Skip to main content
  • Original Research Article
  • Open access
  • Published:

Personal, social, and environmental correlates of physical activity and sport participation in an adolescent Turkish population



Benefits of physical activity has been shown for adolescents; however, there is a decline trend in number of adolescents meeting current WHO recommendations. This trend underlines the importance of identifying factors associated with adolescents’ physical activity level (PAL) with considerations of regional and cultural differences to plan and implement effective policies. Therefore, the aim of this study was to determine personal, ecological, and social factors associated with PAL and sport participation in Turkish adolescents aged 11–14 years. A cross-sectional study was conducted by including 996 adolescents aged between 11 and 14 years from 39 secondary schools in İstanbul, Turkey. Logistic regression analyses performed to identify the significant personal (age, gender, sleep time, screen time, BMIz score, having siblings), ecological (presence of playground, type of school transportation), and social (family income, engaging a physical activity with family, and preferred activity at school breaks) predictors of PAL and sport participation.


Adolescents who were active during break time at school (p < 0.001), engaging a physical activity with family (p < 0.001), and did not have a sibling (p = 0.029) were more likely to be physically active. Adolescents behaved active during break time at school (p < 0.001), had a playground at home (p < 0.001), spending time with family for physical activity (p < 0.001), and did not have a sibling (p = 0.021) were more likely to participate in a sport activity.


Predictors of PAL in this study indicates the need to promote active break time in school, increased physical activity time with family, and to design environmental policies to increase number of playgrounds.


Physical activity (PA) habits and the continuity of individual’s engagement in PA are of great importance for not only health benefits but also individual’s wellbeing and mental health. World Health Organization (WHO) [1] has recommended PA for all age groups and previous studies has provided evidence for the benefits of higher physical activity level (PAL) [2, 3]. Particularly, in children and adolescents, higher levels of physical activity have been linked to reduced risks of severe health problems, optimal wellbeing, physical fitness, improved cognitive function and academic performance, reduced risk of anxiety/depression, and body growth and development [2]. Nevertheless, in a pooled analysis of cross-sectional studies including 1.6 million school-going adolescents globally, 81.0% of the adolescents did not meet the current PAL recommendations [4] announced by WHO. It has been shown that physical activity level declines by an average of approximately 4% per year after the age of six [5] which continue to decline as children progress through childhood to adolescence [6].

This trend was also evident in Turkish population. According to a national report published by the Turkish Ministry of Health, 71.9% of adolescents do not exercise regularly [7]. Similar studies also reported that Turkish adolescents have low level of PAL and high level of sedentary behaviors [8, 9] which further underlines the need to identify the factors leading to this phenomenon.

Previous studies conducted in different countries identified potential factors associated with PAL and physical activity behaviors of adolescents considering individual, interpersonal, and environmental influences [10]. A recent review has pointed out that several factors including age, gender, parent activity level, physical activity and sport participation in school, peer support, and socioeconomic status could affect the PAL of adolescents [11]. Moreover, PAL has been reported to be linked with screen time of adolescents [12], choice of school transportation [10], and condition of school environment [13]. Since regional and cultural differences is an important determinant associated with PAL [14], identifying the factors related with PAL and physical activity behavior of Turkish adolescents in order to plan and implement national public health policies is warranted.

Even though numerous studies investigated the determinants of PAL in adolescent on understanding sociodemographic and environmental factors, there were no studies investigating these determinants in Turkish adolescent population. Therefore, the aim of this study was to determine the individual, social, and environmental factors associated with PAL and sport participation—as an indicator of high level of PAL—in adolescents aged 11–14 years in Turkish population.

Material and methods

Study design

This study used a cross-sectional survey design to investigate the association of physical activity level and sport participation of adolescents with the personal, social, and ecological factors. After obtaining the necessary approvals from local governments and the ethical approval from Ethics Committee of Marmara University Faculty of Health Sciences (No: 239 Date: 19.12.2019), the study was conducted between February 2020 and March 2020. There were 39 secondary schools in Üsküdar district of Istanbul, and the Education Ministry provided a random selection of schools for data collection. To select the sample, the secondary schools in the district were listed via an electronic medium and 8 of these schools were randomly chosen. However, due to COVID-19 pandemic, data collection was only completed in 3 schools (including 996 participants) before the national shutdown.


Nine hundred ninety-six students aged 11–14 years were invited to take part in the study. Those who returned the informed consent form signed by their parents participated in the study. Students with orthopedic problems that prevent them from participating in physical activity, and those with any systemic, neurological, chronic diseases, or mental problems were not included in the study. The flow chart of the study was summarized in Fig. 1.

Fig. 1
figure 1

Flow chart of the study


Data was collected in the classroom by making face-to-face interviews with each student, under the supervision of the teachers. Information Form (Additional file 1: Appendix 1) and Child Physical Activity Form were used as outcomes. Information was obtained from the administration and teachers on the screening day about students with special conditions (who have a disability, inclusive student, etc.), their answers were obtained, but they were excluded from the study.


PAL and sport participation were measured by using Child Physical Activity Questionnaire (PAQ) and questioning sport-related habits of adolescents.

Child physical activity questionnaire

The questionnaire was developed to evaluate the physical activity level of primary schoolchildren aged 8–14, from the fourth grade to the eighth grade. The reliability and validity of the questionnaire have been well documented [15, 16]. The validity of the questionnaire in Turkish population was also reported in 2012 [17]. Each item of the questionnaire, except for the tenth question, which questions the disease status, is evaluated on a 5-point Likert scale and has an activity score between 1 and 5. “1” indicates low physical activity, “5” indicates high physical activity. The total score of the survey is 1–9. It is calculated by summing up the scores of the answers given to the question and dividing it by the number of questions. A cut-off point of 2.75 was used to identify adolescents who are active (a score of 2.75 or more) or inactive (a score of less than 2.75) [18].

Sport participation

The type of sport activity, duration, and frequency were questioned to decide regular sport participation. Adolescents were classified as regular sport participants if they involved in one of the previously identified sport activity at least once in a month [19] or not regular sport participants (i.e., those who did not involve in one of the previously identified sport activity at least once in a month).


Personal factors

Personal factors included age (continuous), gender (categorical: male or female), sleep time (continuous: calculated as hours spent sleeping on average per a day), and screen time (continuous: calculated as hours spent in front of the TV, computer, tablet or phone). BMI z score (BMIz) (continuous) was measured by adjusting weight for participants’ age and sex [20]. Siblings (categorical) was classified as “Yes” if the adolescent had at least one sibling; if they did not, it was classified as “No”.

Ecological factors

Playground (categorical) was classified as “Yes” or “No” depending on presence of park or playground in the adolescent’s neighborhood. Type of school transportation (categorical) were classified based on the type of transportation they use to get to school as “Physically active (e.g., walking, cycling)” or “Physically inactive (e.g., using bus).”

Social factors

Family income (categorical) was grouped into three categories as “Lower”/”Middle”/”Higher.” Family activity time (categorical) was classified as “Yes” or “No” depending on if adolescent spends time with family for physical activity. Adolescent preference for school breaks (categorical) was classified considering the type of activity that adolescents prefer at their break time as “active (e.g., playing tag)” or “inactive (e.g., sitting).”

Data analysis

Descriptive statistics were used to summarize the demographic information of the participants, and all performance scores. The normality of data was visually evaluated by histograms, and Quantile–Quantile plots; and tested using the Shapiro–Wilk test. The observed outliers were removed from the data to improve the normality of the data. In the condition where data was not normally distributed after outlier removal, the log transformation was done for continuous variables.

Before the main analysis, the collinearity among independent variables were checked through variance inflation factor (VIF). Collinearity was determined to be present when the variance inflation factor was over 5 [21].

The two outcomes (physical activity level and sport participation) were regressed against 6 personal (age, gender, BMIz, sleep time, screen time, and siblings), 2 ecological (playground and school transportation choice), and 3 social (adolescent preference, family income, and family activity time) independent variables using logistic regression analysis. The binary variables (gender, adolescent preference, playground access, school transportation, sibling, family activity time) and ordinal variable (family income) were included into the regression analysis. The data was transformed into dummy variables with being female, being inactive, absence of a playground, using inactive mean of transportation, lower income, absence of a sibling, and lack of active time with family as the reference values.

The model was inspected visually for linearity, heteroscedasticity, and normality of the residuals, and goodness of the fit was evaluated using Hosmer-Lemeshow goodness of fit test, which is a Chi-square test conducted by dividing the sorted set into g=10 equal-sized groups [22]. Our previous study is consistent with the previous studies in the literature which showed that the inactivity rate in adolescents was around 80% in Turkish population [1, 2]. Based on this rate, a sample of at least 748 was needed to obtain 99% power with a confidence level of 95% and 5% Type 1 error, which is lesser than the current sample of 996 adolescents. All statistical analysis was done using R statistical software (Version 3.6.0, St. Louis, Missouri, USA), the package “ResourceSelection” [23]. The alpha level was .05.


Table 1 shows the characteristics of study participants stratified by gender and age. Of the 996 participants, 445 (44.7%) of them were female. Participants’ ages ranged from 11 to 14 years with a mean of 12.60 ± 1.10 years. Mean BMIz of the participants was 19.73 ± 3.52 kg/m2. Of 996 participants included 426 (42.8%) were active according to cut-off value of 2.75. The active participants were 276 (50.1%) for males and 150 (33.7%) for females. Mean PAQ score was 2.75 ± 0.71 for males and 2.49 ± 0.67 for females, which together with active percentage indicate lower PAL of female adolescents in this study.

Table 1 Characteristics of study participants

Physical activity level

The analysis showed the suggested model, yielding the χ2 (Chi-square) of 3.29, was fit the data well (p = 0.91). There was a significant relationship between the PAL and sleep time, preference for break activity, having a sibling, and engaging a physical activity with family of adolescents (p < 0.05). Adolescents who were active during break time at school (OR = 4.28, p ≤ 0.001), spending less time for sleep (OR = 2.61, p = 0.042), engaging a physical activity with family (OR = 1.21, p ≤ 0.001) and who did not have a sibling (OR = 6.51, p = 0.029) were more likely to be physically active, respectively (Table 2).

Table 2 Association between the PAQ score of adolescents and the predictors

Sport participation

The suggested model, yielding the χ2 of 5.87, fit the data well (p = 0.661). The sport participation was significantly associated with preference for break activity, availability of playground, having a sibling, and engaging a physical activity with family (p < 0.05). Adolescents who are active during break time at school (OR = 2.35, p ≤ 0.001), had access to playground (OR = 1.75, p ≤ 0.001), reported some level of activity with family (OR = 1.23, p 0.021), and who did not have a sibling (OR = 0.62, p = 0.021) were more likely to participate in a sport activity (Table 3).

Table 3 Association between the sport participation of adolescents and the predictors


This study examined the associations between personal, ecological, and social factors and adolescents’ physical activity level and sport participation in a sample of Turkish population. We found evidence that being active during break time at school, spending less time for sleep, engaging a physical activity with family, and not having a sibling were associated with being physically active in adolescents. Similarly, being active during break time at school, having a playground at home, engaging a physical activity with family, and not having a sibling were associated with participating in sports.

Of the six estimated personal factors (age, gender, sleep time, screen time, BMIz, and siblings), only spending less time in sleeping and not having a sibling were associated with high level of PAL of adolescents. The relationship between sleeping time and PAL has been reported globally [24]. For example, studies done in adolescents in the Europe and North America consistently reported a significant link between less sleep time and higher level of participation in PA and sport [25, 26]. Pedisic et al. [24] also reported that spending more time in sleep is not only associated with low level of PA but also sedentary behavior of children and adolescents. On the other hand, there are other studies that reporting the opposite, where lower sleep duration was related to sedentary behavior of children [27, 28]. This might be related to the differences in optimum duration of sleep time determined in different studies. Contrary to the literature [29], having a sibling was negatively associated with PAL. Gender itself was also associated with PAL; males had higher PAL compared to females in the previous study [30]. In this study, however, even though the difference between genders were observable in the descriptive data, we did not found gender as a predictor neither of PAL nor sport participation. Similarly, age and BMI were not a predictor of PAL or sport participation in this study; yet previous studies including adolescents in different age groups indicated a decline in PAL with age [30]. For example, adolescents aged 10- to 14-year-old had higher PAL compared to those aged 15- to19-year-old [30]. In our study, we only included adolescents 10- to 14-year-old which did not allow us the track the changes in PAL through adolescence. Surprisingly, this study did not found a relationship between screen time and PAL, which was observed in the previous studies where screen time was linked to sedentary behaviors [31].

It has been indicated that the environmental factors are less relevant to PAL compared to social and parental factors [32]; yet the presence of playground in the neighborhood was significantly associated with sport participation in this study but not with PAL. The presence of playground and its association with PAL was investigated from different aspect in a previous study. The study reported that there was a difference in how a place was perceived as a playground by parent. Adolescents whose parents thought they had a playground, even if they had the same environmental facilities with others, were more active than those whose parents perceived that they didn't have a playground [33]. On the other hand, physical environment and having an accessible field is crucial for playing or practicing sports [34]. Our participants revealed that having a playground opportunity lead them to participate in a sport regularly.

Active transportation to school has been also shown to be associated with higher PAL and sport participation in many studies [35]. However, we did not observe this relationship in this study. This may be due to the fact that only a small percentage of our study sample was using active transportation to get to school and Turkish adolescence rarely uses bicycles as a means of transportation compared to other countries (e.g., the Netherlands) [36].

The importance of peer support on participating in sports and PAL has been reported previously [37]. Our survey did not question about the relationship among peers and how they support each other, yet participants were questioned about how they are spending their time during their break time. Those who preferred spending time with their peers in an activity making them physically active, such as playing tag, had higher PAL levels. This tendency has been also reported in the current literature of qualitative and quantitative studies [11, 38]. Similarly, children spending some time with their family for any sort of PA had higher PAL in both current study and previous studies [39]. Family income was another social factor which was deemed to be linked with PAL of adolescents in the previous studies [38]. For example, in a study done in the USA, high family income was associated with the increased level of moderate to vigorous physical activity in adolescents [40], yet this relationship was not significant in a sample of Turkish population.

This study also presented with some limitations. Firstly, due to inability to include adolescents from different cities and regions of Turkey, the results cannot be generalized to all Turkish population. Secondly, we used a questionnaire (PAQ) to measure the PAL of adolescents and did not use a device-based (e.g., accelerometer) or performance-based (e.g., shuttle run test) measurement methods. Thirdly, we were unable to include all predictors specially to investigate the socioeconomic and cultural predictors of PAL and sport participation. Therefore, it is recommended that future studies should include adolescents from different regions and cities of Turkey and should further investigate the relationship between socioeconomic and cultural variables and PAL since these factors may cause lower PAL among adolescents through reduced access. Also, future research could combine the factors identified in this study with previous physical activity interventions to enhance these interventions.


In conclusion, this study showed that half of the male adolescents and more than 65% of the female adolescents were inactive, which underlines the need for implementing physical activity policies for these age group. Policies focusing on adolescents of Turkish population should consider the predictors in this study when implementing physical activity guidelines. The identified factors related to PAL in this study indicates the need to promote active break time in school, adolescents’ physical activity time with family, and optimizing sleep time. Also, it is important to design environmental policies to increase and optimize the playgrounds.

Availability of data and materials

Not applicable.


  1. Organization WH. WHO guidelines on physical activity and sedentary behaviour: at a glance; 2020.

    Google Scholar 

  2. Timurtaş E, Cinar E, Karabacak N, Demirbüken İ, Polat MG. Association of physical fitness indicators with health profile and lifestyle of children. Clin Exper Health Sci. 2021;11(2):263–8.

    Google Scholar 

  3. Poitras VJ, Gray CE, Borghese MM, Carson V, Chaput J-P, Janssen I, et al. Systematic review of the relationships between objectively measured physical activity and health indicators in school-aged children and youth. Appl Physiol Nutr Metab. 2016;41(6):S197–239.

    Article  Google Scholar 

  4. Guthold R, Stevens GA, Riley LM, Bull FC. Global trends in insufficient physical activity among adolescents: a pooled analysis of 298 population-based surveys with 1·6 million participants. Lancet Child Adolesc Health. 2020;4(1):23–35.

    Article  Google Scholar 

  5. Demetriou Y, Bachner J. A school-based intervention based on self-determination theory to promote girls' physical activity: study protocol of the CReActivity cluster randomised controlled trial. BMC Public Health. 2019;19(1):1–9.

    Article  Google Scholar 

  6. Nader PR, Bradley RH, Houts RM, McRitchie SL, O’Brien M. Moderate-to-vigorous physical activity from ages 9 to 15 years. Jama. 2008;300(3):295–305.

    Article  CAS  Google Scholar 

  7. T.C. Sağlık Bakanlığı THSK. Türkiye Sağlıklı Beslenme Ve Hareketli Hayat Programı (2014 - 2017). Sağlık Bakanlığı Yayın. 2013;(935):22-33.

  8. Köksal G, Özel-Gökmen H. Çocukluk ve Ergenlik Dönemi Obezite. Hastalıklarda Beslenme ve Obezite Bilgi Serisi Sağlık Bakanlığı Temel Sağlık Hizmetleri Genel Müdürlüğü. 2008:139–55.

  9. Yabancı N. Okul çağı çocuklarda büyüme ve obezite durumunun saptanması, etkileyen etmenlerin değerlendirilmesi. Hacettepe Üniversitesi Sağlık Bilimleri Enstitüsü Beslenme ve Diyetetik Programı DoktoraTezi, Ankara. 2004.

  10. Rodriguez-Rodriguez F, Galvez-Fernandez P, Huertas-Delgado FJ, Aranda-Balboa MJ, Saucedo-Araujo RG, Herrador-Colmenero M. Parent's sociodemographic factors, physical activity and active commuting are predictors of independent mobility to school. Int J Health Geogr. 2021;20(1):26.

    Article  CAS  Google Scholar 

  11. Van Der Horst K, Paw MJ, Twisk JW, Van Mechelen W. A brief review on correlates of physical activity and sedentariness in youth. Med Sci Sports Exerc. 2007;39(8):1241–50.

    Article  Google Scholar 

  12. Aggio D, Ogunleye AA, Voss C, Sandercock GR. Temporal relationships between screen-time and physical activity with cardiorespiratory fitness in English schoolchildren: a 2-year longitudinal study. Prev Med. 2012;55(1):37–9.

    Article  CAS  Google Scholar 

  13. Naylor PJ, McKay HA. Prevention in the first place: schools a setting for action on physical inactivity. Br J Sports Med. 2009;43(1):10–3.

    Article  Google Scholar 

  14. Iannotti RJ, Chen R, Kololo H, Petronyte G, Haug E, Roberts C. Motivations for adolescent participation in leisure-time physical activity: international differences. J Phys Act Health. 2013;10(1):106–12.

    Article  Google Scholar 

  15. Crocker PR, Bailey DA, Faulkner RA, Kowalski KC, McGrath R. Measuring general levels of physical activity: preliminary evidence for the physical activity questionnaire for older children. Med Sci Sports Exerc. 1997;29(10):1344–9.

    Article  CAS  Google Scholar 

  16. Kowalski KC, Crocker PR, Kowalski NP. Convergent validity of the physical activity questionnaire for adolescents. Pediatr Exerc Sci. 1997;9(4):342–52.

    Article  Google Scholar 

  17. Erdim L, Ergun A, Kuguoglu S. 71 reliability and validity of turkish version of the physical activity questionnaire for older children (PAQ-C). Arch Dis Child. 2012;97(Suppl 2):A20–A.

    Article  Google Scholar 

  18. Benítez-Porres J, Alvero-Cruz JR, Sardinha LB, López-Fernández I, Carnero EA. Cut-off values for classifying active children and adolescentes using the physical activity questionnaire: PAQ-C and PAQ-A. Nutr Hosp. 2016;33(5):564.

    Article  Google Scholar 

  19. Eime RM, Charity MJ, Harvey JT, Payne WR. Participation in sport and physical activity: associations with socio-economic status and geographical remoteness. BMC Public Health. 2015;15(1):434.

    Article  Google Scholar 

  20. Cole TJ, Faith MS, Pietrobelli A, Heo M. What is the best measure of adiposity change in growing children: BMI, BMI %, BMI z-score or BMI centile? Eur J Clin Nutr. 2005;59(3):419–25.

    Article  CAS  Google Scholar 

  21. Vatcheva KP, Lee M, McCormick JB, Rahbar MH. Multicollinearity in regression analyses conducted in epidemiologic studies. Epidemiology (Sunnyvale). 2016;6(2):227.

    Article  Google Scholar 

  22. Paul P, Pennell ML, Lemeshow S. Standardizing the power of the Hosmer–Lemeshow goodness of fit test in large data sets. Stat Med. 2013;32:67–80.

    Article  Google Scholar 

  23. Team RC. R: A language and environment for statistical computing Vienna, Austria. 2013 [Available from:

  24. Pedišić Ž. Measurement issues and poor adjustments for physical activity and sleep undermine sedentary behaviour research—the focus should shift to the balance between sleep, sedentary behaviour, standing and activity. Kinesiology. 2014;46(1):135–46.

    Google Scholar 

  25. Vandendriessche A, Ghekiere A, Van Cauwenberg J, De Clercq B, Dhondt K, DeSmet A, et al. Does sleep mediate the association between school pressure, physical activity, screen time, and psychological symptoms in early adolescents? A 12-country study. Int J Environ Res Public Health. 2019;16(6):1072.

    Article  Google Scholar 

  26. Fomby P, Goode JA, Truong-Vu K-P, Mollborn S. Adolescent technology, sleep, and physical activity time in two US cohorts. Youth Soc. 2021;53(4):585–609.

    Article  Google Scholar 

  27. Ortega FB, Ruiz JR, Labayen I, Kwak L, Harro J, Oja L, et al. Sleep duration and activity levels in Estonian and Swedish children and adolescents. Eur J Appl Physiol. 2011;111(10):2615–23.

    Article  Google Scholar 

  28. Mitchell JA, Rodriguez D, Schmitz KH, Audrain-McGovern J. Sleep duration and adolescent obesity. Pediatrics. 2013;131(5):e1428–e34.

    Article  Google Scholar 

  29. Bagley S, Salmon J, Crawford D. Family structure and children's television viewing and physical activity. Med Sci Sports Exerc. 2006;38(5):910–8.

    Article  Google Scholar 

  30. Baqal OJ, Saleheen H, AlBuhairan FS. Urgent need for adolescent physical activity policies and promotion: lessons from "Jeeluna". Int J Environ Res Public Health. 2020;17(12):4464.

  31. O'Brien W, Issartel J, Belton S. Relationship between physical activity, screen time and weight status among young adolescents. Sports (Basel). 2018;6(3):57.

    Article  Google Scholar 

  32. Graham DJ, Wall MM, Larson N, Neumark-Sztainer D. Multicontextual correlates of adolescent leisure-time physical activity. Am J Prev Med. 2014;46(6):605–16.

    Article  Google Scholar 

  33. Bringolf-Isler B, Schindler C, de Hoogh K, Kayser B, Suggs LS, Dossegger A, et al. Association of objectively measured and perceived environment with accelerometer-based physical activity and cycling: a Swiss population-based cross-sectional study of children. Int J Public Health. 2019;64(4):499–510.

    Article  Google Scholar 

  34. Mertens L, Van Cauwenberg J, Veitch J, Deforche B, Van Dyck D. Differences in park characteristic preferences for visitation and physical activity among adolescents: a latent class analysis. PLoS One. 2019;14(3):e0212920.

    Article  CAS  Google Scholar 

  35. Lee C, Yoon J, Zhu X. From sedentary to active school commute: multi-level factors associated with travel mode shifts. Prev Med. 2017;95(Suppl):S28–36.

    Article  Google Scholar 

  36. Akbulut G, Yildirim M, Sanlier N, van Stralen MM, Acar-Tek N, Bilici S, et al. Comparison of energy balance-related behaviours and measures of body composition between Turkish adolescents in Turkey and Turkish immigrant adolescents in the Netherlands. Public Health Nutr. 2014;17(12):2692–9.

    Article  Google Scholar 

  37. Jago R, Page AS, Cooper AR. Friends and physical activity during the transition from primary to secondary school. Med Sci Sports Exerc. 2012;44(1):111–7.

    Article  Google Scholar 

  38. Haerens L, De Bourdeaudhuij I, Eiben G, Lauria F, Bel S, Keimer K, et al. Formative research to develop the IDEFICS physical activity intervention component: findings from focus groups with children and parents. J Phys Act Health. 2010;7(2):246–56.

    Article  Google Scholar 

  39. Pyky R, Puhakka S, Ikäheimo TM, Lankila T, Kangas M, Mäntysaari M, et al. Parental factors related to physical activity among adolescent men living in built and natural environment: a population-based MOPO study. J Environ Public Health. 2021;2021:3234083.

    Article  Google Scholar 

  40. Gordon-Larsen P, McMurray RG, Popkin BM. Determinants of adolescent physical activity and inactivity patterns. Pediatrics. 2000;105(6):E83.

    Article  CAS  Google Scholar 

Download references


Not applicable.


No financial or material support of any kind was received for the work described in this article.

Author information

Authors and Affiliations



The authors confirm contribution to the paper as follows: study conception and design: ET, ID, MGP; data collection: ET, ID, HS; analysis and interpretation of results: ET, EC, HS, MGP; draft manuscript preparation: ET, EC, EÇ. All authors reviewed the results and approved the final version of the manuscript.

Corresponding author

Correspondence to Eren Timurtaş.

Ethics declarations

Ethics approval and consent to participate

This study has the necessary approvals from local governments and the ethical approval from Ethics Committee of Marmara University Faculty of Health Sciences (No: 239 Date: 19.12.2019). Written informed consent was obtained from the parents.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Timurtaş, E., Selçuk, H., Çınar, E. et al. Personal, social, and environmental correlates of physical activity and sport participation in an adolescent Turkish population. Bull Fac Phys Ther 27, 11 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: