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Interrelation of Sport Participation, Physical Act

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RESEARCH ARTICLE

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Interrelation of Sport Participation, Physical Activity, Social Capital and Mental Health in Disadvantaged Communities: A SEM-Analysis

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Mathieu Marlier1*, Delfien Van Dyck1, Greet Cardon1, Ilse De Bourdeaudhuij1,

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Kathy Babiak2, Annick Willem1

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1 Department of Movement and Sports Sciences, Ghent University, Ghent, Belgium, 2 School of Kinesiology, University of Michigan, Ann Arbor, Michigan, United States of America

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* Mathieu.Marlier@UGent.be

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Abstract

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OPEN ACCESS

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Citation: Marlier M, Van Dyck D, Cardon G, De Bourdeaudhuij I, Babiak K, Willem A (2015) Interrelation of Sport Participation, Physical Activity, Social Capital and Mental Health in Disadvantaged Communities: A SEM-Analysis. PLoS ONE 10(10): e0140196. doi:10.1371/journal.pone.0140196

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Editor: Koustuv Dalal, Örebro University, SWEDEN

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Received: June 2, 2015

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Accepted: September 21, 2015

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Published: October 9, 2015

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Copyright: © 2015 Marlier et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Data Availability Statement: All relevant data are within the paper and its Supporting Information files.

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Funding: Marlier is funded by the Flemish Policy Research Centre on Sports. No grant number was allocated to this project. The URL of its website is: http://www.steunpuntsport.be/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Competing Interests: The authors have declared that no competing interests exist.

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Background

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The Health through Sport conceptual model links sport participation with physical, social and psychological outcomes and stresses the need for more understanding between these outcomes. The present study aims to uncover how sport participation, physical activity, social capital and mental health are interrelated by examining these outcomes in one model.

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Methods

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A cross-sectional survey was conducted in nine disadvantaged communities in Antwerp (Belgium). Two hundred adults (aged 18–56) per community were randomly selected and visited at home to fill out a questionnaire on socio-demographics, sport participation, physi-cal activity, social capital and mental health. A sample of 414 adults participated in the study.

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Results

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Structural Equation Modeling analysis showed that sport participation (β = .095) and not total physical activity (β = .027) was associated with better mental health. No association was found between sport participation and community social capital (β = .009) or individual social capital (β = .045). Furthermore, only community social capital was linked with physi-cal activity (β = .114), individual social capital was not (β = -.013). In contrast, only individual social capital was directly associated with mental health (β = .152), community social capital was not (β = .070).

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Apr 9
Grace Wise (Apr 09 2019 4:15PM) : Sports are related with better mental health overall. [Edited]
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Conclusion

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This study emphasizes the importance of sport participation and individual social capital to improve mental health in disadvantaged communities. It further gives a unique insight into the functionalities of how sport participation, physical activity, social capital and mental

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PLOS ONE | DOI:10.1371/journal.pone.0140196October 9, 20151 / 18

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Interrelation of Physical Activity, Social Capital and Mental Health

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health are interrelated. Implications for policy are that cross-sector initiatives between the sport, social and health sector need to be supported as their outcomes are directly linked to one another.

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Grace Wise (Apr 09 2019 4:17PM) : Not only addressing that multiple factors are all interrelated, but that they need to be seen by policy makers to create an agenda to promote well-being.

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Introduction

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Increasing rates of depression and low mental health are one of the most pressing problems of our society [1]. Sport participation, physical activity and social capital have been at the center of academic and policy interest for their positive effects on mental health [24]. Recently a con-ceptual model of Health through Sport has been conceived linking sport participation with social and psychological outcomes. The model includes three major elements: (a) sport partici-pation, (b) determinants of sports participation, based on the socio-ecological model [5], (c) physical, social and psychological outcomes of sport participation [6]. Eime et al. articulate that more research should focus on investigating how sport, physical, social and psychological out-comes are associated [6]. The present study therefore aims to contribute to the existing litera-ture by examining how sport participation, physical activity, social capital and mental health are interrelated. Incorporating these variables in one model enables insight into how they affect each other and which one is more important in increasing mental health. Having a better understanding of the complex interrelation of these variables should allow clarification of which activities could result in a multiplication of effects of physical, social and psychological outcomes. This study takes place in disadvantaged communities as mental health of residents in these communities is general worse [7], sport participation rates lower [8], physical activity levels inferior [9] and social capital standards lower [10] compared to those living in more prosperous communities. Moreover, action and research in these communities have been advocated to achieve greater health equity and to understand how this can be accomplished [11]. In following paragraphs a theoretical description is given of how these variables interrelate.

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Grace Wise (Apr 09 2019 4:18PM) : A helpful description of the model used a sort of a guide throughout the study.
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Sport participation and physical activity protect against and reduce symptoms of depression and anxiety, delay cognitive decline, increase self-esteem and feelings of energy, and contribute to the overall quality of life [2]. Mechanisms underpinning these association are partially allo-cated to physiological effects of aerobic exercise [12] and partially in psychological processes;

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(a) people being able to master difficult exercise tasks induce feelings of competence stimulat-ing self-esteem (b) people with higher self-esteem and energy are believed to use more prob-lem-focused coping strategies [13]. However, it is not clear yet how much physical activity is needed to improve mental health; findings about which type, duration, level or intensity of physical activity improves mental health, remain contradictory [14]. A large study in Europe reported different relationships across different nations in the European Union between physi-cal activity and mental health [15]. In some studies, data suggested that there might exist a dose-response relationship, while in other studies this relationship could not be observed [15]. Of the different types of total physical activity (PA) (e.g., active transportation, leisure-time PA, household-related PA, work-related PA), leisure time PA has been found most related with higher levels of mental health [16]. In turn, of the different forms of leisure-time PA, sport participation has been consistently associated with better mental health in adults [4, 13, 17]. In this study sport participation is defined as physical activities that require a sufficient rate of exertion and that take place in an athletic context during leisure time [18]. It refers both to organized as well as non-organized and individual as team sport activities. The reason why

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PLOS ONE | DOI:10.1371/journal.pone.0140196October 9, 20152 / 18

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Interrelation of Physical Activity, Social Capital and Mental Health

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sport participation is more closely related to higher levels of mental health has been assigned to intrinsic motivation to participate in sport as enjoyment and challenge which are key to an enhanced psychological well-being [19].

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In recent years, research on the link between social capital and mental health has been stim-ulated by the growing recognition of social determinants of health [20]. These social determi-nants encompass among other poor social policies and bad access to education, health care, and leisure in the community [21], Interventions focusing on individual factors (knowledge, attitudes, skills) to improve health through behavioral change have resulted in limited effects, especially in disadvantaged populations [22]. In contrary, interventions focusing on social determinants of health have led to much better results [21]. Social capital has been acknowl-edged to reduce vulnerability to mental distress by impacting the social determinants, also in a disadvantaged context [23, 24]. Nevertheless, the concept of social capital is complex and much debated. Two main schools of thought are represented by Putnam and Bourdieu [25]. Putnam defines social capital as features of social organizations, such as trust, norms, and net-works (p. 67) [26]. Bourdieus definition is more focused on the resources that accrue to people as a result of participation in social networks [27]. As a consequence of these different views, it is essential for researchers to define how they conceptualize social capital. The literature dis-cusses several different types of social capital which have different associations with mental health [3]. The most common distinction is made between cognitive and structural social capi-tal [28]. The cognitive component refers to trust and reciprocity between individuals, whereas the structural component relates to the ties between friends, family and other social groups. Another debate in this field considers whether social capital is an individual or community-level construct or a combination of the two [29, 30]. Community social capital regards social capital as a collective attribute of the communities, which uniformly benefits all individuals liv-ing in that same community. Individual social capital in contrast, attributes the beneficial prop-erties of social capital to the individuals and their social relationships [31]. The present study examines both cognitive community social capital and cognitive individual social capital. Only the cognitive type is investigated, as this type is most researched and consistently been related to positive mental health [3]. In contrast, the association between structural social capital and mental health remains ambiguous [3, 32]. In this study therefore community social capital refers to the trust and reciprocity one has of people in their immediate community [27, 33], and individual social capital refers to the trust and reciprocity one has of people in general [26, 34]. Some studies found that only individual social capital had better protective effects against mental illness [35, 36], while other studies detected that both community and individual social capital were related to better mental health [37, 38]. Concerning the relation between both types of social capital it is believed that higher levels of community social capital will boost individual social capital as peoples identity and behavior is partly shaped by their interactions with their social environment [39].

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As previously mentioned, sport participation and mental health are closely related [2]. Sport participation has also been associated with social capital through participation in social and civic activities. Sports are considered a platform for people to meet, to enjoy being together and thus to create social networks [25, 30]. Furthermore, in many western countries, voluntary sport organizations make up the largest part of the voluntary sector [40]. According to most theorists, volunteering and active participation in civil society is a crucial element of social cap-ital [25]. This has made the belief in the socially integrative effects of participation in sport and in voluntary organizations so strong, that it appears as self-evident [41]. Several authors warn however that the relation between sport and social capital is ambiguous. Coakley argues that this inherent belief in the purity and goodness of sport has been abused to sponsor sport events which contribute little to the common good in any representative manner [42]. Collins reasons

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PLOS ONE | DOI:10.1371/journal.pone.0140196October 9, 20153 / 18

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Interrelation of Physical Activity, Social Capital and Mental Health

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that sport participation is exclusionary in itself as sport participation rates decline with lower socio-economic status [43]. Furthermore, studies have indicated that sport can also lead to inequalities and social exclusion as a result of the strong bonds that may exist within a sporting club or team that is homogeneous in its membership [44, 45]. The strong bonds may be benefi-cial to in-group members but negative for out-group members. It has therefore been argued that different types of sports and contexts where the sports take place are crucial for the social capital outcome [46, 47]. For instance, a study that focused on the relation of individual and organizational characteristics of sport clubs with social capital, found that members of team sports have stronger bonds with each other than in individual sports [41]. Another study in Japan showed that sport clubs open to people from all ages, from all levels providing various sports in the neighborhood scored higher on social capital compared to more traditional sport clubs, which were more focused on providing the technical practice of sport [46]. One context and type of sports activities which have been most explicitly linked with beneficial social and health outcomes are sport for development programs [47]. Many sport for development pro-grams have recently been implemented in disadvantaged communities to reach United Nations Millennium Development Goals [48]. These programs use sport to exert a positive influence on public health, the socialization of children, youths and adults, the social inclusion of the dis-advantaged, the economic development of regions and states, and on fostering intercultural exchange and conflict resolution (p. 311) [49].

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The relationship between social capital and total physical activity still remains largely to be discovered [50]. Most studies that have investigated this relationship argue that both individual and community social capital are related to higher levels of physical activity [51, 52]. Their arguments are generally based on three mechanisms: (a) decline in crime rate which promotes perceptions of safety and consequently increases physical activity; (b) higher norms of health-related behavior which encourages residents to be more physically active; (c) higher collective efficacy among residents which improves access to resources for physical activity [52, 53]. This direction of the association between social capital and physical activity is reverse when com-pared with the previous argument regarding the relationship between sport participation (= predictor) and social capital (= outcome). However, total physical activity is much broader than sport participation only, so probably other types of physical activity such as active trans-portation, housekeeping, gardening and work-related physical activity interact differently with social capital, which could justify this reverse association.

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In conclusion, the Health through Sport conceptual model has indicated that sport is related to psychosocial outcomes and that this should be further investigated [6]. The present study aims to fill this gap by examining how sport participation, total physical activity, social capital and mental health are interrelated. This study differentiates from other studies by researching these associations in one model, enabling comparison of strength of associations between the different outcomes and measurement of indirect effects. Fig 1 represents the model that will be tested in this paper, showing hypothesized associations based on the results currently available in the literature. As a side note, the association between sport participation and total physical activity should not be regarded as a hypothesis, but rather as a fact. This subdivision has been made as a result of the different associations between sport participation and total physical activity with social capital and mental health, described in the previous section.

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Methods

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Participants

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The study was conducted in Antwerp, Belgium (506,225 inhabitants, 204.26km², 2,478 inhabi-tants/km²), which is the city in Flanders (Belgium) where most disadvantaged communities are

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PLOS ONE | DOI:10.1371/journal.pone.0140196October 9, 20154 / 18

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Interrelation of Physical Activity, Social Capital and Mental Health

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Fig 1. Hypothesized model of relationships between sport participation, total physical activity, community social capital, individual social capital, and mental health.

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doi:10.1371/journal.pone.0140196.g001

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located [54]. Data were collected between January and March 2013. The study was approved by the Ethics Committee of the Ghent University Hospital and all respondents signed an informed consent. In the context of this study, disadvantaged communities were defined as communities which suffer acute social problems such as increasing population densities, low socio-economic status, high rates of chronic disease, high levels of migration and multicultural-ism and young people at risk of exclusion/disaffection from society (p. 264) [55]. Based on this definition four criteria were chosen for which data of the Public Service of Antwerp (2012) were available. For each of these categories the median was chosen as the cutoff point as this is the most common approach for dichotomizing continuous variables when no clear cutoff points are indicated by previous studies [56]. In total nine disadvantaged communities in Ant-werp were selected based on four criteria: (1) average income (median declaration of net tax-able income) lower than the citys median of 19845; (2) unemployment rate (proportion of unemployed people looking for a job between 18 and 64 years) higher than citys median of 8.9%; (3) ethnicity rate (percentage of parents born outside Belgium) higher than the citys median of 30.0%; (4) population density (number of inhabitants per square kilometer), higher than 8005 inh/km². Two communities only met three out of four requirements but were still regarded as the best options when compared to other communities. The socio-economic char-acteristics of the selected communities of the Public Service of Antwerp are provided in S1 Table.

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After the selection of the communities, potential respondents were selected. Prior power analysis indicated a total sample size of 400 adults living in the nine communities was needed. This implied that 45 respondents per community had to be included to have an equal sample distribution over the nine communities. Since recruiting respondents in disadvantaged com-munities presents itself as a complicated endeavor, a response rate of 25% was expected. The Public Service of Antwerp selected in each community a random sample of 200 addresses of adults (aged 1856 years; 1800 adults in total) who had already resided more than two years in the community. Up to three attempts were made on different days (during the week and week-ends) and different times (afternoon, evening) of the day to find these persons at home.

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PLOS ONE | DOI:10.1371/journal.pone.0140196October 9, 20155 / 18

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Interrelation of Physical Activity, Social Capital and Mental Health

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Participating respondents were asked to complete a written informed consent. Researchers conducting the visits were able to fluently speak English and French next to Dutch, to assist if participants showed difficulties responding in any particular language. If language remained a barrier, the help of a family member or friend was asked to assist during the interview. Respon-dents were asked to answer survey questions on socio-demographics, physical activity, sport participation, social capital and mental health. As incentive to participate, nine city bikes (one per community) could be won. When people opened the door and did not want to participate, this was considered a rejection. When people did not open the door it was coded as not at home. People who were not home after three attempts, were not visited anymore. In most communities three rounds of home-visits were needed to recruit 45 participants.

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Measures

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Socio-demographics. Participants were asked to give information about age, gender, edu-cation, ethnicity, tenancy, and civil status. Ethnicity was assessed by birth country of the respondents parents. These socio-demographic variables have been added to the model because evidence from both national and international literature suggests that sport participa-tion [57], community social capital [58], individual social capital [58], total physical activity [59], and mental health [60] are differently distributed according to several of these socio-demographic characteristics. Moreover, the interaction effects of the socio-demographics have been added to the model, as socio-ecological models have emphasized the importance of inter-action effects to explain health behaviors [5].

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Sport Participation. Sport participation was assessed using the sport index of the Flemish Physical Activity Questionnaire (FPAQ) [61]. The criterion validity of this sport index,

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assessed against accelerometers was good with a ρ of 0.52 [62]. Respondents were asked to select up to three organized and non-organized sports they practiced. For each of these sports, data on frequency (from once a year to more than once a day) and duration (from some hours per year to more than 20 hours per week) was collected. Fluctuation of sport participation dur-ing different periods of the year was taken into account by questioning the number of months one practiced the sport throughout the year. A sport participation index was computed by summing hours per week spent in total for the different sports.

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Total Physical activity. Self-reported total physical activity was collected using the short Dutch IPAQ (last seven days interview version). The interview version was chosen because adults tend to over report their physical activity levels with the self-administered version [63]. The short IPAQ has good reliability (intra-class range from 0.66 to 0.88). Criterion validity, assessed against accelerometers is fair-to-moderate with a median ρ = 0.29 [62]. Scoring was applied according to the guidelines of the short form IPAQ [64]. The metabolic equivalent (MET) values were derived for walking, moderate PA and vigorous PA and summed to create the total PA MET-minutes/week.

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Social capital. To capture the multidimensionality of social capital both community and individual social capital were assessed. Community-level social capital was evaluated using a 5-item scale based on the theoretical work of Bourdieu [27] and further developed by Carpiano [33] (see Fig 2). An example item was: People in this neighborhood are willing to help their neighbors?. Five-point answer categories were applied (1 = strongly disagree, 2 = disagree,

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3 = neither agree nor disagree, 4 = agree, 5 = strongly agree). The Cronbachs alpha of the instrument in this study was 0.82.

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Individual social capital was evaluated using a 3-item scale based on the social capital com-munity benchmark survey of Putnam [26]. Moreover, these items were core questions in the European Social Survey [34] (see Fig 2). An example item was: Generally speaking, would you

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PLOS ONE | DOI:10.1371/journal.pone.0140196October 9, 20156 / 18

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Interrelation of Physical Activity, Social Capital and Mental Health

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Fig 2. Structural equation analysis of sport participation, total physical activity, community social capital, individual social capital and mental health. Standardized parameter coefficients are shown.

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doi:10.1371/journal.pone.0140196.g002

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say that most people can be trusted, or that you cant be too careful in dealing with people. The questions had an 11-point answer scale ranging from 0 (e.g., you can never trust people) to 10 (e.g., you can always trust people). The Cronbachs alpha of the instrument in this study was 0.73.

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Mental Health. Mental health was measured using Goldbergs General Health Question-naire (GHQ-12) of [65]. The scale was a valid self-report instrument to assess a persons well-being in the community and non-psychiatric clinical settings [6668]. It consisted of 12 items (see Fig 2) with 4-point answer categories: not at all, same as usual, rather more than usual, or much more than usual. A sample item was: Have you lately felt like you couldnt overcome your difficulties?. The bimodal GHQ-scoring method (1-1-0-0) was applied, as recommended by Goldberg [65]. The resulting total scores ranged from 0 to 12, with higher scores indicating higher perceived health and mental wellbeing. The Cronbachs alpha of the instrument in this study was 0.83.

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Statistical analyses

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MANOVA-models were conducted to determine socio-demographic differences in the latent variables of sport participation, physical activity, community social capital, individual social capital and mental health. Before estimation of the parameters, assumption of normality and equal factor loadings of latent variables were tested. First, concerning normality, sport partici-pation and physical activity were positively skewed as is often the case with these variables. Therefore, skewness of physical activity and sport participation was improved with respectively a Log10 transformation and a Box-Cox transformation. Second, concerning equal factor load-ings, the items of the scales of community social capital, individual social capital and mental health did not have equal contributions. As a result, factor loadings were used instead of sum-marized scales, to have more accurate estimates of community social capital, individual social

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PLOS ONE | DOI:10.1371/journal.pone.0140196October 9, 20157 / 18

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Interrelation of Physical Activity, Social Capital and Mental Health

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capital and mental health. Factor loadings are depicted in Fig 2. Transformed variables were used to calculate F and p-values of MANOVA-analysis. To improve ease of interpretation how-ever mean scores of raw data will be reported. Parameter estimates of the socio-demographics are shown in Table 1. This table does not show interaction effects; however, these are men-tioned at the bottom of the table. These analyses were conducted with SPSS version 22.0 (IBM Corporation, Armonk, NY, USA).

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Structural equation modeling (SEM) was used to identify how sport participation, physical activity, community social capital, individual social capital and mental health (latent variables) were interrelated. SEM allows the simultaneous examination of a set of relationships between one or more independent variables and one or more dependent variables which makes it par-ticularly useful to measure interrelationships of the latent variables set out in the hypothesized model of this study (Fig 1) [69]. Socio-demographic variables that were found to be related to the latent variables in the previous MANOVA-analyses were incorporated as covariates into the final model that tests the interrelations between the sport participation, community social capital, individual social capital, total physical activity and mental health variables. Not signifi-cant relations were discarded from the model. SEM-models were analyzed using MPLUS 7 (Muthen & Muthen). The bias-corrected bootstrap method (5,000 iterations) was used for measuring indirect effects and mediation as advocated by Preacher and Hayes [70].

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To examine whether the hypothesized model fit the observed data, four indices were recom-mended as result of a lack of a standard format for reporting fit [71]: (a) the Root Mean Square Error of Approximation (RMSEA); a good fit is indicated when RMSEA is less than 0.05, (b)

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The Tacker- Lewis index (TLI) and comparative fit index (CFI); a good fit is indicated when TLI and CFI values are greater than 0.90, (c) the Standardized Root Mean Square Residual (SRMR); a good fit is indicated when SRMR is less than 0.05, (d) the normed Χ² chi square test, which is the chi-square fit index divided by degrees of freedom (this makes the test less depen-dent on sample size); a good fit is indicated when X²/df is less than 3 [69, 71]. If all indices dem-onstrate values close to or higher than the presented cutoff values, it is generally accepted that the model fits the observed data [72].

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Finally multiple group analyses in SEM were executed to verify if relations in our structural model (presented in Fig 1), differed for male or female respondents, native or ethnic residents, high or low educated people and for other groupings of socio-demographic variables. The model was therefore fitted separately for the different groups of the socio-demographic vari-ables. To assess whether differences between groups were significant, WALD-tests were com-pleted [69].

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Results

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From the 1800 randomly selected residents, 656 participants were found at home (36%). In total 242 declined to participate, resulting in a total of 414 valid questionnaires and a response rate of 63.1% (414 participants/656 participants found at home). The socio-demographic char-acteristics of the sample are presented in Table 1, a more detailed version of socio-demographic characteristics of the respondents per community can be retrieved in S2 Table. Although communities were selected on several criteria appropriate to disadvantaged communities, sig-nificant differences for the respondents of the communities were noted for ethnicity rate, edu-cation and tenancy. Meaning that some communities were more disadvantaged than others. In general, results showed that younger men participated more in sport; people with lower educa-tion had higher levels of physical activity; owners of a house and adults with higher education demonstrated higher levels of community and individual social capital; and married people and adults owning a house indicated having better mental health.

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PLOS ONE | DOI:10.1371/journal.pone.0140196October 9, 20158 / 18

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Interrelation of Physical Activity, Social Capital and Mental Health

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Table 1. Mean of the raw scores of sport participation, total physical activity, average scores of community social capital, individual social capital and sum score of mental health for the different socio-demographic variables.

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Groups

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n (%)

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Sport

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F-value

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Physical

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F-value

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Community

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F-

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Individual

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F-value

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Mental

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F-value

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Participation

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Activity

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Social Capital

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value

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social capital

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Health

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(Hours/week)

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(MET-

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(Range = 0–5)

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Range = 0-10

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Range = 0-12

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minutes/

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(0 = low 5 = high)

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(0 = low10 =

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(0 = low12 =

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week)

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high)

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high)

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Age group

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Young

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201

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2.217 (3.636)

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4799.677

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3.548 (.722)

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5.877 (1.735)

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9.924 (2.280)

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adults (18–

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(48.6)

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(3512.898)

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37)

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Older adults

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213

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1.561 (3.018)

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9.781**

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4373.773

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.645

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3.629 (.725)

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1.201

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5.766 (1.759)

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.008

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9.730 (2.505)

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.872

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(38–56)

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(51.4)

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(3426.767)

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Gender

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Men

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189

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2.293 (3.859)

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4742.325

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3.628 (.684)

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