Literature Review on Sport Participation and Socio Economic Status

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• Published: 29 April 2015

Participation in sport and physical action: associations with socio-economic status and geographical remoteness

BMC Public Health book 15, Article number:434 (2015) Cite this article

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Abstract

Background

Many factors influence participation in sport and Physical Activity (PA). Information technology is well established that socio-economic status (SES) is a disquisitional cistron. There is also growing bear witness that there are differences in participation patterns according to residential location. Withal, little is known more specifically about the relationship of PA participation and frequency of participation in item contexts, to SES and residential location. This study investigated the human relationship of participation, and frequency and context of participation, to SES and location.

Methods

Three aspects of participation were investigated from information collected in the Exercise, Recreation and Sport Survey (ERASS) 2010 of persons aged 15+ years: whatever participation (yes, no), regular participation (<12 times per yr, ≥12 times per year) and level of organisation of participation setting (not-organised, organised non-club setting, club setting).

Results

The rates of both any and regular PA participation increased as SES increased and decreased equally remoteness increased. Yet, participation in PA was SES- or remoteness-prohibitive for only a few types of PA. As remoteness increased and SES decreased, participation in many team sports actually increased. For both SES and remoteness, there were more significant associations with overall participation, than with regular participation or participation in more organised contexts.

Conclusions

This study demonstrates the complexity of the associations between SES and location beyond different contexts of participation. Still, information technology seems that in one case initial date in PA is established, SES and remoteness are not critical determinants of the depth of engagement.

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Background

There is an abundance of knowledge of the wide range of influences on participation in physical activity (PA). In accordance with the Socio-Ecological model, these influences or determinants of participation tin relate to intrapersonal, interpersonal, organisational, environmental, and policy factors [i,2].

One key influence on participation is Socio-Economic Status (SES). This determinant impacts upon many PA determinants beyond a number of the Socio-Ecological model's domains [iii]. Information technology is consistently reported in both quantitative and qualitative studies that people with higher SES are more than probable than those with lower SES to participate in PA, and more specifically in sport [4-vii].

A qualitative study of adults in the Netherlands, The states and South korea institute that some barriers to PA and sport participation were consistently reported across all three countries. Along with time pressure, toll was articulated consistently throughout equally a barrier to PA participation [8]. In addition to individual and household SES, there is prove that neighbourhood SES is also related to PA participation. There is evidence that higher SES neighbourhoods have significantly more than PA facilities than lower SES neighbourhoods, thus providing more opportunities to be physically active [9]. Furthermore, low SES neighbourhoods were found to take significantly fewer free-for-apply facilities than loftier SES neighbourhoods [ix].

In that location are also differences amongst participation levels and trends according to different geographical regions [10-12]. Information technology is non uncommon for studies to report PA differences according to residence in metropolitan or regional/rural locations [11,12]. There are also reports of variations of PA levels within country capital letter cities [ten] and between different regional communities [11].

Many studies that do report PA co-ordinate to different geographical regions, employ very broad definitions, for example northern and southern regions of a state [half dozen]. While specific measures of location or remoteness exist, these take rarely been used in enquiry in this expanse. ARIA+ is a geographical measure out of remoteness for Australia [xiii]. A study that adopted this mensurate of remoteness investigated PA levels amidst adolescents [fourteen]. Both males and females living in major cities reported significantly lower moderate and vigorous PA (MVPA) minutes than males and females living in whatsoever other type of region. Participation in sport, however did not differ across regional classifications [xiv].

In terms of health-enhancing PA, frequency of participation is a fundamental component. It is also of import to understand the context of participation. Some studies incorporate frequency equally a measure out, especially when categorising individuals every bit coming together or failing to meet the recommended or health-enhancing levels of PA [10]. I of import attribute of the context of leisure-time PA has been termed 'mode' [15], the iv modes being: team sport, individual sport, organised simply non-competitive PA, and non-organised PA [15]. In that location are likely to be differences in participation trends across these modes, however piffling attention has been paid to specific modes across the written report of adolescents past Eime and colleagues [xv].

In summary, many factors influence participation in sport and PA. It is well established that SES is a critical cistron. In that location is also growing evidence that in that location are differences in participation patterns according to residential location. Notwithstanding, little is known more specifically about the relationship of PA participation, and frequency of participation in particular contexts, to SES and residential location.

This report investigates the association of participation, and regularity and organisational context of participation, with SES and location.

Methods

Data collected in the Do, Recreation and Sport Survey (ERASS), 2010 was obtained. The usefulness of the ERASS survey from a public health perspective has been established [ten,16]. Importantly, it is useful every bit a national surveillance of habitual PA behaviours and specifically identifies the types of activities undertaken [16]. Information technology has likewise been used to determine adult participation trends in Leisure Fourth dimension Physical Activity (LTPA) according to city of residence [ten].

Quarterly survey samples for ERASS were selected from all persons anile 15 years and over, living in occupied private dwellings using Computer-assisted Telephone Interviewing. In each quarter approximately 3,400 persons were sampled Australia-wide from all states and territories. Verbal informed consent was indicated by the respondents' willingness to participate in the telephone survey. De-identified data from the 2010 survey menstruation were analysed in this investigation. Ideals approval was granted by the University Human Research Ethics Committee.

Respondents were first asked whether they participated in any PA during the 12 months prior to the survey. Those who had done and so were asked to nominate up to 10 types of PA from a classification of 95 types (e.thousand. basketball, lawn tennis, aerobics, walking), including both sports (divers as a physical activity that by its nature has a sport governing trunk and by its nature and system, is competitive and is generally accepted as being a sport)[17] and other forms of recreational PA. Hence, for each of the 95 ERASS PA types, each respondent was classified equally a participant or a non-participant. For participants in each PA blazon, two further aspects of participation were investigated: frequency of participation in the 12 months prior to the survey and level of organisation of participation setting (non-organised, organised non-club setting, club setting). After consultation with sport governing bodies, regular participation was defined as at least 12 times in the 12 months prior to the survey, i.e. at least monthly on boilerplate. With regard to level of system, a person can engage in a detail type of PA in more than one setting. In accord with the hierarchical precedence of participation settings articulated by Eime et al. [18] all persons who participated in a club setting were classified as club participants, regardless of whether they also participated in other settings. Of those remaining, persons who participated in an organised non-club setting were classified as organised non-club participants, regardless of whether they also participated in non-organised settings. Those remaining participated in simply non-organised settings, and were classified as such.

Thus, the following PA indicators (outcome variables) were defined for each respondent: participation in any type of PA (yes/no), participation in each of 95 types of PA (yep/no), regular participation in upwards to ten types of PA (yes: ≥12 times per yr, no: <12 times per yr), and level of arrangement of participation in up to 10 types of PA (not-organised, organised non-social club setting, society setting).

Socio-economic status was represented by the Australian Bureau of Statistics (ABS) Socio-economic Indices for Areas (SEIFA) Index of Relative Socio-economic Advantage and Disadvantage (IRSAD) [19].The SEIFA IRSAD value assigned to each respondent was the 2011 SEIFA value assigned past ABS to the residential postcode of the respondent [xix]. For ease of estimation, analysis was based on ERASS quintiles of SEIFA IRSAD. SEIFA IRSAD scores are centred on g, with a range in the 2010 ERASS sample from 619.55 to 1164.41. The four quintile cutoffs for the 2010 ERASS sample were 938.80, 979.77, 1019.46 and 1065.35.

Access to services and remoteness was represented by 5 standard categories based on the Access and Remoteness Alphabetize for Australia (ARIA+) [13]. These categories are: Major cities, Inner regional, Outer regional, Remote and Very remote. The ARIA+ category assigned to each respondent was the 2011 ARIA+ category assigned to the residential postcode of the respondent. Because the sample sizes in the 2 most remote of the five ARIA+ categories were minor (see Table 1), the ARIA+ mensurate was collapsed into 3 categories: major cities, inner regional, and other (i.e. outer regional, remote, very remote).

Table 1 - Respondent Characteristics

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All analyses used ERASS data weighted at the country, region (metropolitan, residuum of state), age group and gender levels. Analyses were conducted using SPSS Version 21. Analyses were conducted of the relationship of each of the three event variables with the ii predictors (SEIFA quintile and ARIA category).

For the 2 dichotomous outcome variables (participation, regular participation), binary logistic regression was used to investigate the relationship with each of the two predictors. The results were expressed in terms of rates: the participation rate, estimated by the proportion of the sample who reported participating, and the rate of regular participation, estimated by the proportion of participants who were regular participants. Whatsoever pregnant relationship was further investigated to determine the nature of that relationship. The method of polynomial contrasts was used to break down the (frequently curvilinear) relationship between the log odds of the issue and the predictor into a linear component and any second-, third- or fourth-club components, each of which was independently assessed for statistical significance. For reporting purposes, the relationships identified were classified every bit positive linear, negative linear or non-linear. Positive and negative linear relationships were defined by the sign of the log-odds value. A not-linear relationship was mostly a second-, third- or fourth- order human relationship, perchance superimposed on a linear trend. However, in a few cases an overall statistically significant relationship was shown only this could not be characterised into a polynomial pattern.

For the ordinal result variable (level of organisation), crosstabulation analysis was conducted, and Goodman and Kruskal's gamma, designed to mensurate the concordance of two ordinal variables known to have tied observations, was used to place whatsoever statistically pregnant association between each predictor and the level of organization. For reporting purposes, a positive relationship was divers as a positive value of Goodman and Kruskal'southward gamma between predictor and outcome variable, while a negative human relationship was defined equally a negative value of Goodman and Kruskal's gamma.

Results

Table one summarises the gender, age, SEIFA IRSD and ARIA+ profiles of: ane) all ERASS 2010 survey respondents; and 2) those respondents who reported participating in recreational PA in the twelve months prior to the survey. Table 2 shows the nature of the relationship between each of the three PA participation indicators, with PA aggregated across all 95 types of PA, and the two predictors. For this aggregated analysis, a participant was classified as a regular or non-regular participant on the basis of the highest frequency they reported across all of their (upward to ten) reported PA types. Similarly, the level of arrangement was assigned to each participant on the footing of the highest level of organisation reported across all of their reported PA types. Table 2 shows that the rates of both PA participation in general and regular PA participation increased as SES (SEIFA IRSD quintile) increased and decreased every bit remoteness (ARIA+ category) increased. In the case of SEIFA IRSAD, there was a linear trend with some non-linearity superimposed. Conversely, the level of organisation of PA context increased as remoteness increased, and decreased as SES increased.

Table 2 - Relationships between three PA participation indicators and two predictors

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The subsequent tables summarise the nature of the human relationship betwixt the two predictors and each of the three PA participation indicators, separately for each of the 95 types of PA.

Table 3 shows the nature of the relationship between the rate of whatever participation in PA and SEIFA quintile. Forty two of the 95 types of PA were shown to have a statistically significant relationship between rate of participation and SEIFA IRSAD quintile. Twenty v had a positive linear human relationship betwixt charge per unit of participation and SEIFA IRSAD quintile, with 16 of these also having a superimposed non-linear human relationship. 10 had a negative linear relationship between rate of participation and SEIFA IRSAD quintile, of which vii also had a superimposed not-linear relationship. 5 had a solely non-linear relationship between participation and SEIFA IRSAD quintile, with no significant linear trend. Additional file ane: Tabular array S3A provides detailed examples illustrating different patterns of relationship. It should be borne in mind that the term "linear" applies to the human relationship betwixt the log odds of participation (not the odds of participation) and the SEIFA quintile.

Table three - Relationship betwixt charge per unit of participation in particular types of physical activity and quintiles of SEIFA IRSAD

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Table iv shows the nature of the relationship between rate of regular participation and SEIFA IRSAD quintile. Xx one of the 95 types of PA were shown to have a significant relationship between rate of regular participation and SEIFA IRSAD quintile. Of these, one had a 'purely' positive linear relationship, three had a 'purely' negative linear relationship, and two had a negative only non-linear relationship. The remaining 13 had non-linear relationships with no linear component. Additional file i: Table S4A provides detailed examples illustrating different patterns of relationship.

Table 4 - Human relationship betwixt rate of regular ¹ participation in item types of physical activity and quintiles of SEIFA IRSAD

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Table 5 shows the nature of the relationship between level of arrangement of participation and SEIFA IRSAD quintile. Because level of organisational participation is an ordinal variable (as opposed to a quantitative rate associated with a binary variable), the issue of linearity does not apply. Xx of the 95 types of PA were shown to take a meaning relationship (concordance) betwixt level of system of PA and SEIFA IRSAD quintile. Six concordances were positive and fourteen were negative.

Table five Relationship betwixt level of organisation ¹ of particular types of physical activity and quintiles of SEIFA IRSAD

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Table half dozen shows the nature of the human relationship between rate of any participation in PA and ARIA+ category (numbered from 1–3, with 1 being the major cities). Xxx two of the 95 types of PA were shown to have a significant relationship betwixt rate of participation and ARIA+ category. Xv had a positive linear relationship between rate of participation and ARIA+ category, with 7 of these besides having a superimposed non-linear relationship. Eleven had a negative linear relationship between charge per unit of participation and ARIA+ category, of which 4 also had a superimposed non-linear relationship. Iii had a purely non-linear relationship between participation and ARIA+ category, with no significant linear tendency. Additional file 1: Table S6A provides detailed examples illustrating different patterns of human relationship.

Table 6 Relationship between rate of participation in particular types of physical activity and ARIA+ remoteness category

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Table 7 shows the nature of the relationship between the rate of regular participation and ARIA+ category. 15 of the 95 types of PA were shown to have a significant relationship between rate of participation and ARIA+ category. Five had a positive linear relationship betwixt rate of regular participation and ARIA+ category, with two of these too having a superimposed non-linear relationship. Three had a negative linear relationship between rate of participation and ARIA+ category, of which one also had a superimposed non-linear relationship. 5 had a purely non-linear human relationship between participation and ARIA+ category, with no significant linear tendency. Additional file i: Table S7A provides detailed examples illustrating different patterns of relationship.

Tabular array 7 Relationship between rate of regular ¹ participation in particular types of physical activeness and ARIA+ remoteness category

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Table viii shows the nature of the human relationship betwixt level of organization of participation and ARIA+ category. Seventeen of the 95 types of PA were shown to have a significant relationship (concordance) between level of organisation of PA and ARIA+ category quintile. 14 concordances were positive and three were negative.

Table 8 Relationship between level of organisation ¹ of physical particular types of activity and ARIA+ remoteness category

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Discussion

This written report provides detailed information about the associations between participation in particular sports and concrete activities and measures of SES and location. It demonstrates the complication of these associations beyond unlike contexts of participation.

SES

Many studies have shown a broad association betwixt higher SES and higher levels of PA and sport [5,vi,20,21] and the present written report confirms this positive overall association, both for any recreational PA participation in a 12-month period and for regular participation in some form of PA over that period. However, more specifically this study demonstrates that simply 42 (44%) of the 95 specific types of PA showed a significant association between participation and neighbourhood SES. Furthermore, in even fewer cases (n = 25; 26%) was the association positive, with loftier/depression participation being associated with loftier/low SES.

SES tin be defined in terms of individual, household and neighbourhood characteristics. The socioeconomic inequalities in sport participation have been explained by a combination of individual, household and neighbourhood factors [20]. Lower PA levels have been associated with lower neighbourhood and household SES (education, income) [5]. Participation in club sport past boyish females has been significantly positively associated with neighbourhood and household measures of SES, particularly in metropolitan compared to regional/rural areas [7].

In the nowadays report, for each of 95 different types of sport or PA, a neighbourhood SES measure was associated with charge per unit of participation, rate of regular participation and level of system of the context of participation. Significant associations were observed betwixt SES and a minority of activities - for whatsoever participation (42 activities), regular participation (21 activities) and participation in organised contexts (20 activities).

At that place were relatively few (north = 25) activities for which the charge per unit of any participation increased as SES increased. For only two activities (Athletics/Rails and Field, and Basketball) did the rate of regular participation increase as SES increased. For six activities, the proportion participating in more organised contexts increased as SES increased. For all three aspects of participation, the positive associations between participation and SES generally occurred for 'niche' sports and activities (such as boating/kayaking, rock climbing, rowing) rather than the more popular 'mainstream' sports (such every bit cricket, netball).

There were similar minorities of sports exhibiting negative relationships between the three aspects of participation (any, regular, level of organisation) and SES. Of the activities showing a negative relationship between whatever participation and SES, the bulk were team sports. Negative relationships between participation in more organised contexts and SES were besides more than likely to exist for team sports, such every bit Australian rules football, basketball, football, hockey, netball and tennis.

From the numbers of sports listed in Tables iii,4,five, it would seem that SES is a meaning correlate of participation in but a minority of sports, and is more likely to be associated with participation in general rather than for regular participation or participation in more organised contexts. Further, more complex non-linear relationships predominate over clear positive and negative trends. This contrasts somewhat with the positive overall association observed in this study, and in other studies that take reported significantly higher rates of PA in full general, and organised sport participation in particular, for higher SES compared to lower SES [six,21]. Clearly, the full general positive relationship betwixt SES and participation does non apply uniformly to all types of sport and PA..

Further test suggests that types of PA which: are undertaken indoors; are probable to crave expensive infrastructure or equipment; or crave access to water or snow, were more likely to exhibit positive relationships betwixt participation and SES. Indoor activities such every bit yoga oftentimes require a fee for each participation session, in contrast to many club sports which have a yearly membership rather than an individual pay-and-play system. The cost of equipment is often a determinant of participation [vi,22]. It is a mutual finding that people of higher SES take better access to PA and sports facilities, tin afford to alive in a PA-friendly surroundings and have fewer barriers [23].

Studies investigating broad levels of PA have reported that access to low-cost recreation facilities tin significantly, positively influence PA levels [24]. Recent inquiry in Spain found that the odds for prevalence of concrete activity were lower in neighbourhoods of lower income [25]. The availability of sports facilities explained much of the excess prevalence in older years, but not for younger people [25]. Other studies have reported fewer facilities within lower SES compared to higher SES neighbourhoods, indicating that the physical environment hinders the power in the lower SES categories to admission PA opportunities [ix]. Furthermore, the admission to low-cost recreation facilities is non consistent and quite variable betwixt countries. In a comparing of 11 countries, availability of low-toll recreation facilities was to the lowest degree likely to be reported in Brazil and Columbia and virtually probable in Canada and New Zealand [24].

However the above, for some activities that can take very low participation costs (such as running and cycling) participation was positively associated with SES, although not in a conspicuously linear way. Whilst cycling tin be an expensive activity in terms of equipment, running does non incur expenses higher up and across shoes. We know that people with higher SES are probable to accept higher education, and it is reported that people with higher education take amongst other things, more social back up and greater capacity to seek, understand and deed on wellness messages that promote PA [23]. It may exist that activities such as cycling and running provide easy options that exercise not require skills, facilities nor other people to participate with. It may also be that people from higher SES neighbourhoods take a more aesthetic environment which is more conducive to running and/or cycling, or they may feel safer. Conversely, poor health, cost, unfamiliarity of PA facilities and programs, express social support and living in an unsafe neighbourhood are barriers to men from low SES being physically agile [26].

From an equity perspective, information technology is a positive finding that rates of participation in many physical activities are not positively associated with levels of SES. For some activities, participation decreased equally SES increased. These were predominantly organised team sports such as Australian rules football, basketball, cricket, hockey, netball and tennis. We tin can conclude that many traditional Australian team sports are either not associated with SES in a prohibitive mode, or in some cases are more likely to be participated in by people from lower SES areas.

ARIA

We found that the rates of both PA participation in general and regular PA participation decreased as remoteness (ARIA+ category) increased, and the level of organisation of PA context increased as remoteness increased. However, for specific activities, pregnant associations between participation levels and remoteness occurred in simply a minority of the 95 cases.

Significant associations were observed between remoteness and participation in general for 32 activities. However, for simply 11 activities did the charge per unit of participation decrease with increasing remoteness. For 15 activities, the rate of participation was higher in more remote areas; furthermore, these included some of the most popular mainstream sports – Australian rules football, cricket, netball, hockey and lawn bowls, every bit well equally typical rural PA pursuits such every bit fishing [27]. The activities for which participation rates declined with increasing remoteness included a number requiring indoor facilities – aerobics/fitness, indoor football, tenpin bowling, weight training and yoga, consequent with the notion that infrastructure differences betwixt metropolitan and rural settings can have an effect on participation [10].

For only a small proportion of activities (north = 15) was remoteness associated with the rate of regular participation, and the direction and shape of these relationships was mixed. There was however a much more consequent design with regard to level of organisation, with more organised participation in more than remote areas, again including some of the most popular mainstream sports – Australian rules football, basketball game, cricket, football, netball, hockey and tennis. This suggests that for these activities, sporting clubs and organisations tend to play a more important role in rural than metropolitan communities.

The limited research discussing differences across geographical locations suggests that in rural communities there is probable to be an emphasis on traditional team sports and more limited choices than those bachelor in metropolitan areas [12]. However, a study of younger people (9–16 years) establish that overall, time participating in organised sport did not differ for those living in major cities compared to regional and remote residents [fourteen]. However, recent research with adults has reported that PA levels are lower in regional communities than land averages [eleven]. This study also found that at that place were different PA patterns in dissimilar regional communities. The proportion of people reporting no activity was higher in some regions than others, which the authors suggested may be due to infrastructure for activity, as well as workplace policies and programs [11]. Another factor suggested by the respondents in this study is that their rural work and lifestyles required a considerable corporeality of PA already [eleven].

In summary, it is encouraging that participation in many traditional Australian team sports was not found to be positively associated with SES nor negatively associated with remoteness. Team sports are social in nature, and we know that people are inherently motivated to participate in sport for social opportunities [viii,11]. Team sport participation, in addition to producing physical health benefits tin can enhance psychological and social wellness [28]. A study across three unlike countries found that sport commitment systems that create social opportunities may be a primal to increased adult sport participation [8]. The social context of sport has likewise been identified as a mechanism for profitable men of low SES to overcome isolation [26]. These authors advocated the employ of sport every bit a vehicle to achieve social inclusion.

Strengths and Limitations

A strength of this study is that it is based on a very large national dataset. This is a double edged sword notwithstanding, in that the resulting high statistical ability may result in statistical significance in cases where the strength of the clan is insufficient to be of great practical importance. Also, because of the large number of significance tests conducted, it is acknowledged that some of the relationships identified as pregnant volition be spurious and due to Type ane errors, i.e. chance patterns of participation within the ERASS survey sample. However, the rate of results significant at the 0.05 level in each table far exceeds the chance rate of one in 20, indicating that most of the meaning results reported are valid and meaningful.

Another methodological limitation is that, considering the ERASS survey did not include questions about individual or household SES, the measure of SES used was based on postal area. Further, ERASS data are limited to persons aged 15 years or more. The patterns of human relationship between participation, SES and remoteness may be very unlike for children younger than fifteen years.

Conclusions

In conclusion, information technology is encouraging that few types of PA were cost- or remoteness-prohibitive in terms of participation. As remoteness increased and SES decreased, participation in many team sports really increased. For both SES and remoteness, at that place were more significant associations with overall participation, than with regular participation or participation in more organised contexts. This suggests that once initial date in PA is established, SES and remoteness are not critical determinants of the depth of engagement. Furthermore, it would seem inappropriate to generalise regarding SES and location. The level of contextual differentiation means that policies to promote PA participation based on generalisations may be poorly targeted. Information technology is of import that programs and policies designed to increase participation in PA take into business relationship the strong contextual factors.

Abbreviations

ABS:

Australian Bureau of Statistics

ARIA:

Accessibility/Remoteness Index of Commonwealth of australia

ERASS:

Exercise Recreation and Sport Survey

IRSAD:

Alphabetize of Relative Socio-economical Reward and Disadvantage

PA:

Concrete Activity

SEIFA:

Socio-economic Indexes for Areas

SES:

socio-economical condition

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Acknowledgements

We give thanks the Australian Sports Commission for providing access to the ERASS data. Rochelle Eime is supported by a VicHealth Research Practice Fellowship- Concrete Activity.

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Affiliations

1. Establish of Sport, Exercise and Agile Living, Victoria University, Melbourne, Australia

   Rochelle M Eime, Melanie J Charity & Jack T Harvey

2. School of Wellness Sciences and Psychology, Federation University, Ballarat, Australia

   Rochelle M Eime, Melanie J Clemency & Warren R Payne

Corresponding writer

Correspondence to Rochelle M Eime.

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Competing interests

The authors declare that they have no competing interests.

Authors' contributions

RE contributed to the study blueprint, interpretation of results, manuscript conceptualisation and grooming. MC and JH contributed to the study design, data direction, statistical assay and interpretation, manuscript conceptualisation and preparation. WP contributed to the report blueprint, interpretation of results, manuscript conceptualisation and preparation. All authors accept read and canonical the terminal manuscript.

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Additional file i:

Tabular array S1. List of 95 designated ERASS physical action types. Table S3A. Examples of different patterns of relationship between rate of participation in particular types of physical activity and quintiles of SEIFA IRSAD. Table S4A. Examples of unlike patterns of human relationship between rate of regular participation in item types of physical activity and quintiles of SEIFA IRSAD. Table S6A. Examples of unlike patterns of relationship between rate of participation in item types of physical activity and ARIA+ remoteness category. Table S7A. Examples of different patterns of human relationship between regular participation in particular types of concrete activity and ARIA+ remoteness category.

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Eime, R.Thou., Charity, 1000.J., Harvey, J.T. et al. Participation in sport and concrete activity: associations with socio-economical status and geographical remoteness. BMC Public Health 15, 434 (2015). https://doi.org/10.1186/s12889-015-1796-0

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• Received: 14 Jan 2015

• Accepted: 22 April 2015

• Published: 29 April 2015

• DOI : https://doi.org/10.1186/s12889-015-1796-0

Keywords

• Sport
• Concrete activity
• Socio-economic condition
• Rurality

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