Data on self-reported frequency of exercising or playing sport of adults aged 15 and above in 27 EU countries were collected, from the European Commission’s Special Eurobarometer. A graphical output was obtained using classical a statistical methodology known as metric Multidimensional Scaling method to better define the interrelationships between a large set of variables for the data from the 27 European countries and “average” country included in the study. People in Sweden, Denmark and Finland had the highest level of exercise and playing sport level. High level of exercise and play sport level were detected in Slovenia, the Netherlands, Belgium, Germany, Luxembourg, the United Kingdom and France while low level of exercise and play sport level were found in Romania, Hungary, Italy, Poland and Portugal. The lowest level of exercise and play sport was observed in Bulgaria and Greece. The groups of countries that result from this classification also are characterized by the extent of the difference between the lowest levels of activity (never practising) and the highest (regularly practising); Austria, Czech Republic and Slovakia, have the highest proportion of people who seldom practising. In 4 countries, Ireland, Malta, Republic of Cyprus and Portugal, the proportion of citizens who practice exercise or play sport regularly or never (extreme behaviour) is high. This study shows what a high level and regularly of exercise and playing sport are associated with adults participating in education and training, satisfaction with household financial situation and kind of work activity.
Physical activity is closely related to the morbidity and mortality in developed countries, where non-communicable diseases have increased rapidly as reported by World Health Organization (WHO) [
Regular physical activity reduces the risk of cerebrovascular and coronary events according to Guillumet et al. (1996) [
At present, large amounts of data have to be handled in quantitative analysis of physical activity. As a result, demand for multivariate data processing methods is increasing, using taxonomic methods and graphs [
Physical activity is defined as any bodily movement produced by skeletal muscles that require energy expenditure. This includes occupational physical activity, physical activity at home and physical activity related to commuting. We have mentioned some issues related to sport in Europe, but few studies examine moderate physical activity (including that previously mentioned), although it is important. Physical activity in the European Union has been a topic at conferences (see e.g. Schriften der DeutschenVerinigungfür Sportwissenschaft in Leipzig [
In this study, we analysed the exercise and play sport level of citizens (all adults aged 15 and above) of the European Union, by means of a graphic representation, using metric Multidimensional Scaling (MDS) [
This work has allowed us to classify the countries of the E.U., according to level of physical activity behaviour. We have also related it with social aspects of the populations.
The data presented in this study were provided by the European Commission [
Each country has 4 variables for exercise and play sport level. Each country is represented by one n-dimensional random vectors
A powerful multivariate methodology was used in this study. In
Each country was assigned the vectors
If we consider
Multidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a dataset. It refers to a set of related ordination techniques used in information visualization, in particular to display the information contained in a distance matrix. An MDS algorithm aims to place each object in N-dimensional space such that the between-object distances are preserved as well as possible. Each object is then assigned coordinates in each of the N dimensions [
Based on the Bhattacharyya distances obtained, a MDS was performed. The distance matrix between countries,
countries i, j respectively.
1)', is a column vector of size 27. B = HAH, is the sample matrix of covariance of the values of A. Their eigenvalues are:
where the ith column of T is the ith eigenvector associated with λi. If
the rows of Y are the coordinates of 27 points (Q1, ∙∙∙, Q27) on R27. If B is a non negative definite matrix:
If B is a not nonnegative definite matrix, we obtain pure imaginary values when calculating
If
Then we obtain the coordinates of the EU country, considering EU as the country 28. We calcúlate
If we consider the vector
Then we performed a MDS analysis with the 28 points (Q1, ∙∙∙, Q28). This method allowed us to construct a diagram showing the relationships between a numbers of objects. The diagram is a small p-dimensional space, generally p = 2 or p = 3. The main purpose is to reduce data from a large number of variables to fewer components, so making it possible to view the similarities and differences between the countries studied [
All analyses were performed using the function cmdscale() from the R package HSAUR as reported by Venables & Ripley (2002) [
The MDS produced a two-dimensional solution. The MDS is summarised in
“First component” reflects the differences between countries, indicating the general level of activity. On the left-hand side of
Eigenvalues | |
---|---|
Eigenvalues (λi) for all axes | λ1 = 8.929562e−01 λ2 = 1.789157e−01 λ3 = 9.874179e−02 λ4 = 1.452291e−03 λ5 = 1.142556e−05 λ6 = 7.109765e−06 λ7 = 2.254935e−06 λ8 = 1.368391e−06 λ9 = 3.871012e−07 λ10 = 5.068683e−08 λ11 = 2.059860e−08 λ12 = 2.539000e−09 λ13 = 1.378020e−10 λ14 = 3.803231e−17 λ15 = −1.234949e−11 λ16 = −1.702620e−10 λ17 = −3.407420e−10 λ18 = −7.284797e−10 λ19 = −2.172229e−09 λ20 = −6.728008e−09 λ21 = −5.675427e−08 λ22 = −1.464371e−07 λ23 = −4.386321e−06 λ24 = −5.880097e−05 λ25 = −1.622111e−04 λ26 = −4.975911e−04 λ27 = −9.651985e−04 λ28 = −2.326658e−03 |
Criterion of Mardia for the first two axes | |
Inertia percentages for the first two axes | |
Inertia percentages for the first axe | |
Inertia percentages for by the second axe |
Coordinate 1 | Coordinate 2 | |
---|---|---|
Austria | 0.02492464 | 0.152486259 |
Belgium | 0.10815252 | −0.014315083 |
Bulgaria | −0.31362222 | 0.076267867 |
Cyprus R. | −0.04334258 | −0.148628218 |
Czech Rep. | −0.08124003 | 0.148153734 |
Denmark | 0.25004567 | −0.016519268 |
Estonia | −0.06184680 | 0.040413606 |
Finland | 0.40479139 | 0.052827673 |
France | 0.06073667 | −0.051132247 |
Germany | 0.07927573 | 0.002547291 |
Greece | −0.30320816 | −0.062052135 |
Hungary | −0.19695709 | 0.027215645 |
Ireland | 0.16639144 | −0.113408591 |
Italy | −0.17306903 | −0.031825383 |
Latvia | −0.12168266 | 0.060361327 |
Lithuania | −0.06374756 | −0.058838659 |
Luxembourg | 0.08741280 | −0.050327203 |
Malta | 0.03848483 | −0.125599002 |
Netherlands | 0.11983252 | 0.004783213 |
Poland | −0.16679936 | 0.031829202 |
Portugal | −0.14942343 | −0.142940715 |
Romania | −0.20296363 | 0.039881588 |
Slovakia | −0.05728946 | 0.152649512 |
Slovenia | 0.15903178 | 0.055749693 |
Spain | −0.03361379 | −0.052790663 |
Sweden | 0.42172793 | 0.043780436 |
U. Kingdom | 0.06175753 | −0.013569575 |
UE: Average | −0.01005042 | 0.007000305 |
who practice exercise or play sport regularly in greater proportion. The first component was clearly positive values (indicating highest exercise and play sport level) in 3 countries (Sweden, Finland and Denmark). In 10 countries (Slovenia, The Netherlands, Belgium, Luxembourg, Germany, France, United Kingdom, Austria, Ireland and Malta), the first component was positive values, indicating high exercise and play sport level. The first component was clearly negative values (indicating the lower exercise and play
sport level) in 2 countries (Bulgaria and Romania). In 5 countries (Romania, Hungary, Poland, Italy and Portugal), the first component was negative values, indicating low exercise and play sport level. In 7 countries (Latvia, Estonia, Lithuania, Spain, The Czech Republic, Slovakia and Republic of Cyprus) the first component was moderate negative values (indicating moderate low exercise and play sport level).
“Second component” reflects the differences between countries, indicating the percentages of people with extreme attitudes: they either practise exercise or play sport regularly or, on the contrary, never practise exercise or play sport, or percentage of people who practise exercise or play sport seldom, but not never.
The second component was positive (indicating that the proportion of citizens who practice exercise or play sport regularly or never is low) in 3 countries (The Czech Republic, Slovakia and Austria), and negative (indicating that the proportion of citizens who practice exercise or play sport regularly or never is high) in 4 countries (Ireland, Malta, Republic of Cyprus and Portugal). Then we identified seven population groups:
Group I: Sweden, Finland and Denmark.
Group II: Group IIa: Slovenia, The Netherlands, Belgium, Luxembourg, Germany, United Kingdom and France. Group IIb: Austria. Group IIc: Ireland and Malta.
Group III: Group IIIa: Latvia, Estonia, Lithuania and Spain. Group IIIb: The Czech Republic and Slovakia. Group IIIc: Republic of Cyprus.
Group IV: Group IVa: Romania, Hungary, Poland and Italy. Group IVb: Portugal.
Group V: Bulgaria and Greece.
In this study, graphs were used to plot the data on physical activity levels in various countries. “Dimension 1” represents the exercise and play sport level studied. The “Dimension 2” represents “behaviour”, i.e. people’s extreme attitudes, practice exercise or play sport, they do it, never or regularly, or not extreme attitudes, seldom.
In those countries with points whose first dimension has a high positive value, it is interpreted that people have regularly attitudes on exercise and play sport. Conversely, if the coordinates of the points have a lower-value first dimension, this is interpreted as indicating that most of the population never or seldom practise.
This study shows differences in physical activity regularly in selected European countries. It can be seen that Bulgaria and Greece, particularly, and Romania, Hungary, Poland, Italy and Portugal are the countries where the proportion of people spent less time on exercise and play sport is lowest. These are the European Southern countries, together with Bulgaria, Romania, Hungary and Poland. The groups also includes the countries in which people are with financial problems. On the other hand, the Group I (the Nordic countries) and Group II are the countries where the proportion of people spent more time on exercise and play sport is largest, includes the countries in which the people think their household financial situation is good according to European Commission (2011) [
People think their household financial situation is good: Sweden (90%), Finland (88%) and Denmark (89%). On the other hand, Bulgaria (28%) and Greece (24%). Therefore we consider that physical activity is associated with percentage of people what think their household financial situation is good.
Regular physical activity is linked to employment development of the non-agricultural sector. The percentage of employment in secondary and tertiary (% total employment) of Sweden (97.9%), Finland (95.1%) and Denmark (97.4%), On the other hand, Bulgaria (80.4%), Greece (88.8%), Poland (86.7%), Portugal (88.9%), Romania (69.9%) [
Regular physical activity is linked to percentage of adults participating in education and training: Sweden (25%), Finland (23.7%) and Denmark (32.3%), Luxembourg (13.5%), The Netherlands (16.7%), Austria (13.4%), Slovenia (15.9%). On the other hand, Bulgaria (1.2%), Greece (2.4%), Poland (4.5%) and Romania (1.5%). Data available at: http://ec.europa.eu/agriculture/sites/agriculture/files/statistics/rural-development/2011/ch35_en.pdf.
This study provides a summary of data corresponding to 4 variables using a single standard graph. The graph output allowed us to classify European countries according to physical activity regularity. Using these graphical methods, we were able to study the relationship between physical activity regularity taken by people and the features that give them their identity: People (percentage) think that their household financial situation is good, percentage of employment in secondary and tertiary (% total employment) or percentage of adults participating in education and training.
This study also aims to encourage future researchers to present information accompanied by multivariate methods to appropriately summarise and describe this information.
This work is partially supported by grant 2014 SGR 464 (GRBIO) from the Departament d’Economiai Coneixement de la Generalitat de Catalunya (Spain).
Ríos, D., Monleón- Getino, T., Cubedo, M. and Ríos, M. (2016) A Graphical Classification of European Coun- tries According to Physical Activity Level of Its Citizens. Open Access Library Journal, 3: e3195. http://dx.doi.org/10.4236/oalib.1103195