheringham, M.J. (2000) Behavioral epidemiology: A systematic framework to classify phases of research on health promotion and disease prevention. Annals of Behavioral Medicine, 22(4), 294-298.

Sirard, J. R., Melanson, E. L., Li, L., & Freedson, P. S. (2000). Field evaluation of the computer science and applications, inc. physical activity monitor. Medicine and Science in Sports and Exercise, 32(3), 695-700.

Sirard, J. R., Riner, W. F.,Jr, McIver, K. L., & Pate, R. R. (2005). Physical activity and active commuting to elementary school.  Medicine and Science in Sports and Exercise, 37(12), 2062-2069.

Springer, A. E., Hoelscher, D. M., Castrucci, B., Perez, A., & Kelder, S. H. (2009). Prevalence of physical activity and sedentary behaviors by metropolitan status in 4th-, 8th-, and 11th-grade students in texas, 2004-2005. Preventing Chronic Disease, 6(1), A21.

Timperio, A., Ball, K., Salmon, J., Roberts, R., Giles-Corti, B., Simmons, D., et al. (2006). Personal, family, social, and environmental correlates of active commuting to school. American Journal of Preventive Medicine, 30(1), 45-51.

Treuth, M. S., Schmitz, K., Catellier, D. J., McMurray, R. G., Murray, D. M., Almeida, M. J., et al. (2004). Defining accelerometer thresholds for activity intensities in adolescent girls. Medicine and Science in Sports and Exercise, 36(7), 1259-1266.

Trost, S. G., Morehouse, S., Watson, P., Ward, D. S., Riner, W., & Burke, J. (1998). Validity of the Computer Science and Application (CSA) activity monitor in children. Medicine & Science in Sports & Exercise, 30(40), 629-633.

Trost, S. G., Pate, R. R., Sallis, J. F., Freedson, P. S., Taylor, W. C., Dowda, M., et al. (2002). Age and gender differences in objectively measured physical activity in youth. Medicine and Science in Sports and Exercise, 34(2), 350-355.

Tudor-Locke, C., Ainsworth, B. E., Adair, L. S., & Popkin, B. M. (2003). Objective physical activity of filipino youth stratified for commuting mode to school. Medicine and Science in Sports and Exercise, 35(3), 465-471.

Van Der Horst, K., Paw, M. J. C. A., Twisk, J. W. R., & Van Mechelen, W. (2007). A brief review on correlates of physical activity and sedentariness in youth. Medicine & Science in Sports & Exercise, 39(8), 1241-1250.

Weiss, M. R., & Duncan, S. C. (1997). The relationship between physical competence and peer acceptance in the context of children’s sports participation. Journal of Sport & Exercise Psychology, 14(2), 177-191.

World Health Organization. (2004). Obesity. Preventing and managing the global epidemic. Report of a WHO consultation on obesity. Geneva: World Health Organization.

Responder

Otras colaboraciones