Demographic characteristics and type/frequency of physical activity participation in a large sample of 21,603 Australian people

Demographic characteristics and type/frequency of physical activity participation in a large... Background: Regular physical activity (PA) is imperative for good health and there are many different ways that people can be active. There are a range of health, PA and sport policies aiming to get more people active more often. Much research has been directed towards understanding the determinants of inactivity and PA. However, it is important to understand the differences not only between inactive and active people, but also between activity contexts (for example participation in sport compared to non-sport activities), in order to align policies and strategies to engage market segments who have different participation preferences and accessibility. The aim of this study was to investigate demographic correlates of the propensity to be physically inactive or active within different contexts, and at different levels of frequency of participation. Methods: Data from the Australian Exercise, Recreation and Sport Survey was used for this analysis. This included information on the type, frequency and duration of leisure-time PA for Australians aged 15 years and over. Reported PA participation in the two-week period prior to the survey was used to allocate respondents into three categories: no PA, non-sport PA only, and sport. Subsequently, sport participants were further categorised according to frequency of participation. Potential demographic correlates included sex, age, education, employment, marital status, language spoken, having a condition that restricts life, children, and socio-economic status. Results: The survey included 21,603 people. Bivariate chi-squared analysis showed that there were significant differences between the profiles of leisure-time PA participation across all demographic variables, except the variable languages spoken at home. Ordinal regression analysis showed that the same demographic variables were also correlated with the propensity to engage in more organised and competitive PA contexts, and to participate more frequently. Conclusions: People who were female, older, married or had a disability were less likely to participate in sport. Therefore when designing PA opportunities to engage those who are inactive, particularly those that are organised by a club or group, we need to ensure that appropriate strategies are developed, and tailored sport products offered, to ensure greater opportunities for increased diversity of participation in sport. Keywords: Sport, Leisure-time physical activity, Demographic correlates * Correspondence: r.eime@federation.edu.au School of Health Sciences and Psychology, Federation University, Ballarat, Australia Institute of Sport, Exercise and Active Living, Victoria University, Melbourne, Australia Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Eime et al. BMC Public Health (2018) 18:692 Page 2 of 10 Background proportion of people regularly playing sport”– Sport Being physically active is important for overall health in- England [15]. The task of the Australian Sports Commis- cluding physical and mental health [1]. Regular physical sion (ASC) is also to get more people playing sport more activity (PA) can improve fitness and contribute to lower often, and with a specific focus on young Australians [16]. risk of chronic diseases such as coronary heart disease, In the Australian context there are government synergies stroke, some cancers, type 2 diabetes, osteoporosis and between sport and health, with sport being located within depression [1]. Despite the wide ranging benefits of be- health portfolios at both the national and Victorian state ing physically active we have a global inactivity epidemic levels. More specifically, the primary focus of the Victorian which contributes to an overweight and obesity health Health Promotion Foundation (VicHealth) is “promoting crisis [2, 3], amongst other chronic health conditions. good health and preventing chronic disease” [17] and their World-wide 31% of adults are physically inactive, and Action Agenda for Health Promotion 2013 to 2023 in- 80% of 13–15 year olds are not meeting the recom- cludes the priority of encouraging regular PA. As part of mended levels of PA or 60 min of moderate to vigorous their PA, sport and walking investment plan (2014–2018) intensity per day [3]. there is a whole-of-population approach to getting the in- People can be physically active in different PA domains active and somewhat active people to become more active or contexts, including home, work, transport and [10]. This approach is about shifting participation levels leisure-time [4]. The context of leisure-time PA has three along the continuum from inactive to somewhat active to aspects to it, which have been defined as the type, mode regularly active and very active [10]. Furthermore, their re- and setting [5]. “Type” refers to the specific activity (e.g. cent PA strategy (2018–2023) uses research evidence to football, athletics, swimming). Different modes of partici- focus on specific priority target groups and with reference pation include team sports (e.g. football, cricket and net- to particular action areas or key determinants associated ball), individual sports (e.g. tennis, athletics and triathlon), with those specific groups of the population [10]. These organised but non-competitive PA (e.g. cycling and approaches consider ways to incorporate PA into everyday running groups), and non-organised or informal PA (e.g. life through active living (active travel), active recreation going to the gym or a walk) [5]. Settings of participation (social, non-competitive PA during leisure time) and orga- include organisational settings, such as schools, clubs or nised sport (competitive sport) [10]. leisure centres, and neighbourhood settings such as home, Much research has been conducted on the demo- street or park [5]. graphic determinants of sedentary behaviour [18, 19] Within the PA guidelines and it is recognised that and PA [4, 20] to inform policies and strategies for get- there are different health gains through different types ting people physically active. There is some research of activities such as aerobic or endurance and specifically relating to the demographic determinants of muscle-strengthening or resistance training, [1]. There sport participation [21–24]. However, the definition of is also growing body literature that highlights that sport is quite variable internationally. For example, in there are different types and levels of health gain Australia sport is defined as ‘A human activity capable according to the domain or context of participation of achieving a result, requiring physical exertion and/or [6–9]. For example, participation in sport, can be asso- physical skill which, by its nature and organisation, is ciated with improved social and mental health above competitive and is generally accepted as being a sport’. and beyond improvements attributed to individual The ASC maintains the final authority for determining types of PA, because of the social nature of team and whether an activity meets the definition of a sport [25]. club-based participation [6, 7, 9]. In contrast, countries such as England have a broader From a policy perspective, there are a range of health definition which encompasses physical recreation gen- and PA policies aimed at getting more people more ac- erally [26]. Sport England’s strategy encompasses both tive and more often in order to promote healthier indi- traditional team sports and activities such as walking or viduals, communities and nations [10–12]. There are going to the gym [26]. clear and relatively consistent recommendations inter- It is important that we understand the differences not nationally, including in the UK, USA and Australia, for only between inactive and active people, but also between duration of engagement in moderate to vigorous inten- the active people within different participation contexts sity PA: at least 60 min per day for children and young (types, modes and settings), and at different levels of fre- people, and at least 30 min on at least 5 days per week quency, to align policies and strategies to engage specific (i.e. 2.5 h weekly) for adults [1, 13, 14]. market segments who have different participation pref- With regard to sport in particular, policies in countries erences and levels of access to participation opportun- including England and Australia are very consistent and ities [27, 28]. Accordingly, the aim of this study was to clear that the aim is to get more people active and keep investigate demographic correlates of the propensity to them active. “We are seeking a consistent increase in the be physically inactive or active within different PA Eime et al. BMC Public Health (2018) 18:692 Page 3 of 10 contexts, and at different levels of frequency of participa- status (SES) - quintile of the Socio-economic Indexes for tion. The demographic correlates included personal char- Areas (SEIFA) Index of Relative Socio-economic Advan- acteristics (sex, age, education, employment, marital tage and Disadvantage (IRSAD) for postcode of residence status, languages spoken, disability, dependent children) [32] (1 = most disadvantaged to 5 = most advantaged). and measures of community socioeconomic status and remoteness. Data analysis First, chi-squared tests of independence were used to in- Methods vestigate bivariate associations between the category of Participants and procedure PA undertaken and each demographic characteristic in Data from the Australian Exercise, Recreation and Sport turn. Second, because the three categories of PA were Survey (ERASS) were used for this analysis. Details of considered to be naturally ordered with respect to the ERASS methods have been previously described [29, 30]. associated ‘dose’ of PA, ordinal logistic regression was Briefly, ERASS collected information on the type, fre- used to investigate the association between the propensity quency and duration of leisure-time PA for Australians to participate in ‘higher-dose’ vs ‘lower-dose’ ordered aged 15 years and over. Telephone interviewers collected categories of PA and each demographic characteristic in data on PA for two time frames for each respondent, turn. Ordinal regression predicts the odds of being in with different characteristics recorded for the ‘previous higher-dose versus lower-dose categories, averaged across two weeks’ time frame and the ‘last 12 months’ period. all possible dichotomies derived from the ordered categor- For example, duration of activity was recorded for the ies, in this case “no PA vs any PA”,and “no PA or ‘previous two weeks’ period, while level of organisation non-sport PA” vs sport PA. This was implemented in a of activity (leisure centre, club, etc.) was recorded for the single multivariate model, with the effect of each demo- 12 month data. graphic characteristic on the odds being adjusted for the In addition to details of PA, ERASS collected demo- effects of all other demographic characteristics. graphic data from all respondents surveyed; not just those Similar analyses were conducted with PA participation that were physically active. Questions included age, sex ‘dose’ further subdivided into four ordinal categories: No and postcode along with characteristics such as cultural PA; Non-sport PA only; Sport 1–3 times per fortnight; background, education level and employment status. and Sport 4+ times per fortnight. This multi-wave cross-sectional national survey was conducted in four quarterly tranches each year from 2001 Results to 2010, and data were weighted by state, region (metro- In 2010, 21,603 people were surveyed regarding their politan or rest of the state), age group, gender and year to participation in leisure-time PA. Approximately 82% of reduce response bias in sample estimates. [31]. the sample (n = 17,769) stated they did some form of PA over the past 12 months, while 18% (n = 3834) stated Measures they did none. When those that had done PA in the past Self-reported participation in various PA activities in the 12 months were asked about PA in the past two weeks, two-week period prior to the survey was used to allocate 15,049 (85%) indicated they did some form of PA while respondents into categories for analysis. The first alloca- 2637 (15%) did not, and 83 did not respond. tion was based on frequency or duration of participation The age for PA participants in 2010 ranged from 15 to in PA over the two-week time-frame. Categories were: No 96 years, with a mean of 44.0 and a standard deviation PA; Non-sport PA only; and Sport. The second allocation of 18.5 years. incorporated frequency of sport participation as a repre- sentation of ‘dose’ of sport-based PA. The ‘Sport’ category Bivariate analyses above was divided into two further categories based on The breakdown of those who did some form of frequency of participation in any sport. The resulting leisure-time PA in the past two weeks across various ordinal ‘dose’ categories were: No PA; Non-sport PA only; demographic variables is shown in the first part of Sport 1–3 times per fortnight; and Sport 4+ times per Table 1. Bivariate chi squared analysis showed that fortnight. Respondents who reported a mixture of sport there were significant differences between the profiles and non-sport PA were included in the ‘Sport’ categories of PA participation (no PA, non-sport PA, sport) across for both allocations. all of the demographic variables except languages Potential demographic correlates of PA participation spoken at home. However, it should be borne in mind included sex, age, education, employment, marital status, that becauseof the verylargesamplesizeand conse- speaking a language other than English at home, having quent high statistical power, the tests of significance are a condition that restricts life, having children under 18 very sensitive to small differences in the profiles of par- living at home, and an areal measure of socio-economic ticipation. Because all of the cross-tabulations have Eime et al. BMC Public Health (2018) 18:692 Page 4 of 10 Table 1 Associations between demographic characteristics and type of PA participation in the past two weeks Cross-tabulation (Bivariate) Ordinal logistic regression (Multivariate) No PA Non-sport PA Sport PA Total p-value* OR 95% CI p-value** Predictor n % n % n % Sex 2720 15.3 7382 41.5 7667 43.1 17,769 < 0.001 < 0.001 Male 1359 15.2 2852 32.0 4699 52.7 8910 ref Female 1361 15.4 4530 51.1 2968 33.5 8859 0.53 0.48–0.58 Age Range 2696 15.4 7248 41.3 7608 43.3 17,552 < 0.001 < 0.001 15–29 years 766 16.9 1149 25.4 2609 57.7 4524 ref 30–49 years 1097 16.4 2816 42.2 2766 41.4 6678 0.70 0.59–0.83 50+ years 834 13.1 3283 51.7 2233 35.2 6349 0.62 0.54–0.72 Education 2720 15.3 7382 41.5 7667 43.1 17,769 < 0.001 0.001 < Year 12, still at school 856 17.1 1911 38.3 2227 44.6 4994 ref Highest level of secondary school 577 15.7 1503 40.9 1598 43.5 3678 0.98 0.85–1.12 Undergraduate diploma, Certificate or 589 15.7 1686 44.9 1484 39.5 3760 0.99 0.87–1.13 Trade qualification University degree or higher 698 13.1 2282 42.7 2358 44.2 5338 1.22 1.08–1.38 Employment 2703 15.3 7312 41.5 7614 43.2 17,629 < 0.001 0.008 Full time 1332 16.7 3022 37.9 3628 45.5 7981 ref Part time 576 14.4 1711 42.8 1711 42.8 3999 1.18 1.05–1.33 Other*** 796 14.1 2579 45.7 2274 40.3 5649 1.15 1.03–1.29 Marital status 2705 15.3 7343 41.5 7631 43.2 17,678 < 0.001 < 0.001 Not married 1009 13.9 2723 37.4 3546 48.7 7278 ref Married (includes defacto) 1696 16.3 4619 44.4 4085 39.3 10,400 0.79 0.72–0.87 Language spoken at home 2720 15.3 7382 41.5 7667 43.1 17,769 0.492 0.196 English 2407 15.2 6612 41.8 6802 43.0 15,820 ref Other than English 313 16.1 771 39.5 865 44.4 1949 0.90 0.77–1.05 Has condition that restricts life 2717 15.3 7377 41.6 7660 43.1 17,754 < 0.001 < 0.001 No 2283 14.8 6186 40.1 6956 45.1 15,425 ref Yes 434 18.7 1191 51.1 704 30.2 2329 0.64 0.57–0.72 Number of children aged under 18 at home 2716 15.3 7381 41.6 7664 43.2 17,761 0.002 0.031 None 1777 14.5 5055 41.2 5430 44.3 12,263 ref One 348 18.7 835 44.9 677 36.4 1860 0.80 0.67–0.96 Two 379 16.1 958 40.6 1021 43.3 2358 1.06 0.9–1.24 Three or more 212 16.5 533 41.6 536 41.8 1281 1.00 0.81–1.22 SEIFA IRSAD 2011 2717 15.3 7381 41.6 7665 43.2 17,764 0.005 0.574 quintile 1 (Most disadvantaged) 512 17.3 1205 40.8 1233 41.8 2949 ref quintile 2 565 16.3 1346 38.9 1552 44.8 3463 1.10 0.94–1.28 quintile 3 563 15.6 1555 43.0 1495 41.4 3613 0.98 0.85–1.13 quintile 4 559 15.4 1472 40.4 1610 44.2 3640 1.03 0.89–1.19 quintile 5 (Most advantaged) 518 12.7 1804 44.0 1775 43.3 4097 1.06 0.92–1.22 Notes: OR odds ratio, CI Confidence interval, ref Reference category; * Chi-squared analysis; ** Ordinal regression; ***Other = unemployed+not in labour force more than two categories in one or both dimensions, Males were much more likely to play sport than fe- the details of the differences in the response profiles males, with 52.7% of males playing sport compared to are complex. In the following paragraph we summarise 33.5% of females. Conversely, 51.1% of females were some key differences, focusing on the likelihood of more likely to report only non-sport PA, compared to playing sport. 32.0% of males. Those in the younger age range were the Eime et al. BMC Public Health (2018) 18:692 Page 5 of 10 most likely to play sport; 57.7% of 15–29 year olds 0.80, two children OR 1.06, three or more children OR played sport, while only 35.2% of those 50 and over 1.00, p = 0.031). reported playing sport. Those with an undergraduate So for example, after adjustment for other demographic diploma, certificate or trade qualification were least factors, the odds of females being in a higher dose cat- likely to play sport (39.5%), while in all other education egory of PA are significantly less than the odds of males categories, the rate of sport participation was around being in a higher dose category of PA (OR = 0.53, 95% CI 44%. Regarding employment, participants who were =0.48–0.58). Similarly, after adjustment for other demo- employed full-time were the most likely to play sport graphic factors, the odds of those aged 30–49 years being (45.5%), with the proportion diminishing with the level in a higher dose category of PA are significantly less than of employment. Those who were not married were more the odds of those aged 15–29 years being in a higher dose likely to play sport, with 48.7% playing sport compared category of PA (OR = 0.70, 95% CI = 0.59–0.83). Again, to 39.3% of those that were married. Those who have a after adjustment for other demographic factors, the odds condition that restricts life were less likely to play sport; of those with a university degree or higher qualification 45.1% of those with no restrictive condition played sport, being in a higher dose category of PA are significantly while only 30.2% of those with a condition played sport. greater than the odds of those still at school being in a Language spoken at home was not a significant correlate higher dose category of PA (OR = 1.22, 95% CI = 1.08– of the level of PA participation. The number of children 1.38). However, the odds of those whose highest educa- aged under 18 at home and SEIFA IRSAD quintile were tional level is completion of secondary school being in significant correlates, but in each case there was no clear a higher dose category of PA are not significantly differ- trend in the profiles of participation across the categor- ent than the odds for those still at school (OR = 0.98, ies of the predictor. 95% CI = 0.85–1.12). Table 2 shows the results of similar analyses, but with Ordinal regression analysis the third category of activity (sport participation) split The second part of Table 1 shows the results of mul- into two categories on the basis of frequency of sport tiple ordinal logistic regression models for predicting participation to produce four ordinal categories as the the likelihood (represented by the ‘odds’)ofaperson outcome variable. Of the four PA levels (no PA, being in a ‘higher dose’ category of PA engagement vs non-sport, sport 1–3 times per fortnight, 4+ times per being in any of the ‘lower dose’ categories, averaged fortnight) participants were generally most likely to par- across the PA ‘dose’ categories. The effect of each ticipate in non-sport PA (approximately 15, 42, 11 and demographic variableinturnonthese odds is repre- 32% respectively). sented by a set of ‘odds ratios’, with each odds ratio After controlling for other demographic variables representing thedifferencein theodds in theparticular participants were less likely to do PA at a higher dose demographic category relative to the odds in a chosen level if they were female (OR 0.55, p <0.001), older ‘reference category’ (the first category listed). The odds (30–49 OR 0.71, 50+ OR 0.65, p < 0.001), married (OR ratios are also adjusted for the effects of the incidental 0.80, p < 0.001) or having a restrictive health condition changes in all other demographic variables. (OR 0.65, p < 0.001). Those with a university degree or After controlling for other demographic variables, sex higher were more likely to participate in PA at a higher (p < 0.001), age (p < 0.001), education level (p = 0.001), dose level (OR 1.32, p < 0.001). Those having part-time employment status (p = 0.008), marital status (p < 0.001), work or not being in the labour force were shown to be having a condition that restricts life (p < 0.001) and more likely to participate in PA at a higher dose level (part having children living at home (p = 0.031) had signifi- time OR 1.27, not in labour force OR 1.20, p <0.001). cantly different participation profiles. As expected, being Results indicate that people with one child were less likely female (OR 0.53), older (30–49 OR 0.70, 50 plus OR to participate in higher dose levels of PA while having two 0.62), married (0.79) or having a disability (OR 0.64) or more were no different than having none in terms of made people less likely to participate in higher dose participation in higher dose levels of PA (one child OR levels of PA and sport than people in the respective 0.79, two children OR 1.02, three or more children OR reference category. Those having part-time work or not 1.00, p =0.022). being in the labour force were shown to be more likely to participate in higher dose levels of PA (part time OR Discussion 1.18, not in labour force OR 1.15). Results indicate that This study provides information on demographic corre- people with one child were less likely to participate in lates across the PA dosage spectrum from no-leisure-time higher dose levels of PA while having two or more chil- PA to sport. This is described by VicHealth as the range of dren was no different to having no children in terms of ways to incorporate PA in to everyday life to encourage participation in higher dose levels of PA (one child OR the inactive and somewhat active to become more active, Eime et al. BMC Public Health (2018) 18:692 Page 6 of 10 Table 2 Associations between demographic characteristics and type/frequency of PA participation in the past two weeks Cross-tabulation Ordinal logistic regression No PA Non-sport PA Sport 1–3 times per fortnight Sport 4+ times per fortnight Total p-value* OR 95% CI p-value** Predictor n % n % n % n % Sex 2720 15.3 7382 41.5 2025 11.4 5642 31.8 17,769 < 0.001 < 0.001 Male 1359 15.2 2852 32.0 1300 14.6 3399 38.1 8910 ref Female 1361 15.4 4530 51.1 725 8.2 2244 25.3 8859 0.55 0.50–0.60 Age Range 2696 15.4 7248 41.3 2013 11.5 5595 31.9 17,552 < 0.001 < 0.001 15–29 years 766 16.9 1149 25.4 678 15.0 1932 42.7 4524 ref 30–49 years 1097 16.4 2816 42.2 835 12.5 1931 28.9 6678 0.71 0.60–0.83 50+ years 834 13.1 3283 51.7 500 7.9 1732 27.3 6349 0.65 0.57–0.75 Education 2720 15.3 7382 41.5 2025 11.4 5642 31.8 17,769 < 0.001 < 0.001 < Year 12, still at school 856 17.1 1911 38.3 673 13.5 1554 31.1 4994 ref Highest level of secondary school 577 15.7 1503 40.9 432 11.7 1167 31.7 3678 1.02 0.89–1.16 Undergraduate diploma,Certificate/Trade qualification 589 15.7 1686 44.9 403 10.7 1081 28.8 3760 1.04 0.92–1.18 University degree or higher 698 13.1 2282 42.7 518 9.7 1841 34.5 5338 1.32 1.17–1.48 Employment 2703 15.3 7312 41.5 2012 11.4 5602 31.8 17,629 < 0.001 < 0.001 Full time 1332 16.7 3022 37.9 1120 14.0 2508 31.4 7981 ref Part time 576 14.4 1711 42.8 352 8.8 1359 34.0 3999 1.27 1.13–1.42 Other*** 796 14.1 2579 45.7 539 9.5 1735 30.7 5649 1.20 1.08–1.34 Marital status 2705 15.3 7343 41.5 2020 11.4 5610 31.7 17,678 < 0.001 < 0.001 Not married 1009 13.9 2723 37.4 912 12.5 2634 36.2 7278 ref Married (includes defacto) 1696 16.3 4619 44.4 1109 10.7 2976 28.6 10,400 0.80 0.73–0.88 Language spoken at home 2720 15.3 7382 41.5 2025 11.4 5642 31.8 17,769 0.695 0.119 English 2407 15.2 6612 41.8 1792 11.3 5010 31.7 15,820 ref Other than English 313 16.1 771 39.5 233 12.0 632 32.4 1949 0.89 0.76–1.03 Has condition that restricts life 2717 15.3 7377 41.6 2022 11.4 5638 31.8 17,754 < 0.001 < 0.001 No 2283 14.8 6186 40.1 1847 12.0 5108 33.1 15,425 ref Yes 434 18.7 1191 51.1 174 7.5 529 22.7 2329 0.65 0.58–0.74 Number of children aged under 18 at home 2716 15.3 7381 41.6 2025 11.4 5639 31.8 17,761 < 0.001 0.022 None 1777 14.5 5055 41.2 1342 10.9 4088 33.3 12,263 ref One 348 18.7 835 44.9 218 11.7 459 24.7 1860 0.79 0.67–0.93 Two 379 16.1 958 40.6 318 13.5 703 29.8 2358 1.02 0.88–1.19 Three or more 212 16.5 533 41.6 147 11.5 389 30.4 1281 1.00 0.82–1.21 Eime et al. BMC Public Health (2018) 18:692 Page 7 of 10 Table 2 Associations between demographic characteristics and type/frequency of PA participation in the past two weeks (Continued) Cross-tabulation Ordinal logistic regression No PA Non-sport PA Sport 1–3 times per fortnight Sport 4+ times per fortnight Total p-value* OR 95% CI p-value** Predictor n % n % n % n % SEIFA IRSAD 2011 2717 15.3 7381 41.6 2023 11.4 5642 31.8 17,764 0.001 0.509 quintile 1 (Most disadvantaged) 512 17.3 1205 40.8 379 12.8 854 29.0 2949 ref quintile 2 565 16.3 1346 38.9 447 12.9 1105 31.9 3463 1.10 0.95–1.27 quintile 3 563 15.6 1555 43.0 389 10.8 1106 30.6 3613 1.00 0.87–1.15 quintile 4 559 15.4 1472 40.4 411 11.3 1198 32.9 3640 1.05 0.92–1.20 quintile 5 (Most advantaged) 518 12.7 1804 44.0 397 9.7 1378 33.6 4097 1.09 0.95–1.25 OR odds ratio, CI Confidence interval, ref Reference category, * Chi-squared analysis; ** Ordinal regression; ***Other = unemployed+not in labour force Eime et al. BMC Public Health (2018) 18:692 Page 8 of 10 including through active living, active recreation and orga- conclude that these differences may be due to differences nised sport [10]. Furthermore, it includes an examination in the intensity of participation [34]. of frequency of participation, which is important from a Sex and age were the main factors relating to PA in a re- health perspective. cent Spanish study [36]. Males engaged in more vigorous More than 80% of survey respondents to ERASS had PA and light PA overall, whereas females performed more participated in some PA within the past 12 months, and moderate PA [36]. Similarly a study of demographic deter- within the past two weeks. However this does not mean minants of participation in Sport (and recreation) in Spain that they are active at ‘healthy’ or health-enhancing levels. and England reported that gender, age, occupation and A recent study using the same dataset explored the education level were significant factors in both countries health-enhancing levels of PA participation. Overall, 94% [21]. However the sports participation rate was higher in of the different types of PA were classified as health en- England 48% compared to Spain 37% and there were hancing, and 18% of these activities were club-based sport demographic differences. The gender difference in partici- [30]. Furthermore, most (78%) of the Health Enhancing pation in England was much lower than that in Spain Levels of PA sport participation was played regularly [30]. (11% against 16% respectively) [21], while the age effect The Australian rates of PA within the past two weeks is was more pronounced in England: education effects were higher than those in the European Union, which used a also more important in Spain [21]. broad sport definition including both sport and recreation. An Australian study utilising the same ERASS dataset as In this study participation ranged considerably across 11 the present study found that participation in sport and PA countries from participating at least once a week of 22% was related to SES, in that the rates of ‘any recreational in Portugal to 76% in Finland [21]. England reports par- PA in the past year’ and ‘regular PA’ both increased as SES ticipation rates of adult (16+) at 40% at least once a week increased (being areas of greater advantage), however that from 2005 to 2006 [21]. As these authors acknowledged, it participation in PA was only SES-prohibitive for only a is difficult to look at international comparisons when few types of PA [29]. As SES decreased (being areas of there are major differences in definitions and survey de- greater disadvantage), participation in many teams sports signs [21]. actually increased [29]. Other sports studies have investi- It is well acknowledged that population levels of fre- gated the relationship between SES and access to facilities, quent PA are low and that an improved understanding of with the hypothesis that socially disadvantaged communi- the characteristics of people who are inactive and some- ties may experience further contextual disadvantage with what active can assist development and implementation of less access to sports facilities [37]. A German study also strategies for widespread participation in PA and sports investigated the associations between facility provision [4, 33]. Furthermore sport policies must strive to make and disadvantage [37]. This study included free and sports available to everyone and counter inequality and fee-based facilities and reported that for children and ado- difference, and therefore sport programs need to be de- lescents a lower SES area was actually related to a higher signed more specifically for target groups [33]. availability of PA facilities [37]. This study shows that a number of demographic vari- The correlates investigated in this study are generally ables are correlated with a proxy indicator of “dose” of PA. non-modifiable, so we need to look beyond the correlate Specifically, being female, older, married, having a restrict- itself. Another study of the individual correlates of PA also ive condition, being employed full-time, having a lower report that age, sex, health status, self-efficacy and motiv- level of education, having a child under 18 at home, and ation are associated with being active [4]. It may be that living in a lower SES area are all associated with a lower females in general and those older, married and with lower likelihood of participating in higher dose contexts. For education have lower self-efficacy and motivation which education, those with a degree are more likely to be active may hinder their participation. A systematic review study and active at the higher dose levels. Many studies have in- of the determinants of PA maintenance reported that the vestigated the dose-response of participation in different difference between individuals who did and did not main- domains of PA and all-cause mortality [34, 35]. However tain participation in PA over time reported that main- many of these do not consider the actual domain of PA, tainers had stronger self-efficacy and intention compared and they do not investigate the demographical correlates, to relapsers [20]. That is, the beliefs about capabilities and with the exception of sometimes age and gender [35]. A motivation and goals were the strongest predictors of systematic review and meta-analysis of studies of the gen- participation [20]. More specifically related to sport, the eral population did investigate participation in different Sport Commitment Model is an evolving theory that domains of PA and reported that there was stronger asso- explains participation in PA the sport context [38, 39]. ciations between PA and all-cause mortality for women Satisfaction and enjoyment and personal investments are than for men, and for sport and leisure-time PA than for consistent predictors of commitment to persistent partici- occupational and transport related PA [34]. The authors pation in PA in the form of sport and exercise [38]. Eime et al. BMC Public Health (2018) 18:692 Page 9 of 10 An important limitation of this study is the fact that the Authors’ contributions RE contributed to the study design, interpretation of results, manuscript ERASS was limited to persons aged 15+ years of age, conceptualisation and preparation. MC and JH contributed to the study whereas in the case of many sports, children and adoles- design, data management, statistical analysis and interpretation, cents younger than 15 years of age constitute a large pro- manuscript conceptualisation and preparation. RN contributed to the study design and manuscript preparation. All authors have read and portion of participants. However, the Australian Sports approved the final manuscript. Commission’s newly developed national population track- ing survey, AusPlay, includes provision for each adult re- Ethics approval and consent to participate Ethics approval has been granted by the Federation University, Australia, spondent living with a child or children aged 0–14 to Human Research Ethics Committee. Project number: C13–007. answer questions about one randomly selected child. Con- sequently, future studies of PA participation will be able to Competing interests cover all ages across the lifespan. The authors declare that they have no competing interests. Publisher’sNote Conclusion Springer Nature remains neutral with regard to jurisdictional claims in This study has shown that a number of individually published maps and institutional affiliations. significant demographic correlates of participation in PA Author details are also correlated with the propensity to engage in more School of Health Sciences and Psychology, Federation University, Ballarat, organised and competitive PA contexts, and that also re- 2 Australia. Institute of Sport, Exercise and Active Living, Victoria University, late to participating more frequently. People who were fe- Melbourne, Australia. Victorian Health Promotion Foundation (VicHealth), Melbourne, Australia. male, older, married or had a disability were less likely to participate in sport. These demographic correlates, cap- Received: 18 December 2017 Accepted: 25 May 2018 tured in the ERASS survey and investigated in this study, are largely non-modifiable. We also need to consider how References to improve those that are modifiable, such as self-efficacy, 1. US Department of Health and Human Services. Physical activity guidelines competency and motivation to be physically active, which for Americans. Washington, DC: Office of Disease Prevention and. Health can be addressed by providing a participation environ- Promotion. 2008; 2. Smith GD. A fatter, healthier but more unequal world. Lancet. 2016; ment which is supportive, social, fun and that allows for 387(10026):1349–50. different ability and skill levels. In terms of commitment 3. Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U. Lancet to participate, there are differences between the require- physical activity series working G. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380(9838):247–57. ments of club-based sport and unorganised PA that need 4. Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJF, Martin BW. Correlates of to be considered. There are also often different motiv- physical activity: why are some people physically active and others not? ational factors relating to participation in club-based sport Lancet. 2012;380(9838):258–71. 5. Eime RM, Harvey JT, Sawyer NA, Craike MJ, Symons CM, Polman RC, Payne compared to individually-based unorganised PA. There- WR. Understanding the contexts of adolescent female participation in sport fore when designing PA opportunities to engage those and physical activity. Res Q Exerc Sport. 2013;84(2):157–66. who are inactive, particularly those that are organised by a 6. Eime R, Young J, Harvey J, Charity M, Payne W. A systematic review of the psychological and social benefits of participation in sport for adults: club or group, we need to develop the sporting opportun- informing development of a conceptual model of health through sport. Int ities at clubs from the traditional competitive only model J Behav Nutr Phy. 2013;10:135. of play. Instead, we need to ensure that appropriate strat- 7. Eime R, Young J, Harvey J, Charity M, Payne W. A systematic review of the psychological and social benefits of participation in sport for children and egies are developed, and tailored sport products offered, adolescents: informing development of a conceptual model of health to ensure greater opportunities for increased diversity of through sport. Int J Behav Nutr Phy. 2013;10:98. participation in sport. 8. Basterfield L, Reilly J, Pearce M, Partkinson K, Adamson A, Reilly J, Vella S. Longitudinal associations between sports participation, body composition and physical activity from childhood to adolescence. J Sci Med Sport. 2015; Abbreviations 18(2):178–82. ASC: Australian Sports Commission; ERASS: Exercise Recreation and Sport 9. Vella S, Cliff D, Magee C, Okley A. Associations between sports participation Survey; IRSAD: Index of Relative Socio-Economic Advantage and Disadvan- and psychological difficulties during childhood: a two-year follow up. J Sci tage; PA: physical activity; SEIFA: Socio-Economic Indexes for Areas; Med Sport. 2015;18:304–9. SES: Socio-Economic Status 10. VicHealth. Physical activity, sport and walking. VicHealth's Investment Plan (2014 to 2018). Melbourne: VicHealth; 2014. 11. Australian Sports Commission: Play. Sport. Australia. The Australian Sports Acknowledgements Commission's participation game plan. Canberra, Australia: Australian Sports We thank the Australian Sports Commission for providing access to the Commission. 2015;26. ERASS data. 12. Sport and Recreation Victoria: Active Victoria - A strategic framework for sport and recreation in Victoria 2017–2021. 2017. http://sport.vic.gov.au/ Availability of data and materials publications-and-resources/strategies/active-victoria-strategic-framework- Data are kept at Federation University of Australia and are subject to data sport-and-recreation. Accessed 27 Apr 2018. protection regulations. We are unable to publically deposit this data because 13. Physical activity guidelines for adults. 2011. https://www.gov.uk/ at the time this study was commenced, no informed consent or ethics government/uploads/system/uploads/attachment_data/file/213740/dh_ committee approval was obtained for this to occur. 128145.pdf. Accessed 2 Mar 2018. Eime et al. BMC Public Health (2018) 18:692 Page 10 of 10 14. Department of Health. Australia’s physical activity and sedentary Gudelines. Canberra: Australian Government; 2014. 15. Department of Culture, Media and Sport. Creating a sporting habit for life A new youth sport Strategy London: Sport England; 2012: 20. 16. Australian Sports Commission: Corporate plan 2016–20. Australian Sports Commission; 2016: 48. 17. We aim to create a Victoria where everyone can enjoy better health 2016. https://www.vichealth.vic.gov.au/about/what-we-do. Accessed 2 Mar 2018. 18. Chastin SFM, Buck C, Freiberger E, Murphy M, Brug J, Cardon G, O’Donoghue G, Pigeot I, Oppert J-M. Systematic literature review of determinants of sedentary behaviour in older adults: a DEDIPAC study. Int J Behav Nutr Phy. 2015;12(1) 19. O’Donoghue G, Perchoux C, Mensah K, Lakerveld J, van der Ploeg H, Bernaards C, Chastin SFM, Simon C, O’Gorman D, Nazare J-A. A systematic review of correlates of sedentary behaviour in adults aged 18–65 years: a socio-ecological approach. BMC Public Healt. 2016;16(1):163. 20. Amireault S, Godin G, Vézina-Im L-A. Determinants of physical activity maintenance: a systematic review and meta-analyses. Health Psychol Review. 2013;7(1):55–91. 21. Kokolakakis T, Lera-López F, Panagouleas T. Analysis of the determinants of sports participation in Spain and England. Appl Econ. 2012;44(21):2785–98. 22. Federico B, Falese L, Marandola D, Capelli G. Socioeconomic differences in sport and physical activity among Italian adults. J Sport Sci. 2012;31(4):451–8. 23. Downward P, Rasciute S. Exploring the covariates of sport participation for health: an analysis of males and females in England. J Sport Sci. 2015;33(1):67–76. 24. Eime RM, Casey MM, Harvey JT, Sawyer NA, Symons CM, Payne WR. Socioecological factors potentially associated with participation in physical activity and sport: a longitudinal study of adolescent girls. J Sci Med Sport. 2015;18(6):684–90. 25. ASC Recognition. Undated. https://www.ausport.gov.au/supporting/nso/ asc_recognition. Accessed 2 Mar 2018. 26. Sport England our Strategy 2017. https://www.sportengland.org/active- nation/our-strategy/. Accessed 2 Mar 2018. 27. Australian Sports Commission. Market segmentation for sport participation- adults. March. Canberra: Australian Sports Commission; 2013. 28. Australian Sports Commission. Market segmentation for sport participation: children. In: Canberra: Australian sports commission; 2013. 29. 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;15:434. 30. Eime R, Harvey J, Charity M, Casey M, van Uffelen J, Payne W. The contribution of sport participation to overall health enhancing physical activity levels in Australia: a population-based study. BMC Public Health. 2015;15:806. 31. Standing Committee on Recreation and Sport. Participation in exercise, recreation and sport. In. Canberra: Australian Sports Commission; 2010:186. 32. Australian Bureau of Statistics Socio-economic indexes for areas (SEIFA). Canberra: Australian bureau of Statistics; 2013. 33. Puig N. The sports participation: from research to sports policy. Physical culture and sport studies and research. 2016;70:5–17. 34. Samitz G, Egger M, Zwahlen M. Domains of physical activity and all-cause mortality: systematic review and dose-response meta-analysis of cohort studies. Int J Epidemiol. 2011;40:1382–400. 35. Hupin D, Roche F, Gremeaux V, Chatard J-C, Oriol M, Gaspoz J-M, Barthélémy J-C, Edouard P. Even a low-dose of moderate-to-vigorous physical activity reduces mortality by 22% in adults aged≥ 60 years: a systematic review and meta-analysis. Brit J Sport Med. 2015;49(19):1262–7. 36. Mielgo-Ayuso J, Aparicio-Ugarriza R, Castillo A, Ruiz E, Avila J, Aranceta- Batrina J, Gil A, Ortega R, Serra-Majem L, Varela-Moreiras G, et al. Physical activity patterns of the Spanish population are mostly determined by sex and age: findings in the ANIBES study. PLoS One. 2016;11(2):e0149969. 37. Schneider S, D'Agostino A, Weyers S, Diehl K, Gruber J. Neighborhood deprivation and physical activity facilities—no support for the deprivation amplification hypothesis. JPAH. 2015;12(7):990–7. 38. Williams L: Commitment to sport and exercise. Re-examining the literature for a practical and parsimonious model. J Prev Med Public Health 2013;46 Suppl 35–42. 39. Scanlan T, Carpenter P, Simons J, Schmidt G, Keeler B. The sport commitment model: measurement development for the youth-sport domain. J Sport Exercise Psy. 1993;15:16–38. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png BMC Public Health Springer Journals

Demographic characteristics and type/frequency of physical activity participation in a large sample of 21,603 Australian people

Free
10 pages

Loading next page...
 
/lp/springer_journal/demographic-characteristics-and-type-frequency-of-physical-activity-bjPlQ1EAzm
Publisher
Springer Journals
Copyright
Copyright © 2018 by The Author(s).
Subject
Medicine & Public Health; Public Health; Medicine/Public Health, general; Epidemiology; Environmental Health; Biostatistics; Vaccine
eISSN
1471-2458
D.O.I.
10.1186/s12889-018-5608-1
Publisher site
See Article on Publisher Site

Abstract

Background: Regular physical activity (PA) is imperative for good health and there are many different ways that people can be active. There are a range of health, PA and sport policies aiming to get more people active more often. Much research has been directed towards understanding the determinants of inactivity and PA. However, it is important to understand the differences not only between inactive and active people, but also between activity contexts (for example participation in sport compared to non-sport activities), in order to align policies and strategies to engage market segments who have different participation preferences and accessibility. The aim of this study was to investigate demographic correlates of the propensity to be physically inactive or active within different contexts, and at different levels of frequency of participation. Methods: Data from the Australian Exercise, Recreation and Sport Survey was used for this analysis. This included information on the type, frequency and duration of leisure-time PA for Australians aged 15 years and over. Reported PA participation in the two-week period prior to the survey was used to allocate respondents into three categories: no PA, non-sport PA only, and sport. Subsequently, sport participants were further categorised according to frequency of participation. Potential demographic correlates included sex, age, education, employment, marital status, language spoken, having a condition that restricts life, children, and socio-economic status. Results: The survey included 21,603 people. Bivariate chi-squared analysis showed that there were significant differences between the profiles of leisure-time PA participation across all demographic variables, except the variable languages spoken at home. Ordinal regression analysis showed that the same demographic variables were also correlated with the propensity to engage in more organised and competitive PA contexts, and to participate more frequently. Conclusions: People who were female, older, married or had a disability were less likely to participate in sport. Therefore when designing PA opportunities to engage those who are inactive, particularly those that are organised by a club or group, we need to ensure that appropriate strategies are developed, and tailored sport products offered, to ensure greater opportunities for increased diversity of participation in sport. Keywords: Sport, Leisure-time physical activity, Demographic correlates * Correspondence: r.eime@federation.edu.au School of Health Sciences and Psychology, Federation University, Ballarat, Australia Institute of Sport, Exercise and Active Living, Victoria University, Melbourne, Australia Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Eime et al. BMC Public Health (2018) 18:692 Page 2 of 10 Background proportion of people regularly playing sport”– Sport Being physically active is important for overall health in- England [15]. The task of the Australian Sports Commis- cluding physical and mental health [1]. Regular physical sion (ASC) is also to get more people playing sport more activity (PA) can improve fitness and contribute to lower often, and with a specific focus on young Australians [16]. risk of chronic diseases such as coronary heart disease, In the Australian context there are government synergies stroke, some cancers, type 2 diabetes, osteoporosis and between sport and health, with sport being located within depression [1]. Despite the wide ranging benefits of be- health portfolios at both the national and Victorian state ing physically active we have a global inactivity epidemic levels. More specifically, the primary focus of the Victorian which contributes to an overweight and obesity health Health Promotion Foundation (VicHealth) is “promoting crisis [2, 3], amongst other chronic health conditions. good health and preventing chronic disease” [17] and their World-wide 31% of adults are physically inactive, and Action Agenda for Health Promotion 2013 to 2023 in- 80% of 13–15 year olds are not meeting the recom- cludes the priority of encouraging regular PA. As part of mended levels of PA or 60 min of moderate to vigorous their PA, sport and walking investment plan (2014–2018) intensity per day [3]. there is a whole-of-population approach to getting the in- People can be physically active in different PA domains active and somewhat active people to become more active or contexts, including home, work, transport and [10]. This approach is about shifting participation levels leisure-time [4]. The context of leisure-time PA has three along the continuum from inactive to somewhat active to aspects to it, which have been defined as the type, mode regularly active and very active [10]. Furthermore, their re- and setting [5]. “Type” refers to the specific activity (e.g. cent PA strategy (2018–2023) uses research evidence to football, athletics, swimming). Different modes of partici- focus on specific priority target groups and with reference pation include team sports (e.g. football, cricket and net- to particular action areas or key determinants associated ball), individual sports (e.g. tennis, athletics and triathlon), with those specific groups of the population [10]. These organised but non-competitive PA (e.g. cycling and approaches consider ways to incorporate PA into everyday running groups), and non-organised or informal PA (e.g. life through active living (active travel), active recreation going to the gym or a walk) [5]. Settings of participation (social, non-competitive PA during leisure time) and orga- include organisational settings, such as schools, clubs or nised sport (competitive sport) [10]. leisure centres, and neighbourhood settings such as home, Much research has been conducted on the demo- street or park [5]. graphic determinants of sedentary behaviour [18, 19] Within the PA guidelines and it is recognised that and PA [4, 20] to inform policies and strategies for get- there are different health gains through different types ting people physically active. There is some research of activities such as aerobic or endurance and specifically relating to the demographic determinants of muscle-strengthening or resistance training, [1]. There sport participation [21–24]. However, the definition of is also growing body literature that highlights that sport is quite variable internationally. For example, in there are different types and levels of health gain Australia sport is defined as ‘A human activity capable according to the domain or context of participation of achieving a result, requiring physical exertion and/or [6–9]. For example, participation in sport, can be asso- physical skill which, by its nature and organisation, is ciated with improved social and mental health above competitive and is generally accepted as being a sport’. and beyond improvements attributed to individual The ASC maintains the final authority for determining types of PA, because of the social nature of team and whether an activity meets the definition of a sport [25]. club-based participation [6, 7, 9]. In contrast, countries such as England have a broader From a policy perspective, there are a range of health definition which encompasses physical recreation gen- and PA policies aimed at getting more people more ac- erally [26]. Sport England’s strategy encompasses both tive and more often in order to promote healthier indi- traditional team sports and activities such as walking or viduals, communities and nations [10–12]. There are going to the gym [26]. clear and relatively consistent recommendations inter- It is important that we understand the differences not nationally, including in the UK, USA and Australia, for only between inactive and active people, but also between duration of engagement in moderate to vigorous inten- the active people within different participation contexts sity PA: at least 60 min per day for children and young (types, modes and settings), and at different levels of fre- people, and at least 30 min on at least 5 days per week quency, to align policies and strategies to engage specific (i.e. 2.5 h weekly) for adults [1, 13, 14]. market segments who have different participation pref- With regard to sport in particular, policies in countries erences and levels of access to participation opportun- including England and Australia are very consistent and ities [27, 28]. Accordingly, the aim of this study was to clear that the aim is to get more people active and keep investigate demographic correlates of the propensity to them active. “We are seeking a consistent increase in the be physically inactive or active within different PA Eime et al. BMC Public Health (2018) 18:692 Page 3 of 10 contexts, and at different levels of frequency of participa- status (SES) - quintile of the Socio-economic Indexes for tion. The demographic correlates included personal char- Areas (SEIFA) Index of Relative Socio-economic Advan- acteristics (sex, age, education, employment, marital tage and Disadvantage (IRSAD) for postcode of residence status, languages spoken, disability, dependent children) [32] (1 = most disadvantaged to 5 = most advantaged). and measures of community socioeconomic status and remoteness. Data analysis First, chi-squared tests of independence were used to in- Methods vestigate bivariate associations between the category of Participants and procedure PA undertaken and each demographic characteristic in Data from the Australian Exercise, Recreation and Sport turn. Second, because the three categories of PA were Survey (ERASS) were used for this analysis. Details of considered to be naturally ordered with respect to the ERASS methods have been previously described [29, 30]. associated ‘dose’ of PA, ordinal logistic regression was Briefly, ERASS collected information on the type, fre- used to investigate the association between the propensity quency and duration of leisure-time PA for Australians to participate in ‘higher-dose’ vs ‘lower-dose’ ordered aged 15 years and over. Telephone interviewers collected categories of PA and each demographic characteristic in data on PA for two time frames for each respondent, turn. Ordinal regression predicts the odds of being in with different characteristics recorded for the ‘previous higher-dose versus lower-dose categories, averaged across two weeks’ time frame and the ‘last 12 months’ period. all possible dichotomies derived from the ordered categor- For example, duration of activity was recorded for the ies, in this case “no PA vs any PA”,and “no PA or ‘previous two weeks’ period, while level of organisation non-sport PA” vs sport PA. This was implemented in a of activity (leisure centre, club, etc.) was recorded for the single multivariate model, with the effect of each demo- 12 month data. graphic characteristic on the odds being adjusted for the In addition to details of PA, ERASS collected demo- effects of all other demographic characteristics. graphic data from all respondents surveyed; not just those Similar analyses were conducted with PA participation that were physically active. Questions included age, sex ‘dose’ further subdivided into four ordinal categories: No and postcode along with characteristics such as cultural PA; Non-sport PA only; Sport 1–3 times per fortnight; background, education level and employment status. and Sport 4+ times per fortnight. This multi-wave cross-sectional national survey was conducted in four quarterly tranches each year from 2001 Results to 2010, and data were weighted by state, region (metro- In 2010, 21,603 people were surveyed regarding their politan or rest of the state), age group, gender and year to participation in leisure-time PA. Approximately 82% of reduce response bias in sample estimates. [31]. the sample (n = 17,769) stated they did some form of PA over the past 12 months, while 18% (n = 3834) stated Measures they did none. When those that had done PA in the past Self-reported participation in various PA activities in the 12 months were asked about PA in the past two weeks, two-week period prior to the survey was used to allocate 15,049 (85%) indicated they did some form of PA while respondents into categories for analysis. The first alloca- 2637 (15%) did not, and 83 did not respond. tion was based on frequency or duration of participation The age for PA participants in 2010 ranged from 15 to in PA over the two-week time-frame. Categories were: No 96 years, with a mean of 44.0 and a standard deviation PA; Non-sport PA only; and Sport. The second allocation of 18.5 years. incorporated frequency of sport participation as a repre- sentation of ‘dose’ of sport-based PA. The ‘Sport’ category Bivariate analyses above was divided into two further categories based on The breakdown of those who did some form of frequency of participation in any sport. The resulting leisure-time PA in the past two weeks across various ordinal ‘dose’ categories were: No PA; Non-sport PA only; demographic variables is shown in the first part of Sport 1–3 times per fortnight; and Sport 4+ times per Table 1. Bivariate chi squared analysis showed that fortnight. Respondents who reported a mixture of sport there were significant differences between the profiles and non-sport PA were included in the ‘Sport’ categories of PA participation (no PA, non-sport PA, sport) across for both allocations. all of the demographic variables except languages Potential demographic correlates of PA participation spoken at home. However, it should be borne in mind included sex, age, education, employment, marital status, that becauseof the verylargesamplesizeand conse- speaking a language other than English at home, having quent high statistical power, the tests of significance are a condition that restricts life, having children under 18 very sensitive to small differences in the profiles of par- living at home, and an areal measure of socio-economic ticipation. Because all of the cross-tabulations have Eime et al. BMC Public Health (2018) 18:692 Page 4 of 10 Table 1 Associations between demographic characteristics and type of PA participation in the past two weeks Cross-tabulation (Bivariate) Ordinal logistic regression (Multivariate) No PA Non-sport PA Sport PA Total p-value* OR 95% CI p-value** Predictor n % n % n % Sex 2720 15.3 7382 41.5 7667 43.1 17,769 < 0.001 < 0.001 Male 1359 15.2 2852 32.0 4699 52.7 8910 ref Female 1361 15.4 4530 51.1 2968 33.5 8859 0.53 0.48–0.58 Age Range 2696 15.4 7248 41.3 7608 43.3 17,552 < 0.001 < 0.001 15–29 years 766 16.9 1149 25.4 2609 57.7 4524 ref 30–49 years 1097 16.4 2816 42.2 2766 41.4 6678 0.70 0.59–0.83 50+ years 834 13.1 3283 51.7 2233 35.2 6349 0.62 0.54–0.72 Education 2720 15.3 7382 41.5 7667 43.1 17,769 < 0.001 0.001 < Year 12, still at school 856 17.1 1911 38.3 2227 44.6 4994 ref Highest level of secondary school 577 15.7 1503 40.9 1598 43.5 3678 0.98 0.85–1.12 Undergraduate diploma, Certificate or 589 15.7 1686 44.9 1484 39.5 3760 0.99 0.87–1.13 Trade qualification University degree or higher 698 13.1 2282 42.7 2358 44.2 5338 1.22 1.08–1.38 Employment 2703 15.3 7312 41.5 7614 43.2 17,629 < 0.001 0.008 Full time 1332 16.7 3022 37.9 3628 45.5 7981 ref Part time 576 14.4 1711 42.8 1711 42.8 3999 1.18 1.05–1.33 Other*** 796 14.1 2579 45.7 2274 40.3 5649 1.15 1.03–1.29 Marital status 2705 15.3 7343 41.5 7631 43.2 17,678 < 0.001 < 0.001 Not married 1009 13.9 2723 37.4 3546 48.7 7278 ref Married (includes defacto) 1696 16.3 4619 44.4 4085 39.3 10,400 0.79 0.72–0.87 Language spoken at home 2720 15.3 7382 41.5 7667 43.1 17,769 0.492 0.196 English 2407 15.2 6612 41.8 6802 43.0 15,820 ref Other than English 313 16.1 771 39.5 865 44.4 1949 0.90 0.77–1.05 Has condition that restricts life 2717 15.3 7377 41.6 7660 43.1 17,754 < 0.001 < 0.001 No 2283 14.8 6186 40.1 6956 45.1 15,425 ref Yes 434 18.7 1191 51.1 704 30.2 2329 0.64 0.57–0.72 Number of children aged under 18 at home 2716 15.3 7381 41.6 7664 43.2 17,761 0.002 0.031 None 1777 14.5 5055 41.2 5430 44.3 12,263 ref One 348 18.7 835 44.9 677 36.4 1860 0.80 0.67–0.96 Two 379 16.1 958 40.6 1021 43.3 2358 1.06 0.9–1.24 Three or more 212 16.5 533 41.6 536 41.8 1281 1.00 0.81–1.22 SEIFA IRSAD 2011 2717 15.3 7381 41.6 7665 43.2 17,764 0.005 0.574 quintile 1 (Most disadvantaged) 512 17.3 1205 40.8 1233 41.8 2949 ref quintile 2 565 16.3 1346 38.9 1552 44.8 3463 1.10 0.94–1.28 quintile 3 563 15.6 1555 43.0 1495 41.4 3613 0.98 0.85–1.13 quintile 4 559 15.4 1472 40.4 1610 44.2 3640 1.03 0.89–1.19 quintile 5 (Most advantaged) 518 12.7 1804 44.0 1775 43.3 4097 1.06 0.92–1.22 Notes: OR odds ratio, CI Confidence interval, ref Reference category; * Chi-squared analysis; ** Ordinal regression; ***Other = unemployed+not in labour force more than two categories in one or both dimensions, Males were much more likely to play sport than fe- the details of the differences in the response profiles males, with 52.7% of males playing sport compared to are complex. In the following paragraph we summarise 33.5% of females. Conversely, 51.1% of females were some key differences, focusing on the likelihood of more likely to report only non-sport PA, compared to playing sport. 32.0% of males. Those in the younger age range were the Eime et al. BMC Public Health (2018) 18:692 Page 5 of 10 most likely to play sport; 57.7% of 15–29 year olds 0.80, two children OR 1.06, three or more children OR played sport, while only 35.2% of those 50 and over 1.00, p = 0.031). reported playing sport. Those with an undergraduate So for example, after adjustment for other demographic diploma, certificate or trade qualification were least factors, the odds of females being in a higher dose cat- likely to play sport (39.5%), while in all other education egory of PA are significantly less than the odds of males categories, the rate of sport participation was around being in a higher dose category of PA (OR = 0.53, 95% CI 44%. Regarding employment, participants who were =0.48–0.58). Similarly, after adjustment for other demo- employed full-time were the most likely to play sport graphic factors, the odds of those aged 30–49 years being (45.5%), with the proportion diminishing with the level in a higher dose category of PA are significantly less than of employment. Those who were not married were more the odds of those aged 15–29 years being in a higher dose likely to play sport, with 48.7% playing sport compared category of PA (OR = 0.70, 95% CI = 0.59–0.83). Again, to 39.3% of those that were married. Those who have a after adjustment for other demographic factors, the odds condition that restricts life were less likely to play sport; of those with a university degree or higher qualification 45.1% of those with no restrictive condition played sport, being in a higher dose category of PA are significantly while only 30.2% of those with a condition played sport. greater than the odds of those still at school being in a Language spoken at home was not a significant correlate higher dose category of PA (OR = 1.22, 95% CI = 1.08– of the level of PA participation. The number of children 1.38). However, the odds of those whose highest educa- aged under 18 at home and SEIFA IRSAD quintile were tional level is completion of secondary school being in significant correlates, but in each case there was no clear a higher dose category of PA are not significantly differ- trend in the profiles of participation across the categor- ent than the odds for those still at school (OR = 0.98, ies of the predictor. 95% CI = 0.85–1.12). Table 2 shows the results of similar analyses, but with Ordinal regression analysis the third category of activity (sport participation) split The second part of Table 1 shows the results of mul- into two categories on the basis of frequency of sport tiple ordinal logistic regression models for predicting participation to produce four ordinal categories as the the likelihood (represented by the ‘odds’)ofaperson outcome variable. Of the four PA levels (no PA, being in a ‘higher dose’ category of PA engagement vs non-sport, sport 1–3 times per fortnight, 4+ times per being in any of the ‘lower dose’ categories, averaged fortnight) participants were generally most likely to par- across the PA ‘dose’ categories. The effect of each ticipate in non-sport PA (approximately 15, 42, 11 and demographic variableinturnonthese odds is repre- 32% respectively). sented by a set of ‘odds ratios’, with each odds ratio After controlling for other demographic variables representing thedifferencein theodds in theparticular participants were less likely to do PA at a higher dose demographic category relative to the odds in a chosen level if they were female (OR 0.55, p <0.001), older ‘reference category’ (the first category listed). The odds (30–49 OR 0.71, 50+ OR 0.65, p < 0.001), married (OR ratios are also adjusted for the effects of the incidental 0.80, p < 0.001) or having a restrictive health condition changes in all other demographic variables. (OR 0.65, p < 0.001). Those with a university degree or After controlling for other demographic variables, sex higher were more likely to participate in PA at a higher (p < 0.001), age (p < 0.001), education level (p = 0.001), dose level (OR 1.32, p < 0.001). Those having part-time employment status (p = 0.008), marital status (p < 0.001), work or not being in the labour force were shown to be having a condition that restricts life (p < 0.001) and more likely to participate in PA at a higher dose level (part having children living at home (p = 0.031) had signifi- time OR 1.27, not in labour force OR 1.20, p <0.001). cantly different participation profiles. As expected, being Results indicate that people with one child were less likely female (OR 0.53), older (30–49 OR 0.70, 50 plus OR to participate in higher dose levels of PA while having two 0.62), married (0.79) or having a disability (OR 0.64) or more were no different than having none in terms of made people less likely to participate in higher dose participation in higher dose levels of PA (one child OR levels of PA and sport than people in the respective 0.79, two children OR 1.02, three or more children OR reference category. Those having part-time work or not 1.00, p =0.022). being in the labour force were shown to be more likely to participate in higher dose levels of PA (part time OR Discussion 1.18, not in labour force OR 1.15). Results indicate that This study provides information on demographic corre- people with one child were less likely to participate in lates across the PA dosage spectrum from no-leisure-time higher dose levels of PA while having two or more chil- PA to sport. This is described by VicHealth as the range of dren was no different to having no children in terms of ways to incorporate PA in to everyday life to encourage participation in higher dose levels of PA (one child OR the inactive and somewhat active to become more active, Eime et al. BMC Public Health (2018) 18:692 Page 6 of 10 Table 2 Associations between demographic characteristics and type/frequency of PA participation in the past two weeks Cross-tabulation Ordinal logistic regression No PA Non-sport PA Sport 1–3 times per fortnight Sport 4+ times per fortnight Total p-value* OR 95% CI p-value** Predictor n % n % n % n % Sex 2720 15.3 7382 41.5 2025 11.4 5642 31.8 17,769 < 0.001 < 0.001 Male 1359 15.2 2852 32.0 1300 14.6 3399 38.1 8910 ref Female 1361 15.4 4530 51.1 725 8.2 2244 25.3 8859 0.55 0.50–0.60 Age Range 2696 15.4 7248 41.3 2013 11.5 5595 31.9 17,552 < 0.001 < 0.001 15–29 years 766 16.9 1149 25.4 678 15.0 1932 42.7 4524 ref 30–49 years 1097 16.4 2816 42.2 835 12.5 1931 28.9 6678 0.71 0.60–0.83 50+ years 834 13.1 3283 51.7 500 7.9 1732 27.3 6349 0.65 0.57–0.75 Education 2720 15.3 7382 41.5 2025 11.4 5642 31.8 17,769 < 0.001 < 0.001 < Year 12, still at school 856 17.1 1911 38.3 673 13.5 1554 31.1 4994 ref Highest level of secondary school 577 15.7 1503 40.9 432 11.7 1167 31.7 3678 1.02 0.89–1.16 Undergraduate diploma,Certificate/Trade qualification 589 15.7 1686 44.9 403 10.7 1081 28.8 3760 1.04 0.92–1.18 University degree or higher 698 13.1 2282 42.7 518 9.7 1841 34.5 5338 1.32 1.17–1.48 Employment 2703 15.3 7312 41.5 2012 11.4 5602 31.8 17,629 < 0.001 < 0.001 Full time 1332 16.7 3022 37.9 1120 14.0 2508 31.4 7981 ref Part time 576 14.4 1711 42.8 352 8.8 1359 34.0 3999 1.27 1.13–1.42 Other*** 796 14.1 2579 45.7 539 9.5 1735 30.7 5649 1.20 1.08–1.34 Marital status 2705 15.3 7343 41.5 2020 11.4 5610 31.7 17,678 < 0.001 < 0.001 Not married 1009 13.9 2723 37.4 912 12.5 2634 36.2 7278 ref Married (includes defacto) 1696 16.3 4619 44.4 1109 10.7 2976 28.6 10,400 0.80 0.73–0.88 Language spoken at home 2720 15.3 7382 41.5 2025 11.4 5642 31.8 17,769 0.695 0.119 English 2407 15.2 6612 41.8 1792 11.3 5010 31.7 15,820 ref Other than English 313 16.1 771 39.5 233 12.0 632 32.4 1949 0.89 0.76–1.03 Has condition that restricts life 2717 15.3 7377 41.6 2022 11.4 5638 31.8 17,754 < 0.001 < 0.001 No 2283 14.8 6186 40.1 1847 12.0 5108 33.1 15,425 ref Yes 434 18.7 1191 51.1 174 7.5 529 22.7 2329 0.65 0.58–0.74 Number of children aged under 18 at home 2716 15.3 7381 41.6 2025 11.4 5639 31.8 17,761 < 0.001 0.022 None 1777 14.5 5055 41.2 1342 10.9 4088 33.3 12,263 ref One 348 18.7 835 44.9 218 11.7 459 24.7 1860 0.79 0.67–0.93 Two 379 16.1 958 40.6 318 13.5 703 29.8 2358 1.02 0.88–1.19 Three or more 212 16.5 533 41.6 147 11.5 389 30.4 1281 1.00 0.82–1.21 Eime et al. BMC Public Health (2018) 18:692 Page 7 of 10 Table 2 Associations between demographic characteristics and type/frequency of PA participation in the past two weeks (Continued) Cross-tabulation Ordinal logistic regression No PA Non-sport PA Sport 1–3 times per fortnight Sport 4+ times per fortnight Total p-value* OR 95% CI p-value** Predictor n % n % n % n % SEIFA IRSAD 2011 2717 15.3 7381 41.6 2023 11.4 5642 31.8 17,764 0.001 0.509 quintile 1 (Most disadvantaged) 512 17.3 1205 40.8 379 12.8 854 29.0 2949 ref quintile 2 565 16.3 1346 38.9 447 12.9 1105 31.9 3463 1.10 0.95–1.27 quintile 3 563 15.6 1555 43.0 389 10.8 1106 30.6 3613 1.00 0.87–1.15 quintile 4 559 15.4 1472 40.4 411 11.3 1198 32.9 3640 1.05 0.92–1.20 quintile 5 (Most advantaged) 518 12.7 1804 44.0 397 9.7 1378 33.6 4097 1.09 0.95–1.25 OR odds ratio, CI Confidence interval, ref Reference category, * Chi-squared analysis; ** Ordinal regression; ***Other = unemployed+not in labour force Eime et al. BMC Public Health (2018) 18:692 Page 8 of 10 including through active living, active recreation and orga- conclude that these differences may be due to differences nised sport [10]. Furthermore, it includes an examination in the intensity of participation [34]. of frequency of participation, which is important from a Sex and age were the main factors relating to PA in a re- health perspective. cent Spanish study [36]. Males engaged in more vigorous More than 80% of survey respondents to ERASS had PA and light PA overall, whereas females performed more participated in some PA within the past 12 months, and moderate PA [36]. Similarly a study of demographic deter- within the past two weeks. However this does not mean minants of participation in Sport (and recreation) in Spain that they are active at ‘healthy’ or health-enhancing levels. and England reported that gender, age, occupation and A recent study using the same dataset explored the education level were significant factors in both countries health-enhancing levels of PA participation. Overall, 94% [21]. However the sports participation rate was higher in of the different types of PA were classified as health en- England 48% compared to Spain 37% and there were hancing, and 18% of these activities were club-based sport demographic differences. The gender difference in partici- [30]. Furthermore, most (78%) of the Health Enhancing pation in England was much lower than that in Spain Levels of PA sport participation was played regularly [30]. (11% against 16% respectively) [21], while the age effect The Australian rates of PA within the past two weeks is was more pronounced in England: education effects were higher than those in the European Union, which used a also more important in Spain [21]. broad sport definition including both sport and recreation. An Australian study utilising the same ERASS dataset as In this study participation ranged considerably across 11 the present study found that participation in sport and PA countries from participating at least once a week of 22% was related to SES, in that the rates of ‘any recreational in Portugal to 76% in Finland [21]. England reports par- PA in the past year’ and ‘regular PA’ both increased as SES ticipation rates of adult (16+) at 40% at least once a week increased (being areas of greater advantage), however that from 2005 to 2006 [21]. As these authors acknowledged, it participation in PA was only SES-prohibitive for only a is difficult to look at international comparisons when few types of PA [29]. As SES decreased (being areas of there are major differences in definitions and survey de- greater disadvantage), participation in many teams sports signs [21]. actually increased [29]. Other sports studies have investi- It is well acknowledged that population levels of fre- gated the relationship between SES and access to facilities, quent PA are low and that an improved understanding of with the hypothesis that socially disadvantaged communi- the characteristics of people who are inactive and some- ties may experience further contextual disadvantage with what active can assist development and implementation of less access to sports facilities [37]. A German study also strategies for widespread participation in PA and sports investigated the associations between facility provision [4, 33]. Furthermore sport policies must strive to make and disadvantage [37]. This study included free and sports available to everyone and counter inequality and fee-based facilities and reported that for children and ado- difference, and therefore sport programs need to be de- lescents a lower SES area was actually related to a higher signed more specifically for target groups [33]. availability of PA facilities [37]. This study shows that a number of demographic vari- The correlates investigated in this study are generally ables are correlated with a proxy indicator of “dose” of PA. non-modifiable, so we need to look beyond the correlate Specifically, being female, older, married, having a restrict- itself. Another study of the individual correlates of PA also ive condition, being employed full-time, having a lower report that age, sex, health status, self-efficacy and motiv- level of education, having a child under 18 at home, and ation are associated with being active [4]. It may be that living in a lower SES area are all associated with a lower females in general and those older, married and with lower likelihood of participating in higher dose contexts. For education have lower self-efficacy and motivation which education, those with a degree are more likely to be active may hinder their participation. A systematic review study and active at the higher dose levels. Many studies have in- of the determinants of PA maintenance reported that the vestigated the dose-response of participation in different difference between individuals who did and did not main- domains of PA and all-cause mortality [34, 35]. However tain participation in PA over time reported that main- many of these do not consider the actual domain of PA, tainers had stronger self-efficacy and intention compared and they do not investigate the demographical correlates, to relapsers [20]. That is, the beliefs about capabilities and with the exception of sometimes age and gender [35]. A motivation and goals were the strongest predictors of systematic review and meta-analysis of studies of the gen- participation [20]. More specifically related to sport, the eral population did investigate participation in different Sport Commitment Model is an evolving theory that domains of PA and reported that there was stronger asso- explains participation in PA the sport context [38, 39]. ciations between PA and all-cause mortality for women Satisfaction and enjoyment and personal investments are than for men, and for sport and leisure-time PA than for consistent predictors of commitment to persistent partici- occupational and transport related PA [34]. The authors pation in PA in the form of sport and exercise [38]. Eime et al. BMC Public Health (2018) 18:692 Page 9 of 10 An important limitation of this study is the fact that the Authors’ contributions RE contributed to the study design, interpretation of results, manuscript ERASS was limited to persons aged 15+ years of age, conceptualisation and preparation. MC and JH contributed to the study whereas in the case of many sports, children and adoles- design, data management, statistical analysis and interpretation, cents younger than 15 years of age constitute a large pro- manuscript conceptualisation and preparation. RN contributed to the study design and manuscript preparation. All authors have read and portion of participants. However, the Australian Sports approved the final manuscript. Commission’s newly developed national population track- ing survey, AusPlay, includes provision for each adult re- Ethics approval and consent to participate Ethics approval has been granted by the Federation University, Australia, spondent living with a child or children aged 0–14 to Human Research Ethics Committee. Project number: C13–007. answer questions about one randomly selected child. Con- sequently, future studies of PA participation will be able to Competing interests cover all ages across the lifespan. The authors declare that they have no competing interests. Publisher’sNote Conclusion Springer Nature remains neutral with regard to jurisdictional claims in This study has shown that a number of individually published maps and institutional affiliations. significant demographic correlates of participation in PA Author details are also correlated with the propensity to engage in more School of Health Sciences and Psychology, Federation University, Ballarat, organised and competitive PA contexts, and that also re- 2 Australia. Institute of Sport, Exercise and Active Living, Victoria University, late to participating more frequently. People who were fe- Melbourne, Australia. Victorian Health Promotion Foundation (VicHealth), Melbourne, Australia. male, older, married or had a disability were less likely to participate in sport. These demographic correlates, cap- Received: 18 December 2017 Accepted: 25 May 2018 tured in the ERASS survey and investigated in this study, are largely non-modifiable. We also need to consider how References to improve those that are modifiable, such as self-efficacy, 1. US Department of Health and Human Services. Physical activity guidelines competency and motivation to be physically active, which for Americans. Washington, DC: Office of Disease Prevention and. Health can be addressed by providing a participation environ- Promotion. 2008; 2. Smith GD. A fatter, healthier but more unequal world. Lancet. 2016; ment which is supportive, social, fun and that allows for 387(10026):1349–50. different ability and skill levels. In terms of commitment 3. Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U. Lancet to participate, there are differences between the require- physical activity series working G. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380(9838):247–57. ments of club-based sport and unorganised PA that need 4. Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJF, Martin BW. Correlates of to be considered. There are also often different motiv- physical activity: why are some people physically active and others not? ational factors relating to participation in club-based sport Lancet. 2012;380(9838):258–71. 5. Eime RM, Harvey JT, Sawyer NA, Craike MJ, Symons CM, Polman RC, Payne compared to individually-based unorganised PA. There- WR. Understanding the contexts of adolescent female participation in sport fore when designing PA opportunities to engage those and physical activity. Res Q Exerc Sport. 2013;84(2):157–66. who are inactive, particularly those that are organised by a 6. Eime R, Young J, Harvey J, Charity M, Payne W. A systematic review of the psychological and social benefits of participation in sport for adults: club or group, we need to develop the sporting opportun- informing development of a conceptual model of health through sport. Int ities at clubs from the traditional competitive only model J Behav Nutr Phy. 2013;10:135. of play. Instead, we need to ensure that appropriate strat- 7. Eime R, Young J, Harvey J, Charity M, Payne W. A systematic review of the psychological and social benefits of participation in sport for children and egies are developed, and tailored sport products offered, adolescents: informing development of a conceptual model of health to ensure greater opportunities for increased diversity of through sport. Int J Behav Nutr Phy. 2013;10:98. participation in sport. 8. Basterfield L, Reilly J, Pearce M, Partkinson K, Adamson A, Reilly J, Vella S. Longitudinal associations between sports participation, body composition and physical activity from childhood to adolescence. J Sci Med Sport. 2015; Abbreviations 18(2):178–82. ASC: Australian Sports Commission; ERASS: Exercise Recreation and Sport 9. Vella S, Cliff D, Magee C, Okley A. Associations between sports participation Survey; IRSAD: Index of Relative Socio-Economic Advantage and Disadvan- and psychological difficulties during childhood: a two-year follow up. J Sci tage; PA: physical activity; SEIFA: Socio-Economic Indexes for Areas; Med Sport. 2015;18:304–9. SES: Socio-Economic Status 10. VicHealth. Physical activity, sport and walking. VicHealth's Investment Plan (2014 to 2018). Melbourne: VicHealth; 2014. 11. Australian Sports Commission: Play. Sport. Australia. The Australian Sports Acknowledgements Commission's participation game plan. Canberra, Australia: Australian Sports We thank the Australian Sports Commission for providing access to the Commission. 2015;26. ERASS data. 12. Sport and Recreation Victoria: Active Victoria - A strategic framework for sport and recreation in Victoria 2017–2021. 2017. http://sport.vic.gov.au/ Availability of data and materials publications-and-resources/strategies/active-victoria-strategic-framework- Data are kept at Federation University of Australia and are subject to data sport-and-recreation. Accessed 27 Apr 2018. protection regulations. We are unable to publically deposit this data because 13. Physical activity guidelines for adults. 2011. https://www.gov.uk/ at the time this study was commenced, no informed consent or ethics government/uploads/system/uploads/attachment_data/file/213740/dh_ committee approval was obtained for this to occur. 128145.pdf. Accessed 2 Mar 2018. Eime et al. BMC Public Health (2018) 18:692 Page 10 of 10 14. Department of Health. Australia’s physical activity and sedentary Gudelines. Canberra: Australian Government; 2014. 15. Department of Culture, Media and Sport. Creating a sporting habit for life A new youth sport Strategy London: Sport England; 2012: 20. 16. Australian Sports Commission: Corporate plan 2016–20. Australian Sports Commission; 2016: 48. 17. We aim to create a Victoria where everyone can enjoy better health 2016. https://www.vichealth.vic.gov.au/about/what-we-do. Accessed 2 Mar 2018. 18. Chastin SFM, Buck C, Freiberger E, Murphy M, Brug J, Cardon G, O’Donoghue G, Pigeot I, Oppert J-M. Systematic literature review of determinants of sedentary behaviour in older adults: a DEDIPAC study. Int J Behav Nutr Phy. 2015;12(1) 19. O’Donoghue G, Perchoux C, Mensah K, Lakerveld J, van der Ploeg H, Bernaards C, Chastin SFM, Simon C, O’Gorman D, Nazare J-A. A systematic review of correlates of sedentary behaviour in adults aged 18–65 years: a socio-ecological approach. BMC Public Healt. 2016;16(1):163. 20. Amireault S, Godin G, Vézina-Im L-A. Determinants of physical activity maintenance: a systematic review and meta-analyses. Health Psychol Review. 2013;7(1):55–91. 21. Kokolakakis T, Lera-López F, Panagouleas T. Analysis of the determinants of sports participation in Spain and England. Appl Econ. 2012;44(21):2785–98. 22. Federico B, Falese L, Marandola D, Capelli G. Socioeconomic differences in sport and physical activity among Italian adults. J Sport Sci. 2012;31(4):451–8. 23. Downward P, Rasciute S. Exploring the covariates of sport participation for health: an analysis of males and females in England. J Sport Sci. 2015;33(1):67–76. 24. Eime RM, Casey MM, Harvey JT, Sawyer NA, Symons CM, Payne WR. Socioecological factors potentially associated with participation in physical activity and sport: a longitudinal study of adolescent girls. J Sci Med Sport. 2015;18(6):684–90. 25. ASC Recognition. Undated. https://www.ausport.gov.au/supporting/nso/ asc_recognition. Accessed 2 Mar 2018. 26. Sport England our Strategy 2017. https://www.sportengland.org/active- nation/our-strategy/. Accessed 2 Mar 2018. 27. Australian Sports Commission. Market segmentation for sport participation- adults. March. Canberra: Australian Sports Commission; 2013. 28. Australian Sports Commission. Market segmentation for sport participation: children. In: Canberra: Australian sports commission; 2013. 29. 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;15:434. 30. Eime R, Harvey J, Charity M, Casey M, van Uffelen J, Payne W. The contribution of sport participation to overall health enhancing physical activity levels in Australia: a population-based study. BMC Public Health. 2015;15:806. 31. Standing Committee on Recreation and Sport. Participation in exercise, recreation and sport. In. Canberra: Australian Sports Commission; 2010:186. 32. Australian Bureau of Statistics Socio-economic indexes for areas (SEIFA). Canberra: Australian bureau of Statistics; 2013. 33. Puig N. The sports participation: from research to sports policy. Physical culture and sport studies and research. 2016;70:5–17. 34. Samitz G, Egger M, Zwahlen M. Domains of physical activity and all-cause mortality: systematic review and dose-response meta-analysis of cohort studies. Int J Epidemiol. 2011;40:1382–400. 35. Hupin D, Roche F, Gremeaux V, Chatard J-C, Oriol M, Gaspoz J-M, Barthélémy J-C, Edouard P. Even a low-dose of moderate-to-vigorous physical activity reduces mortality by 22% in adults aged≥ 60 years: a systematic review and meta-analysis. Brit J Sport Med. 2015;49(19):1262–7. 36. Mielgo-Ayuso J, Aparicio-Ugarriza R, Castillo A, Ruiz E, Avila J, Aranceta- Batrina J, Gil A, Ortega R, Serra-Majem L, Varela-Moreiras G, et al. Physical activity patterns of the Spanish population are mostly determined by sex and age: findings in the ANIBES study. PLoS One. 2016;11(2):e0149969. 37. Schneider S, D'Agostino A, Weyers S, Diehl K, Gruber J. Neighborhood deprivation and physical activity facilities—no support for the deprivation amplification hypothesis. JPAH. 2015;12(7):990–7. 38. Williams L: Commitment to sport and exercise. Re-examining the literature for a practical and parsimonious model. J Prev Med Public Health 2013;46 Suppl 35–42. 39. Scanlan T, Carpenter P, Simons J, Schmidt G, Keeler B. The sport commitment model: measurement development for the youth-sport domain. J Sport Exercise Psy. 1993;15:16–38.

Journal

BMC Public HealthSpringer Journals

Published: Jun 5, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off