Skip to main content
Journal of Human Kinetics logoLink to Journal of Human Kinetics
. 2012 May 30;32:175–183. doi: 10.2478/v10078-012-0034-4

Socio-Demographic Determinants of Participation in Swimming Amongst Working Residents of Warsaw

Elżbieta Biernat 1,
PMCID: PMC3590861  PMID: 23486742

Abstract

The aim of research is to assess the correlation between socio-demographic factors and swimming activity among the working population of Warsaw. The questionnaire survey included 4405 randomly selected residents of Warsaw. The correlation between the swimming activity and the variables characterizing the socio-demographic structure of the respondents were assessed by log-linear modelling. The significance of the impact of factors included in the analysis was determined using the chi-square test. Thirty-five per cent of the respondents declared recreational swimming. Gender, age, BMI, education, occupation, and income were significantly related to the swimming activity. Women (33%) – compared to men (38%) – were almost 1.2 times less likely to participate in swimming; similarly, overweight people (33%, OR = 0.90) and obese people (33%, OR = 0.92). People from Warsaw from 20–29 years (43%), with higher education (40%), incomes above the national average (40%), and representing the profession of an actor (52%), swam relatively more often. The results of the study might help in developing marketing plans and market segmentation strategies, as well as in forecasting the development trends of the leisure activity.

Keywords: swimming activity, socio-demographic determinants, Warsaw

Introduction

An active lifestyle is associated with a number of benefits important for society (Stephenson et al., 2000; Sallis & Owen, 1999) and health (WHO, 2002). However, despite extensive knowledge about the effectiveness of exercise in the prevention and treatment of diseases (Stephenson et al., 2000), 30% of the population of Europe (excluding Poles; Sjöström et al., 2006) and 28% of the Polish population (Central Statistical Office, 2009) could still be characterized by low levels of physical activity.

Regular physical activity of contemporary Poles is a developing process. On one hand, more and more people are engaged in regular physical activity, while on the other hand, for some social groups, this kind of activity is still unavailable (Biernat & Tomaszewski, 2011; Biernat et al., 2010). To obtain an explanation of the scale and nature of social disparities in this area, an analysis of the determinants of participation in active forms of leisure time is required. The active forms of leisure activities of various socio-professional groups are the subjects of many sociological studies. But when it comes to swimming, which is one of the most popular disciplines in Poland, studies mostly regard methods of teaching. They are mostly designed to fulfil sport-related objectives and described from the standpoint of the theory of sport. An analysis of the determinants of participation in this discipline is not that frequent. Therefore, the purpose of this study is to assess the correlation between demographic factors, such as sex, age, BMI, marital status, occupation, education, as well as income, and one of the most popular forms of physical education undertaken by respondents – swimming.

Respondents were randomly chosen mostly, from among professional groups that are internationally recognized as having social and economic prestige – the elite leadership groups that constitute a model for the rest of society. Thus, the result of this study might be helpful not only in building an effective strategy for sport development in the tested environment, but also might indirectly contribute to changes in the leisure time behaviour of society as a whole.

Material and Methods

The study included 4405 randomly selected residents of Warsaw. The group was comprised of representatives of seven occupational groups: teachers (from high schools, middle schools, and primary schools), scientists (research institutes and academics), healthcare professionals, administrative personnel (central and local government employees), administrative and technical personnel (from universities, theatres, and research institution), trade industry professionals (hypermarkets, retail employees), and actors. The groups were distinguished according to International Standard Classification of Occupations (ISCO; www.ilo.org); however in the case of teachers, healthcare professionals, and actors originally representing the same category, it was decided to analyse these groups separately, as the representatives of these groups may be regarded as creators of the behaviour patterns and thus may influence behaviour of the general population (Cardinal, 2001; Cardon et al., 2009; Lobelo et al., 2009).

The study was conducted following the summer holiday season (November 2007–2008) and following the winter holiday season (March 2008–2009). In order to select the test group, a two-stage sampling system was used. The first step was to choose 3 to 10 institutions that employed people engaged in a particular profession from among all the institutions of that type in Warsaw. The exception was the group of retail employees, in which case three streets with a significant number of commercial buildings were selected in each district in Warsaw. In the second stage, a certain number of people in each institution were selected. At institutions employing up to 35 workers, the study embraced the whole group. In institutions employing or educating more people, a 30% sample group was selected, but limited to not more than 100 people.

The study was conducted in a survey method. Trained and supervised interviewers, according to a specific plan (the number of questions and their content were identical for all respondents) led direct interviews (standardized). The percentage of refusals to answer survey questions was small, and limited to a range of 3% to 5%. A slightly larger number of refusals were found in groups of trade industry professionals (10%) and actors (20%).

The poll – modified after the pilot version – included questions concerning the participation of respondents in recreational activities over the past year. The objects were asked about the nature of this participation (regular, periodic, sporadic), and type of sport disciplines or physical activities practiced recreationally, not professionally. The present paper is based on answers regarding the declaration of participation in swimming during the year. Besides the information concerning participation in swimming, interviewers collected data on the respondents’ gender, age, body weight and height, marital status, education, occupation, and income. Based on this data, the respondents were placed into various categories: age (20–29, 30–39, 40–49, 50–59, > 60 years old); BMI (underweight: < 20; standard: 20.0–24.9, overweight: 25.0–29.9; obesity: 30.0–39.9), marital status (single – never married women/man, divorced/widowed, in a stable relationship – marriage/partnership); education (primary/vocational, secondary, higher); income (< PLN 2500, > PLN 2500 gross per family member; based on data on website Money.pl (http://www.money.pl/) it is respectively approximately < €737 and > €737). The number of respondents in each category is shown in Table 1.

Table 1.

Number of subjects of the research (n=4405) within given category of socio-demographic variable

Variable n %
Sex
  Male 1533 34.8
  Female 2872 65.2
Age
  20–29 years old 909 20.6
  30–39 years old 1244 28.2
  40–49 years old 991 22.5
  50–59 years old 929 21.1
  >60 years old 303 6.9
In a relationship
  No 1488 33.8
  Yes 2881 65.4
BMI
  Underweight (<20) 482 10.9
  Normal (20.0–24.9) 2272 51.6
  Overweight (25.0–29.9) 1305 29.6
  Obese (30.0–39.9) 287 6.5
Education
  Primary/Vocational 183 4.2
  Secondary 1118 25.4
  Higher 3098 70.3
Professional group
  Actors 103 2.3
  Teachers 1089 24.7
  Administrative personnel 627 14.2
  Trade industry professionals 685 15.6
  Scientists 810 18.4
  Healthcare professionals 764 17.3
  Administrative and technical staff 327 7.4
National average (737 €)*
  Below 3127 71.0
  Above 589 13.4

Due to possible data deficiencies the number of respondents may vary between individual variables

Correlations between the declarations regarding swimming and the variables that characterize the socio-demographic structure of respondents were evaluated based on a log-linear analysis. In order to determine the optimal model for testing, values of the chi-square test for main effects without interaction were calculated, and extended models were analysed considering higher-order interactions. The significance of the analysed correlations in the test model was evaluated on the basis of partial correlations and boundary correlations. A partial correlation indicates whether the given interaction affects the fitting of the model, when all other effects of the same order are already in the model. A boundary correlation allows for the comparison of the model without any interaction with the model, including only a given correlation. Hierarchical models were tested with different interactions. In order to fit the model with the observed values, an iterative procedure was also performed. The process was interrupted when the difference between the observed and fitted boundary distributions was not greater than the convergence criterion = 0.01. The results obtained in the analyses are presented using fractions, and odds ratios (OR) with 95% confidence intervals.

The analyses were performed using the statistical package of STATISTICA 8.0 PL. The significance level of p < 0.05 was assumed in assessing the significance of effects.

Results

Among the working population of Warsaw, 35% declared swimming as an active form of leisure time. As a result of the analysis of partial and boundary correlations it was found that, among the analysed variables, only marital status showed no relation to the level of the respondents’ participation in swimming. Other variables, such as gender (partial χ2 = 11.8, p = 0.000), age (partial χ2 = 42.8, p = 0.000), BMI (partial χ2 = 10.3, p = 0.035), education (partial χ2 = 113.9, p = 0.000) and occupation (partial χ2 = 154.6, p = 0.000) were factors significantly associated with swimming. The same was true with regards to income (partial χ2 = 8.2, p = 0.017), which variable was included into the analysis despite a significant proportion of missing data (reaching 16%).

The analysis of boundary frequency tables estimated on the set models indicates that swimming was practiced relatively more often (p < 0.001) by men (38%) than by women (33%, Table 2). Women – compared with men – were almost 1.2 times less likely to practice this discipline.

Table 2.

Factors determining swimming activity of employed residents of Warsaw (n=4405) and odds ratios (OR) as well as 95% confidence intervals (95% CI) established for being active

Variable Swimming Not-swimming p OR 95% CI


n % n %
Sex 0.001
  Male 957 62.4 576 37.6 1 -
  Female 1912 66.6 960 33.4 0.83 0.73–0.95
Age 0.000
  20–29 years old 518 57.0 391 43.0 1 -
  30–39 years old 781 62.8 463 37.2 0.79 0.66–0.94
  40–49 years old 679 68.5 312 31.5 0.61 0.50–0.73
  50–59 years old 651 70.1 278 29.9 0.57 0.47–0.69
  >60 years old 220 72.6 83 27.4 0.50 0.38–0.66
In a relationship NS
  No 941 63.2 547 36.8
  Yes 1900 65.9 981 34.1
BMI 0.035
  Underweight (<20) 276 57.3 206 42.7 1.39 1.14–1.70
  Normal (20.0–24.9) 1478 65.1 794 34.9 1 -
  Overweight (25.0–29.9) 880 67.4 425 32.6 0.90 0.78–1.04
  Obese (30.0–39.9) 192 66.9 95 33.1 0.92 0.71–1.20
Education 0.000
  Higher 1862 60.1 1236 39.9 1 -
  Secondary 832 74.4 286 25.6 0.52 0.44–0.60
  Primary/Vocational 170 92.9 13 7.1 0.12 0.07–0.20
Professional group 0.000
  Actors 50 48.5 53 51.5 1 -
  Administrative personnel 372 59.3 255 40.7 0.65 0.43–0.98
  Healthcare professionals 452 59.2 312 40.8 0.65 0.43–0.98
  Scientists 486 60.0 324 40.0 0.63 0.42–0.95
  Teachers 715 65.7 374 34.3 0.49 0.33–0.74
  Administrative and technical staff 235 71.9 92 28.1 0.37 0.23–0.58
  Trade industry professionals 559 81.6 126 18.4 0.21 0.13–0.33
National average (737 €)* 0.017
  Below 2080 66.5 1047 33.5 0.76 0.64–0.92
  Above 355 60.3 234 39.7 1 -

NS – non-significant; Odds ratios (OR) were computed with reference to the not-swimming respondents.

Due to possible data deficiencies the number of respondents may vary between individual variables

The designated odds ratio for participation in swimming of underweight people (43%) was approximately 1.4 compared to those with normal BMI (35%). Contrast results were achieved for overweight people (33%, OR = 0.90) and people with obesity (33%, OR = 0.92).

In addition, swimming activity was more often undertaken (p = 0.000) by Warsaw’s young residents – aged 20–29 (43%), with higher education (40%) and level of income above the national average (40%). Thus, in comparison with the youngest respondents, the odds ratio of participation in swimming for people aged 30–39 years (37%), 40–49 years (32%), 50–59 years (30%) and > 60 years (27 %) was correspondingly reduced (OR = 0.79; 0.61; 0.57; 0.50). In turn, among the inhabitants of Warsaw with primary/vocational education (7%) and secondary education (26%) the risk of not practicing in this discipline was 8.3 and 1.9 times higher, respectively, than among those with higher education (40%). Respondents who declared income below the national average swam (34%, OR = 0.76) relatively less often (p = 0.017), then the respondents reporting above-average income.

It was also found, that there is correlation between the actual occupation and the practice of swimming. The observed interaction was largely reflected in differences between the actors (52%) and other groups: administrative staff (41%, OR = 0.65); healthcare professionals (41%, OR = 0.65); scientists (40%, OR = 0.63) and teachers (34%, OR = 0.49); administrative and technical staff (28%, OR = 0.37); and retail workers (18%, OR = 0.21).

Discussion

Leisure time is an important component and inherent feature of contemporary social structures. As such, it is an object of sociological research in which behaviours of the various socio-professional groups are essential.

The studied respondents – working people in Warsaw (4405 individuals) – were recruited from seven professional groups (teachers, scientists, healthcare professionals, administrative personnel, administrative and technical stuff, trade industry professionals, and actors). These were mostly people with higher education (70%). Evaluated was their participation in swimming, a discipline that practiced rationally brings many health benefits (Barter, 1992; Parker, 2011), remedial and compensative benefits (Alavi et al., 2011; Jennings, 2011), as well as recreational benefits (Hastings et al., 1995).

On the basis of the self declaration, it was concluded that 35% of the respondents swam at least once a year. It seems that, in comparison with Canadians, among which 10.4% (5.5% of men and 18.7% women) practise this sport on a regular basis (at least once a week in the season or over a period of time during the year), it is not a satisfactory result (Ifedi, 2008). What then influences the level of participation of respondents in Warsaw in this discipline? Despite the fact that unmarried persons are considered to be a group that is very active in recreational activities (Lubowiecki-Vikuk, 2011), the analysis of the correlation of particular socio-demographic factors with swimming didn’t reveal a dependency regarding marital status. It has been shown, however, that gender age, BMI, education, occupation, and income are crucial variables that affect the behaviour of respondents. Previous publications showed a generally known relation that men are more active than women (ABS, 2003; Ifedi, 2008). Indeed, in this case, women are almost 1.2 times less like to participate in swimming. Henderson et al. (2000) explained the reasons for such a situation by the differences between men and women in the access and freedom of choice of recreation. This thesis was confirmed by Bittman and Wajcman (2000), who added an additional argument: namely the lower level of financial independence of women. It seemed that in Warsaw’s reality, major reasons for women lower participation in this discipline (33% vs. 38%) are on one hand – highlighted by Miller and Brown (2005) – family roles assigned to women; on the other hand, the – recently observed – distinctive increase of women’ professional activity. Consequently, work and family responsibilities significantly limit women's leisure time in Warsaw.

Swimming is often advertised as the best form of exercise for weight loss (Hastings et al., 1995). At the same time it is highlighted that it must be combined with rational nutrition. In the absence of diet, swimming has little or no effect on weight loss (Gwinup, 1987). Despite the high awareness of the respondents (17.3% of respondents comprised of healthcare professionals, 24.7% of teachers, 18.4% of scientists), the percentage of overweight (33%) and obese (33%) Varsovians who swim was not too high. Only underweight people are about 1.4 times more likely (compare to those with standard BMI) to practise swimming. It is difficult to definitively state the reason for such a situation. It doesn’t seem likely that among highly educated people lack of knowledge was an obstacle in this regard. Perhaps further studies on the correlation between motives, obstacles, frequency of swimming, and BMI of respondents would explain the problem.

The results also showed that age actually determines swimming activity among working resident of Warsaw. It is known that the risk of inactivity increases with age (Katzmarzyk, 2007). And in this case, when compared with the youngest respondents (aged 20–29 years), the risk for the 30–39-year-old group increased by 1.3 times; for 40–49-year-olds by 1.6 times; for the group of 50–59-year-olds by 1.8 times; and for people over 60 years old about 2 times. It is one of the global public health priorities to encourage older people into physical activity. It is a priority from the standpoint of physical health (Mensink et al., 1999), mental health (Mummery et al., 2004; Takkinen et al., 2001), and in terms of reducing mortality (Landi et al., 2004; Villeneuve et al., 1998). It seemed that swimming, as a form of sport that fulfilled all these requirements, would dominate the leisure time behaviour of the study participants. However, in this study, as well as in earlier studies conducted in Warsaw (Biernat et al., 2011), a downward trend has been noted in the older age groups. A similar problem was noticed among Canadians (Ifedi, 2008) and Swedes (Hagströmer et al., 2006). It seems that in this regard Australians should be the example to follow. Among them, more than half the population perform the recommended 30 minutes of physical activity almost every day (Armstrong et al., 2000).

Physical activity (including swimming) is not a simple function of age. In addition to many conditions, sport requires both some physical fitness and sufficient income. Respondents of this research were working (earning money), highly educated residents of Warsaw with particular cultural aspirations (Csikszentmihalyi, 1996). The correlation between level of education and care for one’s own physical condition seemed to be quite natural. Higher education entailed not only more knowledge on how to take care of one’s own health, but also the position in the occupational structure, which often requires creative approaches towards problems arising in the workplace. The high requirements related to occupation (care for appearance, slender body, and a high involvement in work, which must be combined with good physical fitness and resistance to stress) greatly contributed to the decision for regular fitness activity (Zarotis et al., 2007). It is not surprising, then, that a resident of Warsaw with higher education (40%) has more then an eight times greater chance of being involved in swimming, than those with a primary/vocational education (7%).

This statement is confirmed in virtually all studies, which show extremely high, positive levels of correlation not only between education and practicing different disciplines of sports (Ifedi, 2008), but also with the amount of the expenditure incurred for this purpose. However, Heilbrun and Gray (1993) believed that level of education has a stronger influence on recreational activity than level of income, yet many researchers highlight the fact that a lack of money can be a major obstacle, while higher incomes and mobility can be an important factor in undertaking physical activity (Ifedi, 2008; Merriman, 1991). Similarly, in the presented study, the financial situation of the participants highly influenced their participation in swimming. It turned out that people who reported the highest monthly income (above €737 gross per family member) practice swimming relatively more often (p = 0.000) than other respondents. With regard to income level, the obtained results should be interpreted with caution, as the percentage of a refusal to answer was significant (about 16% of the respondents).

The results also indicate that, although knowledge of the role of physical activity in maintaining health may be relevant in deciding whether to swim, among certain groups of respondents it is not a sufficient factor to continue such activity. The groups who are careless in this regard are easy to distinguish. It is unfortunate that, among those who do not swim – other than people that belong to the trade industry professionals group (82%) – are members from the group of healthcare professionals (59%). Compared to the group of actors – the group the most involved in swimming (52% of them swimming) – the chances of being engaged in swimming among the representatives of two aforementioned groups are, respectively, 4.8 and 1.5 times smaller. When analysing the impact of occupation on participation in swimming, it must be remembered that the classifications drawn up according to the performed occupation is based not only on the data regarding the amount and structure of free time or the leisure patterns of a particular occupation, but also based on the size of income – as demonstrated earlier in this paper.

At this point it is important to highlight that:

  1. The chosen sample can be considered representative only for the working residents of Warsaw, with the exception of the blue-collar workers.

  2. The results of the declared income and body mass should be interpreted with caution, as the percentage of refusal to answer was significant (16% and 7%, respectively). In this context, it is difficult to determine the credibility of the declarations of other respondents; that is to say, false declarations cannot be ruled out (resulting from a reluctance to disclose actual income or body weight). On the other hand (without ascribing a respondent’s intent to deceive), the real financial situation of the respondents can be stated based on data collected from over 4400 subjects.

  3. No information on the motives and obstacles to undertake the activity of swimming, and a lack of information with regards to training frequency in specific environments, socio-professional groups, etc., significantly limited the ability to draw conclusions. Therefore, further study seems to be necessary to comprehensively explain the problem.

In conclusion, the findings of the research highlight the importance of the socio-demographic determinants of active forms of leisure time. It seems that the results of the study, including the strong correlations between factors such as gender, age, BMI, education, occupation, income, and swimming, might help in developing motivational programs and practical health-related activities conducted in the workplace. However, this requires further systematic research in various socio-occupational environments.

Acknowledgments

This work was supported by the Joseph Pilsudski University of Physical Education in Warsaw, the Ministry of Science and Higher Education in Poland (grant DS – 86; project leader prof. dr hab. A.K. Gajewski), and the Provost Fund of the Warsaw School of Economics.

References

  1. Alavi SH, Barari A, Ghazalian F, Bashiri J. Comparison of swimming exercise and amitriptyline drug use on depression. Ann Biol Res. 2011;2(4):206–211. [Google Scholar]
  2. Armstrong T, Bauman A, Davies J. Physical activity patterns of Australian adults. Canberra: Australian Institute of Health and Welfare; 2000. Results of the 1999 National Physical Activity Survey. [Google Scholar]
  3. Australian Bureau of Statistics. Participation in Sport and Physical Activities. Australia: 2002. Canberra 2003. [Google Scholar]
  4. Barter T. Effective use of secondary school swimming pools. Swimming Times. 1992;69(1):17–19. [Google Scholar]
  5. Biernat E, Tomaszewski P. Determinants of physical activity of working Warsaw residents aged 60 to 69 years. J Hum. Kinet. 2011;30:107–115. doi: 10.2478/v10078-011-0085-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Biernat E, Tomaszewski P, Milde K. Physical activity of office workers. Biol Sport. 2010;27(4):289–296. [Google Scholar]
  7. Bittman M, Wajcman J. The rush hour: The character of leisure time and gender equity. Soc Forces. 2000;79(1):165–89. [Google Scholar]
  8. Cardinal BJ. Role Modeling attitudes and physical activity and fitness promoting behaviors of HPERD professionals and preprofessionals. Res Q Exerc Sport. 2001;72:84–91. doi: 10.1080/02701367.2001.10608937. [DOI] [PubMed] [Google Scholar]
  9. Cardon GM, Haerens LE, Verstraete S, de Bourdeaudhuij I. Perceptions of a School-Based Self-Management Program Promoting an Active Lifestyle Among Elementary Schoolchildren, Teachers, and Parents. JTPE. 2009;28:141–154. [Google Scholar]
  10. Central Statistical Office. Participation of Poles in sports and physical recreation in 2008. Warsaw: 2009. [Google Scholar]
  11. Csikszentmihalyi M. Flow: The Psychology of Optimal Experience. New York: Harper and Row; 1990. [Google Scholar]
  12. Gwinup G. Weight loss without dietary restriction: Efficacy of different forms of aerobic exercise. Am J Sport Med. 1987;15:275–279. doi: 10.1177/036354658701500317. [DOI] [PubMed] [Google Scholar]
  13. Hagströmer M, Bergman P, Bauman A, Sjöström M. The international prevalence study (IPS): health-enhancing physical activity in Sweden. J Public Health. 2006;14:307. [Google Scholar]
  14. Hastings DW, Kurth SB, Schloder M, Cyr D. Reasons for participating in a serious leisure: comparison of Canadian and U.S. masters swimmers. Int Rev Soc Sport. 1995;30:101–119. [Google Scholar]
  15. Heilbrun J, Gray C. The Economics of Art and Culture: An American Perspective. Cambridge University Press; Cambridge: 1993. [Google Scholar]
  16. Henderson K, Bialeschki D, Gibson H, Huidi M, Kelly J. Gender inclusion: still an issue after all these years?; Trends 2000: Shaping the future. The 5th Outdoor Recreation and Tourism Trends Symposium; Lansing, MI. September 17–20, 2000. [Google Scholar]
  17. Ifedi F. Culture, Tourism and the Centre for Education Statistics. Canada: 2008. Sport Participation in Canada, 2005. [Google Scholar]
  18. International Standard Classification of Occupations. Available online at: ( http://www.ilo.org/public/english/bureau/stat/isco/isco68/major.htm); accessed on 01.12.2011.
  19. Jennings M. The Truth About Swimming and Weight Loss. Available online at: ( http://mens-total-fitness.com/swimming-weight-loss.html); accessed on 01.12.2011.
  20. Katzmarzyk P. Physical activity and fitness with age among sex and ethnic groups. In: Bouchard C, Blair SN, Haskell W, editors. Physical activity and health. Human Kinetics; Champaign, IL: p. 2007. [Google Scholar]
  21. Landi F, Cesari M, Onder G, Lattanzio F, Gravina EM, Bernabei R. Physical activity and mortality in frail, community-living elderly patients. The Journals of Gerontology Series A, Biol Sci Med Sci. 2004;59(8):833–837. doi: 10.1093/gerona/59.8.m833. [DOI] [PubMed] [Google Scholar]
  22. Lobelo F, Duperly J, Frank E. Physical activity habits of doctors and medical students influence their counseling practices. J Sports Med. 2009;43:89–92. doi: 10.1136/bjsm.2008.055426. [DOI] [PubMed] [Google Scholar]
  23. Lubowiecki-Vikuk AP. Determinants of recreational and tourism activity among people living alone/‘singles’ in Greater Poland Voivodeship. AWF; Poznań: 2011. [Google Scholar]
  24. Mensink GB, Ziese T, Kob FJ. Benefits of leisure-time physical activity on the cardiovascular risk profile at older age. Int J Epidemiol. 1999;28(4):659–666. doi: 10.1093/ije/28.4.659. [DOI] [PubMed] [Google Scholar]
  25. Merriman N. Beyond The Glass Case: The Past, The Heritage and The Public. Left Coast Press; Walnut Creek, California: 2000. [Google Scholar]
  26. Miller Y, Brown W. Determinants of Active Leisure for Women with Young Children – An ‘Ethic of Care’ Prevails. Leisure Sciences. 2005;27(5):405–420. [Google Scholar]
  27. Mummery K, Schofield G, Caperchione C. Physical activity dose-response effects on mental health status in older adults. Aust NZ J Publ Heal. 2004;28(2):188–192. doi: 10.1111/j.1467-842x.2004.tb00934.x. [DOI] [PubMed] [Google Scholar]
  28. Parker C. A Comparative Historical Study. Refereed. Australian Association for Research in Education (AARE); Adelaide: 2006. Swimming Education in England. Paper. [Google Scholar]
  29. Stephenson J, Bauman A, Armstrong T, Smith B, Bellew B. The costs of illness attributable to physical inactivity in Australia: a preliminary study. Commonwealth Department of Health and Aged Care and the Australian Sports Commission; Canberra: 2000. [Google Scholar]
  30. Sallis J, Owen N. Physical activity and behavioural medicine. SAGE Publications Inc; California: 1999. [Google Scholar]
  31. Sjöström M, Oja P, Hagströmer M, Smith BJ, Bauman A. Health-enhancing physical activity across European Union countries: the Eurobarometer study. J Public Health. 2006;14(5):291–300. [Google Scholar]
  32. The WHO Report 2002. Reducing Risk, Promoting Healthy Life. Geneva: WHO; 2002. [Google Scholar]
  33. Takkinen S, Suutama T, Ruoppila I. More meaning by exercising? Physical activity as a predictor of a sense of meaning in life and of self-rated health and functioning in old age. J Aging Phys Activ. 2001;9(2):128–141. [Google Scholar]
  34. Villeneuve PJ, Morrison HI, Craig CL. Physical activity, physical fitness and risk of dying. Epidemiology. 1998;9(6):626–631. [PubMed] [Google Scholar]
  35. Zarotis GF, Katsagolis A, Mitrotasios M. Free time and its alteration tendencies. Studies in Physical Culture and Tourism. 2007;14(2):182. [Google Scholar]
  36. Money.pl Available online at: (http://www.money.pl/); accessed on 01.12.2011

Articles from Journal of Human Kinetics are provided here courtesy of Academy of Physical Education in Katowice, Poland

RESOURCES