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Sports Specialization in Young Athletes

Evidence-Based Recommendations

Neeru Jayanthi , MD

Courtney pinkham , bs, lara dugas , phd, brittany patrick , mph, cynthia labella , md.

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Neeru Jayanthi, MD, Associate Professor, Department of Family Medicine and Orthopaedic Surgery and Rehabilitation, Loyola Stritch School of Medicine, 2160 First Avenue, Bldg 54, Rm 260, Maywood, IL 60153 (e-mail: [email protected] )

Issue date 2013 May.

Sports specialization is intense training in 1 sport while excluding others. Sports specialization in early to middle childhood has become increasingly common. While most experts agree that some degree of sports specialization is necessary to achieve elite levels, there is some debate as to whether such intense practice time must begin during early childhood and to the exclusion of other sports to maximize potential for success. There is a concern that sports specialization before adolescence may be deleterious to a young athlete.

Evidence Acquisition:

PubMed and OVID were searched for English-language articles from 1990 to 2011 discussing sports specialization, expert athletes, or elite versus novice athletes, including original research articles, consensus opinions, and position statements.

For most sports, there is no evidence that intense training and specialization before puberty are necessary to achieve elite status. Risks of early sports specialization include higher rates of injury, increased psychological stress, and quitting sports at a young age. Sports specialization occurs along a continuum. Survey tools are being developed to identify where athletes fall along the spectrum of specialization.

Conclusion:

Some degree of sports specialization is necessary to develop elite-level skill development. However, for most sports, such intense training in a single sport to the exclusion of others should be delayed until late adolescence to optimize success while minimizing injury, psychological stress, and burnout.

Keywords: intense training, children, adolescents, overtraining, exercise

Youth sports participation has evolved from child-driven, recreational free play for enjoyment to adult-driven, highly structured, deliberate practice devoted to sports-specific skill development. 12 , 32 Emphasis is placed on developing and attaining sufficient skill levels to excel at many levels of athletics. 35 , 44 This evolution in youth sports may have developed as a result of society’s increasing regard for successful athletes, who enjoy significant recognition and financial rewards for their achievements. Consequently, many children and adolescents participating in sports now aspire to achieve elite levels. 44 , 46

The amount of training necessary to develop elite-level sports skills has long been debated. Ericsson et al defined the necessary components for expert skill acquisition in musicians, and these concepts have been extrapolated to sports. 16 To achieve expertise, musicians must practice 10 000 hours over 10 years. This intense practice is more likely to be successful if begun during the early years of development. Lesser practice and a delayed start resulted in less expertise. In contrast, others believe that fewer hours are needed to achieve elite-level skills and that intense specialized training is more effective during later stages of development. 42

A survey of elite young athletes (Training of Young Athletes Study) found that parents were the strongest influence on the initiation of a sport (gymnastics, tennis, swimming, soccer) while coaches were the strongest influence on their decision to perform intense training. 8 Similarly, a survey of 153 high school athletic directors suggested that coaches were the most powerful influence to specialize in a single sport. 23 This may create a disconnect: initially, a parent introduces the child to the sport; successes follow; then the coach encourages specialized training to achieve higher level success. The parent may acknowledge and encourage increased participation, not want to interfere with the child-coach relationship, and/or assume that this path is necessary for continued success. If the child has an injury as a result of training, the medical provider may treat the injury but may not have enough information to provide appropriate training recommendations for injury prevention.

Defining Sports Specialization

Sports specialization is defined as intense, year-round training in a single sport with the exclusion of other sports. 29 , 35 Variations on this general theme exist, with disagreement on what volume of training constitutes “intense” and whether year-round participation or exclusion of all other sports is essential for classifying an athlete as specialized. Some advocate that a minimum volume of training is required to meet the definition, 16 , 42 while others define specialization as simply limiting participation to a single sport on a year-round basis, regardless of training volume. 23 Ericsson et al proposed 3 stages in becoming a specialist or expert musician: (1) start at an early age, (2) specialize and increase participation, and (3) dedicate full-time commitment. 16 Côté et al further characterizes the intense training as the ultimate purpose of improving performance (“deliberate practice”) as opposed to enjoyment of the activity (“deliberate play”). 14 Soberlak and Côté developed a different approach when evaluating elite hockey players: sampling (ages 6-12 years), specializing (ages 13-15 years), and investment (ages 16+ years). 42 The distinction of sports specialization should really be focused on children who commit exclusively to a sport during the early-to-middle elementary school years, since later specialization is very common and almost standard in today’s society. 46

These definitions exclude athletes who perform a large volume of intense training in a single sport throughout the year but still compete in others concomitantly and those who train intensely in a single sport during parts of the year with variable year-round participation. As a result, sports specialization may be better defined along a continuum.

In an ongoing study, the rates of sports specialization in young athletes (8-18 years old) presenting to a pediatrician or family physician for sports physicals were compared with those presenting for an injury. 29 Based on questions about their sports participation, a sports specialization score was tabulated for each athlete. Preliminary data suggest that the most relevant question is whether they have quit other sports to focus on 1 sport. This factor accounted for 38% of the variance in the sport specialization score. 29 The second-most relevant question (32% of the variance) was whether the child had spent more than 3 quarters of their training time in 1 sport. Year-round and/or out-of-state training and competition were also relevant in determining level of specialization.

Trends in Sports Specialization

In the United States, participation in organized sport has increased from approximately 9% of children 6 years and younger in 1997 to 12% in 2008. 35 A majority (77.7%) of high school athletic directors reported an increasing trend in sports specialization. 23 Further evidence for early sports specialization is the growing number of travel leagues at 7 or 8 years of age 37 and an increase in young Olympic athletes. 46

Rates of sports specialization appear to increase with age. A study of 519 US Tennis Association junior tennis players found that 70% began specializing at an average age of 10.4 years old. 28 Specialization rate gradually increased after 14 years, with 95% of players by age 18 years. However, enjoyment and satisfaction ratings decreased in players older than 14 years old ( P < 0.01).

The reality is that few athletes achieve the elite or professional level. 35 Less than 1% of young athletes 6 to 17 years of age achieve elite status in basketball, soccer, baseball, softball, or football. 35 The data are similar for Germany 21 and Australia. 39

Does Early Specialization Promote Success in Sports?

There is general agreement that the number of hours spent in deliberate practice and training positively correlates with level of achievement in both individual and team sports; whether this intense practice must begin during early childhood and to the exclusion of other sports is a matter of debate. There are relatively few data to validate these theories. Professional medical organizations have published position statements on sports specialization and intense training in young people but have limited data upon which to base their recommendations and thus rely on expert opinion. 2 , 3 , 17 , 26 , 36

Early vs Late Intense Training

The best musicians spent over 10 000 hours practicing alone, while their less successful peers had accumulated 7000 hours or less, coinciding with critical periods of biological and cognitive development. Musicians began training around 5 years of age; those who began after age 5 years were unable to catch up.

Research in athletes has not consistently demonstrated that early intense training is essential for attaining an elite level in all sports ( Table 1 ). 5 , 6 , 13 , 21 , 22 , 24 , 25 , 30 , 31 , 38 , 42 , 45 Data from these studies are limited by a subset of sports, small samples sizes, and retrospective design; few included athletes who began intense training before 12 years. Two studies demonstrated that accomplished elite athletes were more likely to initiate intense training in early and middle childhood; both were women’s rhythmic gymnastics. In gymnastics, peak performance occurs before full maturation, requiring intense training before puberty. 25 , 30

Evidence for and against early sports specialization to achieve elite status

Plus sign (+) indicates “evidence for.”

Begin intense training.

Specialize in sport.

Diversify early, specialize in sport.

Canoeing/kayak, cycling, orienteering, rowing, sailing, skiing, swimming, track and field, triathalon, weight lifting.

In contrast, elite athletes in other sports were more likely to initiate intense training later in adolescence. World-class athletes were more likely to start competing at a later age, competed in other sports, and were typically selected for a sport federation program at an older age than those at the national level. 44 A recent survey of 148 elite and 95 near-elite Danish athletes (mean age, 24.5 years; track and field, weightlifting, cycling, rowing, swimming, skiing) found that the elite group began intense training at a later age and spent fewer hours practicing its main sport up to the age of 15 years compared to the near-elite group.38 By 18 years of age, the 2 groups had accumulated a similar number of practice hours, but by 21 years, elites had accumulated more practice hours.38 Involvement in other sports was not different between the groups and did not predict success. These sports require a high physical and aerobic capacity and lower technical or tactical requirement relative to ball and performance sports (gymnastics 11 and figure skating 43 ). While some physiologic adaptations to aerobic training occur in childhood, they are much less pronounced than adaptations in adolescence.

Early vs Late Specialization

For most sports, early diversification is more likely to lead to success ( Table 1 ). 5 , 6 , 13 , 21 , 22 , 24 , 31 , 38 , 42 , 45 A survey of 376 female Division 1 intercollegiate athletes found that the majority had their first organized sports experiences in other sports. 35 Only 17% had previously participated exclusively in their current sport; the majority simultaneously participated in individual sports (swimming, track and field, diving, tennis, and golf). 35

Early diversification provides the young athlete with valuable physical, cognitive, and psychosocial environments and promotes motivation. 1 , 2 , 36 , 38 , 46

Among high-level athletes of basketball, netball, and field hockey, the greater the number of activities that the athletes experienced and practiced in their developing years (ages 0-12 years), the less sports-specific practice was necessary to acquire expertise in their sport. 4 , 5 This is transfer of pattern recall skills from one sport to another, most pronounced during the early stages of involvement. 1 Early diversification followed by specialization may lead to more enjoyment, fewer injuries, and longer participation, contributing to the chances of success. 6 , 20 , 45

Other Factors Promoting Success in Sports

Early participation differences between elite youth soccer players who progressed to professional status at age 16 years and those who did not revealed that those who progressed had accumulated more hours per year in unstructured soccer activities between the ages of 6 and 12 years. There was no difference in soccer practice, soccer competition, or other sports in that time frame. 19 This suggests that elites sought more unstructured soccer during free time. This is supported by data that show that enjoyment of the sport and intrinsic motivation predict attainment. 20 , 25 , 31 Successful elite tennis players often have good long-term relationships with the same coach, access to tennis courts, and less overall demands for success compared with age-matched controls. 13

Risks of Single-Sport Intense Training

The risks of single-sport intense training include adverse psychological stress and premature withdrawal from competitive sport. Current data suggest that intense training and specialization may be independent risk factors. 28 , 29

The risks of intense training in elite young athletes in the United Kingdom was relatively low (rates of injury < 1/1000 hours of training) with few serious consequences. 7 , 34 Training volumes were often < 16 hours per week; lower than for other intensely trained athletes. 29 , 41 A 10-year follow-up suggests that injury incidence is significantly higher for athletes competing at an international level (87.5%) and a regional/country level (64.0%) compared with those competing at a national level (16.7%) or recreational level (47.1%). 33

Higher training volumes may increase risk for injury in a variety of sports. 41 In 2721 high school athletes, increased exposure was the most important risk factor for injury. 41 There was a linear relationship between exposure and risk of injury (odds ratio, 8.28), showing significantly elevated risk once training volume exceeded 16 hours per week ( Figure 1 ). Cumulative match (or competition) exposure also carries a significant risk: medical withdrawals increased in national tennis players after playing > 5 matches per year in supernational tournaments. 27 Players who specialized only in tennis were 1.5 times more likely to report an injury. 28 A 10-year prospective analysis of 481 youth baseball pitchers (9-14 years old) found that those who pitched more than 100 innings per year were 3.5 times more likely to be injured. 18 Others have found a significantly increased risk (odds ratio, 5.05) for shoulder or elbow surgery if pitching more than 8 months per year. 40

Relationship of injury to exposure hours in high school athletes. 41

The risk of injury from intense training and specialization may be affected by age, competitive level, growth rate, and pubertal maturation stage. Higher rates of injury were found in athletes older than 13 years of age and those at higher competitive levels. 15 Peripubertal gymnasts are more likely injured during periods of rapid growth (Tanner stages 2 and 3). 11 Fracture risk is also higher during peak height velocity. 9 , 29

Psychological Stress and Dropping Out of Sports

Early sports specialization may contribute to burnout and dropping out of sports ( Table 1 ). 5 , 6 , 13 , 22 , 24 , 25 , 30 , 38 , 42 , 45 Swimmers who specialized early spent less time on the national team and retired earlier than athletes who specialized later. 6 Minor league ice hockey players (boys) that dropped out of the sport started off-ice training earlier and spent more time in off-ice training than those who continued to compete. 45

In a retrospective 10-year review, 1 out of 5 of the most competitive elite athletes reported injury as the reason for quitting one’s sport. 10 Rhythmic gymnasts, those who specialized earlier and spent more hours training from age 4 to 16 years, rated their health lower and experienced less fun. 30 Junior tennis players who burned out early had less input in their training, higher perceived parental criticism and expectations, and lower levels of extrinsic motivation. 20 Elite Russian swimmers who dropped out reported that the main reasons for leaving the sport were psychological fatigue, general health, and difficult loads. 6

Some degree of sports specialization is necessary to attain elite-level skill. 2 , 3 , 17 , 26 , 36 However, for most sports, intense training in a single sport to the exclusion of others should be delayed until late adolescence to optimize success while minimizing risk for injury and psychological stress. 5 , 6 , 13 , 22 , 24 , 25 , 30 , 38 , 42 , 45

The following authors declared potential conflicts of interest: Neeru Jayanthi, MD, received a grant from the American Medical Society for Sports Medicine, and received honoraria and travel/expenses from United States Tennis Assocation (Player Development) for lectures and expenses and travel/expenses for serving on the American Medical Society for Sports Medicine Board of Directors; Brittany Patrick received a grant from the American Medical Society for Sports Medicine; Cynthia LaBella, MD, received a grant from the American Medical Society for Sports Medicine, and received honoraria for lectures given at AAP NCE, received royalties for published work, and received AAP COSMF executive committee travel expenses.

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Sports Participation and Juvenile Delinquency: A Meta-Analytic Review

Anouk spruit, eveline van vugt, claudia van der put, trudy van der stouwe, geert-jan stams.

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Received 2015 Sep 15; Accepted 2015 Nov 12; Issue date 2016.

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.

Participation in sports activities is very popular among adolescents, and is frequently encouraged among youth. Many psychosocial health benefits in youth are attributed to sports participation, but to what extent this positive influence holds for juvenile delinquency is still not clear on both the theoretical and empirical level. There is much controversy on whether sports participation should be perceived as a protective or a risk factor for the development of juvenile delinquency. A multilevel meta-analysis of 51 published and unpublished studies, with 48 independent samples containing 431 effect sizes and N  = 132,366 adolescents, was conducted to examine the relationship between sports participation and juvenile delinquency and possible moderating factors of this association. The results showed that there is no overall significant association between sports participation and juvenile delinquency, indicating that adolescent athletes are neither more nor less delinquent than non-athletes. Some study, sample and sports characteristics significantly moderated the relationship between sports participation and juvenile delinquency. However, this moderating influence was modest. Implications for theory and practice concerning the use of sports to prevent juvenile delinquency are discussed.

Keywords: Sports participation, Juvenile delinquency, Multilevel meta-analysis, Review

Introduction

A large number of adolescents is participating in sports activities. The 2011–2012 National Survey of Children’s Health showed that 63 % of the 12- to 17-year olds participated in sports lessons or a sports team. Generally, sports participation is perceived as a positive leisure activity that is associated with positive (psychosocial) health outcomes in adolescents (Eime et al. 2013 ; Janssen and LeBlanc 2010 ). However, the public opinion about adolescent athletes’ behavior is ambiguous. On the one hand it is believed that sports have a positive influence on the development of youth, and therefore, youth who participate in sports activities are expected to have a lower risk of engaging in delinquent behavior than youth who do not participate in sports activities (Faulkner et al. 2007 ; Miller et al. 2007 ; Shields and Bredemeier 1995 ). This assumption has led local governments and institutions all over the world to offer youth sports activities and interventions to prevent juvenile delinquency (Cameron and MacDougall 2000 ; Hartmann 2003 ; Kelly 2013 ; Miller et al. 2007 ; Nichols 2007 ; Sandford et al. 2006 ). On the other hand, due to negative reports in the media about athletes’ drug use and anti-social behavior, sports participation has often been linked to (juvenile) delinquency (Benedict and Klein 1997 ; Hughes and Shank 2005 ; Kwan et al. 2014 ; Yesalis and Bahrke 2000 ).

This division in views has led some researchers to test the assumptions on the association between sports participation and juvenile delinquency in order to understand if, and how sports participation is contributing to the occurrence of juvenile delinquency (Miller et al. 2007 ). So far, empirical evidence is inconclusive (Coakley 2002 ; Farb and Matjasko 2012 ; Gardner et al. 2009 ; Nichols 2007 ), and to date, there is no systematic review on the association between sports participation and juvenile delinquency available. It remains unclear whether sports participation is either positively or negatively associated with delinquent behavior among youth or whether no associations exist at all. Therefore, the aim of the current meta-analysis is to examine the relationship between sports participation and juvenile delinquency.

Theoretical Framework

Sports participation and delinquency are important developmental themes in adolescence. During adolescence, youth become more autonomous from their parents and the influence of the home environment shifts towards the afterschool, peer, and leisure setting (Fredricks and Eccles 2008 ). At the same time, the development and incidence of delinquent behaviors peaks (Moffitt 1993 ). Studying the relationship between sports participation and juvenile delinquency is therefore particularly relevant during adolescence.

Over the years, scientists have developed multiple theories about the relationship between sports participation and delinquency during adolescent years. Some of these theories support the idea that sports participation is associated with less juvenile delinquency. For example, Hirschi’s ( 1969 ) social bonds theory claims that individuals with stronger bonds to society are less likely to engage in delinquency, as delinquency may put these valuable bonds at risk. Four elements in Hirschi’s ( 1969 ) theory are central: attachment, commitment, belief, and involvement. Some (Agnew and Petersen 1989 ; Hass 2001 ) argue that sports participation has a positive influence on all four elements. Sports are supposed to enhance the attachment to significant others as youth become members of a team, generally supervised by a coach who is closely related to all members. When youth are committed to conventional activities, such as sports, they may refrain from deviant acts as this may jeopardize their opportunity to participate in sports. Beliefs in society’s values may be strengthened by sports participation, as similar rules, norms, and values are being practiced in the sports context. Finally, involvement in sports is thought to protect from juvenile delinquency because athletes are simply too occupied to engage in delinquency (Hirschi 1969 ). Similar arguments can be found in the boredom theory (Schafer 1969 ) and the routine activities theory (Cohen and Felson 1979 ). The boredom theory states that juvenile delinquency may originate from boredom, and because athletes are just too busy to become bored, they might refrain from delinquency (Schafer 1969 ). The routine activities theory assumes that delinquency occurs when there are opportunities, and thus engagement in structured activities, such as sports, reduces one’s time and opportunity to engage in delinquency (Cohen and Felson 1979 ).

Furthermore, the “sports build character”-idea claims that sports may contribute to the development of positive traits, skills, and virtues in youth (Sage 1990 ; Segrave 1983 ). For example, Arnolds ( 1994 ) states that athletes judge what is right or wrong according to the rules of the game, care for the wellbeing of all participants in the game, and choose an appropriate moral action. By committing to the internal goals and standards of the sports, athletes practice the exercise of virtues, such as honesty and fairness (Arnold 1994 ). It has been mentioned as well that sports teach youth to deal with setbacks, stimulate perseverance and self-control, enhance the co-operation between peers, and increase peer acceptance (Kreager 2007 ; Shields and Bredemeier 1995 ). Furthermore, higher rates of initiative and emotional regulation have been found among young athletes compared to non-athletes (Larson et al. 2006 ; Shields and Bredemeier 1995 ). Finally, there is a widely supported assumption that sports participation will lead to more self-esteem in adolescents (Adachi and Willoughby 2014 ; Findlay and Bowker 2009 ), making them less vulnerable to negative peer influences (Wild et al. 2004 ). Therefore, many scholars hypothesize that sports participation can reduce juvenile delinquency (Donnellan et al. 2005 ). In sum, there are several theories supporting the assumption that sports participation is associated with less juvenile delinquency.

On the contrary, scholars have suggested that sports participation is related to more juvenile delinquency. It has been argued that the competitive element in the sports context can actually encourage immoral behavior. Injuring an opponent, cheating, or using illegal performance-enhancing products may be rewarding if that leads to winning a game (Boardley and Kavussanu 2011 ; Lee et al. 2007 ; Nucci and Young-Shim 2005 ; Shields and Bredemeier 1995 ). Bredemeier et al. ( 1986 ) found that children participating in contact sports showed lower levels of moral judgment. As lower levels of moral judgment have been found in juvenile delinquents (Stams et al. 2006 ) and criminal offense recidivism (Van Vugt et al. 2011 ), it can be argued that certain sports activities may enhance the risk for juvenile delinquency. Finally, the culture of some sports teams have been associated with excessive alcohol consumption (Kwan et al. 2014 ), increasing the likelihood of engaging in delinquent behaviors (Barnes et al. 2002 ). All in all, there are also theories supporting the assumption that sports participation is associated with more juvenile delinquency.

Further, there are scholars who have argued that sports participation is not associated with delinquency at all, and they have criticized the theories supporting a protective influence of sports participation on juvenile delinquency. The idea that young athletes are just too busy with sports to commit crimes (Hirschi 1969 ; Schafer 1969 ) has been rejected for being too simplistic. Tappan ( 1949 ) mentioned that “If a child is disposed towards law violation … it will require much more than games and sports to do anything effective about it” (p. 150). Furthermore, it has been questioned if young athletes are in fact too busy to commit delinquent acts (Agnew and Petersen 1989 ; Chapple et al. 2005 ; Tappan 1949 ), because “even highly organized recreational activities do not absorb enough of the energy or time of a child to reduce appreciably his opportunities to engage in delinquency” (Tappan 1949 , p. 150). The idea that sports build character, and therefore protect against the development of juvenile delinquency, has been questioned too. One of the concerns about this theory is that the potential skills and virtues that are learned in the sports context may not be carried over to situations outside this context, and that the influence of sports might not be large enough to change behavioral patterns and personality traits (Shields and Bredemeier 1995 ; Tappan 1949 ). Therefore, sports participation and juvenile delinquency may not be related to each other at all.

Summarizing the abovementioned theories on the relationship between sports participation and juvenile delinquency, it can be concluded that from a theoretical point of view there is much contradiction regarding the association between sports participation and juvenile delinquency. Previously conducted empirical research has not shed a clear light on the relationship between sports participation and juvenile delinquency either, as empirical research has shown mixed and inconclusive results (Coakley 2002 ; Farb and Matjasko 2012 ; Miller et al. 2007 ). Primary studies have found that sports participation was positively (Begg et al. 1996 ; Fauth et al. 2007 ; Kelley and Sokol-Katz 2011 ), negatively (Buhrmann 1977 ; Segrave and Hastad 1982 ), or not associated (Barnes et al. 2007 ; Gardner et al. 2009 ; Miller et al. 2007 ; Wong 2005 ) with juvenile delinquency. To determine the role of sports participation in the occurrence of juvenile delinquency, the relationship between sports participation and juvenile delinquency should be clarified.

Current Study

To date, no systematic review has been conducted to examine the relationship between sports participation and juvenile delinquency, although there are multiple primary studies on the relationship between sports participations and juvenile delinquency available. This meta-analytic review aims to answer the question whether there is a relationship between sports participation and juvenile delinquency by synthesizing the previously conducted studies. Further, as the results of previous studies are inconsistent (Coakley 2002 ; Farb and Matjasko 2012 ; Gardner et al. 2009 ), there is particular interest to assess which factors moderate the association between sports participation and juvenile delinquency. A meta-analysis can provide a summary of this previously conducted research more adequately and precisely than a narrative review (Lipsey and Wilson 2001 ), and it is an appropriate method to quantify and analyze inconsistencies. Therefore, we chose to conduct a meta-analysis to assess the strength of the relationship between sports participation and juvenile delinquency, and to examine factors that may moderate this association.

The current meta-analysis addressed the following research questions: (1) What is the strength and direction of the relationship between sports participation and juvenile delinquency? (2) Which offense, study, sample, and sports characteristics moderate the relationship between sports participation and juvenile delinquency?

Inclusion Criteria

Multiple inclusion criteria were formulated to select the studies for this meta-analysis. First, juvenile delinquency has been operationalized as criminal behavior (i.e., a violation of the law) by a minor outside the sports context. We excluded other types of deviant behavior (for example, behavioral problems, status offenses, antisocial behavior, substance use, or aggression) from the current meta-analysis to increase the comparability of the outcome measures in the studies (Hofer and Piccinin 2009 ). Second, the study had to report about the relationship between sports participation and juvenile delinquency in a way that made it possible to calculate an effect size. We included studies reporting on adjusted statistics (the reported statistic is controlled for background characteristics) and unadjusted statistics (the reported statistic is not controlled for background characteristics). Third, the mean age of the sample had to be between age 12 and 18. Fourth, the study had to contain both athlete and non-athlete samples, and both delinquent and non-delinquent samples, or samples of the general population of adolescents. Finally, the variables of interest had to be measured on the individual level. Studies measuring sports participation combined with other types of activity participation and studies measuring the effect of a sports intervention were excluded.

Selection of Studies and Handling Publication Bias

All studies addressing the relationship between sports participation and delinquency in juveniles which were published before October 2015 were included in the current meta-analysis. Nine electronic databases were searched by the first author: ScienceDirect, Web of Knowledge, Ovid (including ERIC), Picarta, Wiley, Google Scholar, Proquest (including Dissertations and Theses and Sociogical Abstracts), EBSCOhost (including SPORTDiscus), and Narcis. The search string included three combined variables: a sports element, a delinquency element, and an age element. For the sports element, the following keywords were used: sport*, leisure, physical activity, after-school, or extracurricular. For the delinquency element, the following keywords were used: delinquen*, aggressi*, externali*, crim*, deviant, behavioral problem, offend*, or antisocial. For the age element, the keywords youth*, juvenile, adolescen*, or child were used. In most electronic databases it was possible to search only in specific parts of the publications (i.e., in the title, abstract, or key-words). In case the database offered this search option, we selected this option to reduce the number of unsuitable hits.

A common problem in performing a meta-analysis is that studies may not have been published because of non-significant or unfavorable findings, the so called “publication or file drawer bias” (Rosenthal 1995 ). Therefore, it is possible that the studies included in the meta-analysis are not an adequate representation of all previous studies that have been conducted. In order to prevent the problem of publication bias, we screened unpublished studies by searching the Proquest Dissertations and Theses database. Additionally, reference sections of review studies on leisure participation and behavioral problems were searched for qualifying studies. Finally, the publication lists of some experts on sports and antisocial behavior were checked for eligible studies. In case we found unpublished studies, we emailed the authors for the full text of the study, or ordered the study from the Proquest Dissertation Express.

The first author conducted the screening and selection process. When in doubt, the last author was consulted. " Appendix " presents a flow chart of the search. The initial search resulted in 414 articles, which also contained review and qualitative studies. This was narrowed down to 181 articles by inspection of the abstract and the method section, including studies examining all kinds of deviant behavior. After excluding the studies with other types of deviant behavior than delinquent behavior, 73 articles remained for thorough investigation. Finally, a total of 51 studies (with 48 independent samples, 431 effect sizes, and 132,366 participants) met the inclusion criteria. Five studies had overlapping samples; three studies (Daigle et al. 2007 ; Kelley and Sokol-Katz 2011 ; Tolk 2003 ) used the same waves of the Add Health-trial, and two studies (Gardner et al. 2009 ; Fauth et al. 2007 ) both used data from the Project on Human Development in Chicago Neighborhoods. Studies with overlapping samples were given the same study number. Table  1 shows the study characteristics of the included studies.

Study characteristics of included studies

N  = number of participants; # r ( M ) = number of effect sizes (mean); impact factor = impact factor of journal; design = cross-sectional or longitudinal; outcome = type of offense; % male = percentage of males in sample; % minority = percentage non-Caucasian; team sports = team sports versus individual sports; contact sports = contact sports yes/no; setting = setting of sports participation; CROSS = cross-sectional design; LONG = longitudinal design; Mix = study contains different categories of moderator variables; OD = overall delinquency; PRO C = property crime; PRO D = property damage; PET = petty crimes; SER =  serious/violent crimes; UN = variable unspecified in study; TEAM = team sports; IND = individual sports; CON = contact sports; SCH = school setting; OUT = out of school setting

Coding the Studies and Potential Moderators

The first author of this article coded the included studies according to the suggestions of Lipsey and Wilson ( 2001 ). The dependent variable in this meta-analysis was juvenile delinquency. The independent variable was sports participation. Ten studies ( #ES  = 46) were double coded by the first author and a research assistant. It is common to calculate the inter-rater agreement in a meta-analysis, because in addition to categorical variables, we also coded continuous variables. The inter-rater reliability proved to be good with 94 % agreement between the two coders.

The potential moderators of the association between sports participation and juvenile delinquency were grouped into offense, study, sample, and sports characteristics. The type of offense measured in the included studies was first coded as a string variable. After all studies were coded, we distinguished five types of offenses, based on the available data: overall delinquency, property crime (i.e., theft, shoplifting, stealing), property damage (i.e., vandalism), violent/serious crime (i.e., armed robbery, violent assault), and petty crime (i.e., minor offenses other than property crime or property damage).

The type of offense was coded as moderator variable, because different developmental trajectories towards different offense types have been showed (Moffitt 1993 ). Moreover, a commonly used argument supporting the association between sports participation and lower levels of engagement in delinquency is that athletes are just too busy to commit crimes (Hirschi 1969 ; Osgood et al. 1996 ; Schafer 1969 ). This seems specifically relevant when it comes to minor, opportunistic crimes (like petty crimes or property damage), because these crimes particularly originate from boredom and opportunity (Hirschi 1969 ; Osgood et al. 1996 ; Schafer 1969 ). Furthermore, it is possible that athletes withdraw from more serious crimes, as a possible sanction may jeopardize their opportunity to play (Miller et al. 2007 ). On the other hand, acting out may be part of the athletes’ culture, which can result in the engagement of minor delinquent behaviors, such as property damage and petty crimes (Miller et al. 2007 ). Therefore, the relationship between sports participation and juvenile delinquency may be moderated by offense type. In the majority of the studies (92 %) delinquency was measured by means of self-report. In four studies (8 %; #ES  = 7) delinquency was measured through file information or official data. The effect of this possible moderator could not be assessed, because the numbers were too small to obtain sufficient statistical power.

We coded several study characteristics that may influence the strength of the relationship between sports participation and juvenile delinquency. First, the impact factor of the journal in which the study was published (continuous variable) was coded, because the impact factor is a first indication of study quality (Saha et al. 2003 ). Second, the year of publication (continuous variable) was coded, because we expected that the quality of older studies was lower than the quality of more recent studies, as the statistical and methodological knowledge has increased largely in social research over the last decades. Finally, the study design was coded (cross-sectional vs. longitudinal designs), as cross-sectional studies measure the relationship between sports participation and juvenile delinquency at one point in time, and longitudinal studies are able to take the developmental aspect of the relationship between sports participation and juvenile delinquency into account.

As sample characteristics we coded the proportion of males (continuous variable) and the proportion of youth with a minority background (non-Caucasian) in the sample (continuous variable). Gender is a potential moderator, because there are gender differences in developmental pathways towards delinquency and differences in benefits of leisure activity for boys and girls (Fredricks and Eccles 2006 , 2008 ; Wong et al. 2010 , 2013 ). Ethnicity was coded as a potential moderator, as it is unknown how well the findings of previous research generalize across ethnic groups (Fredricks and Eccles 2008 ).

Multiple sports characteristics were coded as potential moderators, because the type and setting of the sports activities might be significant in whether sports participation is positively, negatively or not related to juvenile delinquency. We coded whether the type of sports were team sports or individual sports. Team sports have been related to positive developmental outcomes because these sports promote the immediate practice of social skills (Ewing et al. 1996 ). On the other hand, Rutten et al. ( 2007 ) found that soccer players tend to show more antisocial behavior than swimmers. Whether sports were contact sports or non-contact sports was also coded as a potential moderator, because previous studies have found that young athletes in contact sports report more delinquent and violent behavior than athletes in non-contact sports (Levin et al. 1995 ; Endresen and Olweus 2005 ). Finally, it was coded whether the sports activities took place in a school or out-of-school setting. Sports in a school setting often involve skilled coaches, whereas the out-of-school setting often involves volunteers who do not necessarily have a pedagogical background or lack specific coaching skills (Ewing et al. 1996 ). Moreover, within the school setting there is often consultation between the school and the coach, which can contribute to a positive effect on the development of the participants (Perkins and Noam 2007 ).

Calculation and Analysis

Effect sizes were transformed into correlation coefficient r . A positive correlation indicated that athletes are more delinquent than non-athletes, whereas a negative correlation can be interpreted as athletes being less delinquent than non-athletes. Effect sizes were calculated using the calculator of Wilson ( 2013 ) and formulas from Lipsey and Wilson ( 2001 ). If an article only mentioned that the relationship was not significant, an effect size was coded as zero (Lipsey and Wilson 2001 ), and a sensitivity analysis was conducted to test if this decision affected overall results. We also performed a sensitivity test to see if the inclusion of the adjusted effect sizes affected the overall results.

Continuous variables were centered on the mean, and categorical variables were recoded into dummy variables. Extreme values of the effect sizes (>3.29 SD from the mean; Tabachnik and Fidell 2013 ) were adjusted by winsorizing these outliers. Four outliers were identified at the lower bound of the distribution (range r  = −.6790 to −.4170), they were winsorized to the value of r  = −.4090. One outlier was identified at the upper bound of the distribution ( r  = .6690), this outlier was winsorized to the value of r  = .4299. Correlation coefficients r were recoded into Fisher z-values (Lipsey and Wilson 2001 ). After the analyses, the Fisher z-values were transformed back into correlation coefficients for interpretation and reporting. Standard errors and sampling variance of the effect sizes were estimated using formulas by Lipsey and Wilson ( 2001 ).

By including multiple effect sizes per study, the assumption of independent effect sizes that underlie classical meta-analytic strategies was violated (Hox 2002 ; Lipsey and Wilson 2001 ). To deal with the interdependency of effect sizes, we applied a multilevel approach to the present meta-analysis as suggested by Van den Noortgate and Onghena ( 2003 ). A multilevel approach has the advantage that it accounts for the hierarchical structure of the data, where the effect sizes are nested within the studies. Therefore, all information in the studies can be preserved and maximum statistical power is generated, which allows comprehensive moderator analyses to assess the influence of offense, study, sample, and sports characteristics on the relationship between sports participation and juvenile delinquency (Van den Noortgate and Onghena 2003 ). We used a 3-level random effects model to account for three levels of variance, including the sampling variance for each effect size (level 1), the variance between effect sizes within a study (level 2), and the variance between the studies (level 3) (Wibbelink and Assink 2015 ). The meta-analysis was conducted in R (version 3.2.0) with the metafor-package, employing a multilevel random effects model (Houben et al. 2015 ; Van den Bussche et al. 2009 ; Viechtbauer 2010 ). This model is adequate and often used for multilevel meta-analyses, and in general superior to the fixed-effects approaches used in traditional meta-analyses (Van den Noortgate and Onghena 2003 ).

To estimate the model parameters the restricted maximum likelihood estimate (REML) was applied (Van den Noortgate and Onghena 2003 ). The Knapp and Hartung-method ( 2003 ) was performed to test individual regression coefficients of the models and for calculating the corresponding confidence intervals. The Knapp and Hartung-method ( 2003 ) has the advantage that it reduces Type I-errors (Wibbelink and Assink 2015 ). Likelihood ratio tests were used to compare the deviance scores of the full model and the models excluding the variance parameters of level 2 or 3, making it possible to determine whether significant variance is present at the two levels (Wibbelink and Assink 2015 ). In case there was significant variance on these two levels, the distribution of effect sizes was considered to be heterogeneous. This indicates that the effect sizes could not be treated as estimates of a common effect size, and moderator analyses were performed. For models including moderators, an omnibus test of the fixed-model parameters was conducted, which tests the null hypothesis that the group mean effect sizes are equal. Therefore, the test statistics of the moderator analyses were based on the F-distribution.

Although we made several efforts to prevent publication bias by our search strategy, this could not guarantee the absence of publication bias. In order to assess the influence of publication bias, we first tested funnel plot asymmetry according to Egger’s method (Egger et al. 1997 ). A funnel plot is a scatter plot of the effect sizes against the effect size’s precision (the inverse of the standard error). In case of publication bias, a gap in the effect size distribution would be present, showing an asymmetrical funnel plot and a significant Egger’s test. Second, we performed a trim and fill procedure (Duval and Tweedie 2000 ) by drawing a trim and fill plot in MIX 2.0 (Bax 2011 ). The trim and fill procedure corrects for funnel plot asymmetry by imputing estimated missing effect sizes that are calculated on the basis of existing effect sizes. If the trim and fill plot showed missing effect sizes, we imputed these estimated effect sizes of missing studies to the meta-analytic data, and reran the multilevel meta-analysis in R, as this shows the influence of the estimated missing data on the overall effect of the meta-analysis. Finally, the skewness of the effect size distribution was calculated in SPSS, because if publication bias is present, a skew distribution of the effect sizes would be expected (Begg and Mazumdar 1994 ).

Table  2 presents the results of the multilevel meta-analysis. The overall association between sports participation and juvenile delinquency can be found in this table, as well as the results of the moderator analysis. Only moderator variables with a significant contribution to a better fit of the model are reported in this table.

The overall results and moderator effects relationship between sports participation and juvenile delinquency

# studies = number of independent studies; # ES = number of effect sizes; t 0  = difference in mean r with zero; t 1  = difference in mean r with reference category; mean r  = mean effect size ( r ); F (d f 1 , d f 2 ) = omnibus test; RC = reference category

*  p  < .05; **  p  < .01

Overall Relationship Sports Participation and Juvenile Delinquency

No significant association was found between sports participation and juvenile delinquency ( r  = .005; 95 % CI −.023 to .033; p  > .05), suggesting that there is no significant overall relationship between athletic status and the level of delinquent behavior in adolescents.

Sensitivity analysis excluding the adjusted effect sizes (effect sizes controlled for background characteristics) had little effect on the overall association between sports participation and juvenile delinquency ( r  = −.001; 95 % CI −.039 to .037; p  > .05). The sensitivity analysis excluding the studies where a reported null effect was coded as r  = 0 did not affect the overall association between sports participation either ( r  = .006; 95 % CI −.023 to .034; p  > .05; # studies = 47; # ES = 424).

When checking for publication bias, first, Egger’s method did not indicate funnel plot asymmetry, because the intercept was not significant ( t  = −0.118, p  = .906). However, the trim and fill plot revealed that there were some missing effect sizes, indicating publication bias. The trim and fill plot in Fig.  1 shows the imputation of estimated effect sizes with negative correlation coefficients (represented by the white dots) on the left side of the funnel. This indicates the absence of studies reporting that athletes are less delinquent than non-athletes. To check if this possible publication bias influenced the overall association between sports participation and juvenile delinquency, we added the imputed estimates to the data. Table  2 shows that imputation of the estimated effect sizes to the meta-analysis did not render results significantly ( r  = −.022, p  > .05). Finally, the skewness test was not significant ( Z  = −1.263, p  > .05), indicating that the effect size distribution was not skewed. Although there was some indication of publication bias according to the trim and fill analysis, we concluded that our findings are robust to the threat that excluded studies might have yielded a significant effect, because after imputation of the estimated effect sizes the overall mean effect size remained non-significant.

Trim-and-fill plot. Note graph from Bax ( 2011 )

The likelihood ratio test comparing models with and without between-study variance (level 3) showed that significant variance was present at the between-study level ( σ level 3 2 = 0.007 , χ 2 (1) = 215.784; p  < .0001). The variance between the effect sizes within studies (level 2) was significant as well ( σ level 2 2 = 0.005 , χ 2 (1) = 1965.307; p  < .0001), indicating a heterogeneous effect size distribution. About 4 % of the total effect size variance was accounted for the sampling variance (level 1), 39 % for the variance between effect sizes within studies (level 2), and 57 % for the variance between studies (level 3). In case of heterogeneous effect size distributions, moderator analyses are advised to assess whether the variance between the effect sizes can be explained by certain factors, regardless of the significance of the overall effect size. Therefore, we conducted moderator analyses on offense, study, sample, and sports characteristics to examine the strength of the relationship between sports participation and juvenile delinquency. Table  2 shows the results of the moderator analyses.

Type of Offense

The type of offense did not moderate the relationship between sports participation and juvenile delinquency ( F (4,426) = 5.556; p  > .05). The associations between sports participation and respectively property crime, property damage, serious/violent crime, and petty crime did not deviate from the association between sports participation and overall delinquency. None of the specific types of offenses were significantly related with sports participation.

Study Characteristics

Several study characteristics had a moderating effect on the relationship between sports participation and juvenile delinquency (see Table  1 ). The impact factor of the journal in which the study was published significantly moderated the relationship between sports participation and juvenile delinquency ( F (1,177) = 7.650; p  < .01). Among published articles, stronger, positive associations between sports participation and juvenile delinquency were found for studies in the more frequently cited journals. Moreover, the type of study seemed to influence the relationship between sports and juvenile delinquency ( F (1,429) = 6.387; p  < .05). Only among studies using longitudinal designs significant results were found ( r  = .074), indicating that athletes were more delinquent than non-athletes. Furthermore, the year of publication did not moderate the strength of the relationship between sports participation and juvenile delinquency.

Sample Characteristics

Only gender moderated the relationship between sports and juvenile delinquency ( F (2,413) = 4.856; p  < .05). Studies with lower proportions of males in the sample, showed more positive correlations with juvenile delinquency. To be able to interpret this result more clearly, we conducted post hoc analysis with a more stringent α-level of .025, with all-male, mixed, and all-female samples in the analysis. In this post hoc analysis, gender significantly moderated the relationship between sports participation and delinquency ( F (2,413) = 4.259; p  < .025). The correlations between sports participation and juvenile delinquency significantly differed in all-female samples from the all-male samples. However, the individual categories did not show significant correlations between sports participation and juvenile delinquency (male samples r  = −.013, mixed samples r  = .013, female samples r  = .027; p  > .05). The proportion of adolescents from ethnic minority groups did not moderate the relationship between sports participation and juvenile delinquency.

Sports Characteristics

Moderating effects were found for multiple sports characteristics. The type of sport had a moderating effect on the relationship between sports participation and juvenile delinquency ( F (1,139) = 7.889; p  < .01). Individual sports showed a significant mean association ( r  = .057), indicating that athletes of individual sports were more delinquent than non-athletes, whereas no relationship between sports participation and juvenile delinquency was found in team sports. Further, the setting of the sports participation (whether the sports were school-based or in an out-of-school setting) moderated the relationship between sports and juvenile delinquency ( F (1,290) = 6.094; p  < .05). However, the individual categories did not show significant correlations for the relationship between sports participation and juvenile delinquency (school setting mean r  = −.047, out of school setting mean r  = .042, both p  > .05). Finally, whether or not the athletes participated in contact sports did not moderate the relationship between sports participation and juvenile delinquency.

Sports participation plays an important role in the lives of adolescents. Much is known about the positive associations between sports participation and psychosocial health (Eime et al. 2013 ; Janssen and LeBlanc 2010 ), but theoretical and empirical knowledge about the relationship between sports participation and juvenile delinquency is lacking (Coakley 2002 ; Farb and Matjasko 2012 ; Nichols 2007 ). Nevertheless, sports are used worldwide to prevent juvenile delinquency (Cameron and MacDougall 2000 ; Hartmann 2003 ; Kelly 2013 ; Miller et al. 2007 ; Nichols 2007 ; Sandford et al. 2006 ). This multilevel meta-analysis is the first systematic review that examined the association between sports participation and juvenile delinquency by synthesizing previous research on sports participation and juvenile delinquency.

Overall, no significant association was found, indicating that there was no significant relationship between sports participation and juvenile delinquency ( r  = .005). This result was maintained even after controlling for possible publication bias by a trim and fill procedure. However, the distribution of effect sizes was heterogeneous, indicating that there was variation between the effect sizes within and across studies, possibly explained by moderators. Therefore, we conducted moderator analyses on offense, study, sample, and sports characteristics.

Moderator analyses showed that the type of offense did not influence the relationship between sports participation and juvenile delinquency, and that sports participation was not associated with overall delinquency, serious/violent crime, property crime, property damage, or petty crime. Some study, sample, and sports characteristics did influence the relationship between sports participation and juvenile delinquency. Athletes were more delinquent than non-athletes in studies published in more frequently cited journals and using longitudinal designs. Furthermore, gender influenced the relationship between sports participation and juvenile delinquency. In all-female samples, more positive correlations were found than in all-male samples. Finally, the setting of the sports environment and whether it was a team or individual sport moderated the relationship with juvenile delinquency. Athletes participating in an out-of-school setting appear to have less favorable outcomes regarding juvenile delinquency compared to athletes in a school setting. Individual sports were associated with less delinquency, whereas for team sports no significant results were found. However, it has to be noted that, although there were significant moderating effects from study, sample, and sports characteristics, the correlations found in the moderator analyses were extremely small (in all cases r  < .08), and it is expected that the practical or clinical value of these findings is minimal.

From the results of the current meta-analysis, we conclude that, in general, sports involvement is not reliably related to more or less juvenile delinquency, and that this non-significant association is only marginally affected by the moderating factors that were assessed in the current study. This conclusion has some important theoretical implications. Contrary to many criminological theories, such as Hirschi’s ( 1969 ) theory of social bonds, the boredom theory (Schafer 1969 ), and the routine activities theory (Cohen and Felson 1979 ), sports alone fail to protect youth from delinquent behaviors. In line with other researchers and theorists, we conclude that sports participation by itself may not be enough to increase protective social bonds and to eliminate boredom and opportunities for crimes in order to reduce delinquent behavior (Agnew and Petersen 1989 ; Tappan 1949 ; Wong 2005 ). On the other hand, contrary to theories assuming that sports participation is associated with more delinquency (i.e., the theories on the antisocial influence of sports because of the competitive element of sports and the alcohol consumption culture), sports do not seem to increase delinquent behavior among youth either. One explanation of the finding of no significant overall effect could be that sports participation is not associated with juvenile delinquency at all. The assumed positive influences of sports may not be strong enough to affect behaviors and skills outside the sports context, and to protect against juvenile delinquency (Shields and Bredemeier 1995 ; Tappan 1949 ). Another explanation we would like to propose is the possibility that protective influences of sports participation may be attenuated by the negative influences of sports participation on the development of juvenile delinquency. In this view, we acknowledge the potential positive influences of sports, but also consider a possible risk of sports participation regarding the development of juvenile delinquency.

Our suggestion that the positive and negative influences of sports participation on juvenile delinquency may countervail each other has implication for the realization of an appropriate sports context. In the sports environment, the protective influences of sports on juvenile delinquency must be highlighted, and the negative influences on the development of juvenile delinquency confined. The results of the current meta-analysis showed that more favorable outcomes (i.e., less delinquency) were found in sports participation within school settings and in team sports. This may be explained by the involvement of skilled coaches in school settings, while the out-of-school setting often involves volunteers who do not necessarily have a pedagogical background or lack specific coaching skills (Ewing et al. 1996 ). Further, within the school setting, there is often consultation between the school and the coach, which can contribute to a positive effect on the development of the participants (Perkins and Noam 2007 ). Team sports may have been related to less delinquency, because these sports promote the immediate practice of social skills (Ewing et al. 1996 ).

Previous studies have offered some implications for the development of an adequate sports context as well. The beneficial effects of sports can be expected when there is a climate of “fair play”-mentality and when team play, the development of athletes, and acquiring skills are considered more important than performance (Guivernau and Duda 2002 ; Miller et al. 2005 ; Rutten et al. 2007 ). The sports coach plays a significant role in providing an adequate sports context that leads to positive psychological outcomes in athletes (Côté and Gilbert 2009 ; Ntoumanis et al. 2012 ; Smith et al. 2007 ). Knowledge of education, interpersonal skills, the ability to reflect upon oneself, and understanding of the developmental needs of individual adolescent athletes are important characteristics of coaches, which might positively affect the development of young athletes (Côté and Gilbert 2009 ). In sum, we argue that sports participation may protect against juvenile delinquency when the sports environment consists of elements that guarantee a positive and safe sports environment (Côté and Gilbert 2009 ; Rutten et al. 2007 ).

In the current meta-analysis, it was difficult to test our hypothesis of a protective influence of sports on juvenile delinquency when the sports environment is able to guarantee an appropriate context for development and negative aspects of sports are minimized. None of the included studies provided information about relevant characteristics of the sports environment, such as the quality of the relationship with the coach, the education of the coach, and the quality of the moral atmosphere of the sports environment (Rutten et al. 2007 ). Future research with longitudinal designs should focus on these contextual factors to understand more about the relationship between sports participation and juvenile delinquency, and mechanisms that contribute to positive developmental outcomes in adolescents.

There are some limitations of this study that need to be addressed. First, this study included non-published, non-peer reviewed manuscripts with weak study designs. Second, we combined unadjusted and adjusted effect sizes in the meta-analysis. This may be problematic, because the adjusted effect size may be smaller or larger than the related unadjusted effect size, which can affect the overall effect size (Aloe and Thompson 2013 ). On the other hand, the sensitivity analysis showed that the exclusion of the adjusted effect sizes had little effect on the overall relationship between sports participation and juvenile delinquency. Therefore, we argue it is justified to include the adjusted effect sizes in the meta-analysis, and with that, to prevent publication bias. Third, the included studies did not always provide detailed information about sample and sports characteristics. In the majority of studies, the independent variable was described as the general term “sports”. Previous research, as well as the current study, showed that specific characteristics of sports or the sports environment influence the relationship between sports participation and juvenile delinquency (Endresen and Olweus 2005 ; Rutten et al. 2007 ). However, because of the lack of a distinction between the different types of sports in the studies and characteristics of the sports context, we could only include a limited number of moderators. Finally, youth with more proneness towards delinquency may show lesser or greater chances to get involved in sports participation. Thus, the results of this meta-analysis may be influenced by self-selection bias (Fredricks and Eccles 2006 ). As the present meta-analysis consists of mostly cross-sectional studies aimed to assess the relationship between sports participation and juvenile delinquency, we refrain from making a causal statement about the effects of sports participation on juvenile delinquency.

Despite the limitations, the current meta-analysis has several strengths. First of all, this is the first systematic review on the relationship between sports participation and juvenile delinquency filling gaps in theoretical and empirical knowledge on two important topics in adolescence. Second, by using an advanced multilevel approach that allowed for the inclusion of multiple effect sizes per study, comprehensive moderator analyses were possible, leading to a better understanding of (the lack of) moderating influences. Third, we increased the comparability of the studies included in the meta-analysis by using a narrow definition of juvenile delinquency. Finally, we have made efforts to prevent publication bias by conducting an extensive systematic literature search and including unpublished studies. The advantage of including unpublished studies is that it increases the representativeness of the selected studies and decreases the chances of publication bias (Duval and Tweedie 2000 ). Moreover, we controlled for the possible publication bias by performing a trim and fill procedure. All in all, the strengths of this meta-analysis assure the representativeness of the finding of no overall significant relationship between sports participation and juvenile delinquency, providing an important contribution to the research on adolescence.

Conclusions

Despite the large role of sports in the development of adolescence, little is known about the relationship between sports participation and juvenile delinquency. There is much controversy on whether sports participation should be perceived as a protective or a risk factor for the development of juvenile delinquency. This study aimed to provide more insight in the association between sports participation and juvenile delinquency. The findings of this multilevel meta-analytic review showed that, overall, sports participation was not related to juvenile delinquency. Some significant moderators were identified, but the influences of the study, sample, and sports characteristics examined in this review were minimal. We have explained these results by the suggestion that the alleged positive influences of sports may be countervailed by the supposed negative influences of sports. This has implications for the way that sports activities are implemented for adolescents. The sports context may amplify the positive elements of sports, such as the opportunity to form prosocial relationships with peers and the coach (Fredricks and Eccles 2005 ; Rutten et al. 2007 ), practice social skills (Vidoni and Ward 2009 ), and decrease the elements that may contribute to juvenile delinquency, such as the emphasis on competition (Stanger et al. 2013 ). Improving the pedagogical quality of the sports environment and including those measures in research on sports participation and psychosocial development may provide important knowledge to realize the potential positive influence of sports activities on juvenile delinquency.

Author Contributions

AS participated in its design, performed the statistical analysis, interpreted the results and drafted the manuscript; EvV helped to draft the manuscript; CvdP conceived of the study and critically reviewed the manuscript; TvdS participated in the design of the study, and critically reviewed the manuscript; GS conceived of the study, participated in the interpretation of the results, and critically reviewed the manuscript. All authors read and approved the final manuscript.

Biographies

is a Ph.D.-student at the University of Amsterdam. She received her master’s degree in Forensic Child and Youth Care Studies from the University of Amsterdam in 2014. Her research interests include juvenile delinquency, sports participation and externalizing behaviors, meta-analytical approaches, and forensic child psychology.

is an assistant professor at the University of Amsterdam. She received her doctorate in Forensic Child and Youth Care Studies in 2011 for a dissertation on the moral development of juvenile sex offenders. Her major research interests include delinquent behavior of girls, juvenile sex offenders, moral development.

is a post-doctoral researcher at the University of Amsterdam. She received her doctorate in risk and needs assessment for juvenile delinquents. Her major research interests include the prevention of child maltreatment and juvenile delinquency, risk/protective factors for juvenile delinquency and child abuse and risk and needs assessment.

is a Ph.D.-student at the University of Amsterdam. She received her master’s degree in Forensic Child and Youth Care Studies from the University of Amsterdam in 2013. Her research interests include social skills interventions, martial arts, and child development.

is a full professor at the University of Amsterdam. He received his doctorate in 1998 on the study of the development of adopted children. His major research interests include forensic child and youth care, moral development, attachment, and meta-analysis.

Conflict of interest

The authors report no conflict of interests.

Contributor Information

Anouk Spruit, Email: [email protected].

Eveline van Vugt, Email: [email protected].

Claudia van der Put, Email: [email protected].

Trudy van der Stouwe, Email: [email protected].

Geert-Jan Stams, Email: [email protected].

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• Published: 15 August 2018

Examining the relationship between sports participation and youth developmental outcomes for socially vulnerable youth

• Sabina Super   ORCID: orcid.org/0000-0002-2586-1953 1 ,
• Niels Hermens 1 , 2 ,
• Kirsten Verkooijen 1 &
• Maria Koelen 1  

BMC Public Health volume  18 , Article number:  1012 ( 2018 ) Cite this article

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Research has shown that sports participation is positively related to youth developmental outcomes, but it is still unknown if sports participation relates to these outcomes among socially vulnerable youth. Hence, this research aimed to examine the relationship between sports participation and youth developmental outcomes (i.e., problem behaviour, pro-social behaviour, school performance, subjective health, well-being, self-regulation skills, and sense of coherence) for socially vulnerable youth. In addition, the stability of the relationship between sports participation and the youth developmental outcomes were investigated with a six-month interval.

Two identical questionnaires were administered with a six-month interval by youth professionals from four youth organisations, measuring the youth developmental outcomes and sports participation rates of socially vulnerable youth. In total, 283 socially vulnerable youths (average 14.68 years old) participated at baseline and 187 youths after six months.

The results showed that sports participation was positively related to pro-social behaviour, subjective health, well-being, and sense of coherence at both measurements. We found no evidence for the relationship between sports participation and problem behaviour and the self-regulatory skills. In addition, sports participation was only positively related to school performance at the first, but not at the second, measurement.

Conclusions

The results of this study show that there are positive relationships between sports participation and several youth developmental outcomes. Based on the current data no conclusions can be drawn about the causal relationship between sports participation and youth developmental outcomes. Given the focus of policymakers and health professionals on sport as a means to achieve wider social and educational outcomes for young people, including in the Netherlands, further research is needed to shed light on the relationship between sports participation and youth developmental outcomes for socially vulnerable youth, with a special focus on this group’s heterogeneity.

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Trialregister.nl NTR4621 Date of Registration: 2 June 2014 (retrospectively registered).

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Researchers and policymakers have often advocated that sports participation can be beneficial for the personal development of young people [ 1 , 2 ] as studies have found evidence that sports participation can benefit not only physical health, but also mental, cognitive and social health (see for reviews [ 3 , 4 ]). The Human Capital Model developed by Bailey [ 3 ], for example, gives a comprehensive overview of six different forms of capital that showed to have a positive relationship with sports participation: physical capital, emotional capital, individual capital, social capital, intellectual capital and financial capital. Underlying the Human Capital Model is the assumption that competencies, skills and knowledge can be acquired by participating in sport resulting in positive youth development. The evidence base for the different forms of capital gains from sports participation is diverse, with strong evidence supporting physical capital gains but with weaker evidence for individual capital gains or financial capital gains. Nonetheless, the sports setting is often considered an avenue for positive youth development [ 1 ].

Organising inclusive sports activities is considered to be especially relevant for socially vulnerable youth who are characterised by an accumulation of negative experiences with the institutions in their lives [ 5 ]. The negative experiences with institutions can relate to the family domain (e.g., the parents have financial problems or youths experience domestic violence), to the school domain (e.g., youths are bullied at school), to the judicial system (e.g., after drug use or after a crime) or to the community (e.g., living in a bad neighbourhood with high crime rates). These negative experiences lead to distorted and disconnected relationships with those institutions [ 5 ] and as a result socially vulnerable youth are often confronted with feelings of incompetence, rejection, isolation and a low self-esteem. Considering that socially vulnerable youth participate less often in sport than their non-vulnerable peers [ 6 ], there is great potential to engage these young people in a pedagogical and supportive setting. However, the relationship between sports participation and developmental outcomes amongst socially vulnerable youth is hardly investigated.

The present study

A large body of evidence is available that suggests that sports participation is positively associated with more healthy behaviours [ 7 , 8 ], improved school performance [ 9 , 10 ], improved subjective health [ 3 , 11 ], and increased well-being [ 12 , 13 ] in young people. However, this research has paid little attention to investigating this relationship among socially vulnerable youth groups . Recent reviews examining the effects of sports programs on the personal development of socially vulnerable youth also concluded that very little research has been conducted among this specific youth group [ 14 , 15 ] and that the effects of sports participation on youth development were inconsistent. Indeed, Ten Broeke demonstrated that knowledge in developmental psychology is largely based on research that has been conducted in Western, Caucasian, and middle-class research populations [ 16 ]. Yet, Henrich et al. [ 17 ] argue that the results from studies among these WEIRD populations – White, Educated, Industrialised, Rich, and Democratic – are the least representative to generalise to other populations. Because there is still limited and inconsistent evidence regarding the relationship between sports participation and youth development amongst socially vulnerable youth, there is a need for further research. Hence, this first aim of this study is to investigate, among socially vulnerable youth, the relation between sports participation and indicators of youth development, as measured in: (a) behaviour, (b) school performance, (c) subjective health, and (d) well-being.

The second aim of this study is to investigate, among socially vulnerable youth, the relation between sports participation and two proximal outcomes: self-regulation skills and sense of coherence. The first proximal outcome, self-regulations skills, refers to a specific set of assets that may be relevant for the longer-term developments in the distal outcomes behaviour, school-performance, subjective health, and well-being. These self-regulations skills are: planning, self-evaluation, monitoring, effort, reflection, and self-efficacy [ 18 ]. Self-regulation is considered to have an influence on a person’s success [ 19 ] in the broadest sense of the word and in various societal domains [ 20 , 21 , 22 ]. Self-regulatory skills have previously been found to correlate positively with young people’s sports participation [ 22 , 23 , 24 , 25 ]. A study by Jonker et al. [ 23 ] demonstrated that pre-university students (12–16 years) participating in sport scored higher on planning, reflection, and effort than their pre-university peers that did not participate in sport. Posner and Rothbart [ 26 ] state that the development of self-regulation in children is influenced by both genes and the environment in which children live. Specific exercises during childhood, especially attention training, can improve self-regulation skills. In this respect, it has been claimed that youths that participate in sport have increased opportunities to train and develop self-regulation skills [ 25 ]. In addition, it has been pointed out that people develop self-regulatory skills best in inspiring environments that are rich in feedback and that require goal-setting [ 22 ], characteristics that are frequently present in the sports setting. According to Piché et al. [ 24 ], there exists a mutual relation between sports participation and self-regulation. The authors found that kindergarten childhood participation in physical activity predicted self-regulation skills in the fourth grade. Moreover, they found that kindergarten childhood self-regulation skills predicted participation in physical activity in the fourth grade. Current studies on the relationship between sports participation and self-regulation focussed only to a limited extent on vulnerable youth groups.

The second proximal outcome, sense of coherence, explains people’s capacity to cope with stressful life challenges in a health-promoting way [ 27 , 28 ]. Sense of coherence has a vital role in orienting a person towards understanding a specific stressor (i.e., comprehensibility), in evaluating the resources that might be available to deal with everyday life stressors (i.e., manageability), and in engaging with challenges as a meaningful process (i.e., meaningfulness). Individuals with a relatively strong sense of coherence are better able to comprehend the stressors that they encounter in everyday life and have a general confidence that resources are available to meet the demands posed by stressful situations [ 27 ]. Furthermore, they consider stressors more as a meaningful challenge than as a threat and, hence, they are better able to select effective coping mechanisms, resolving tension in a health-promoting manner. Previous studies have found a positive relationship between sports participation and sense of coherence [ 29 , 30 , 31 ]. Yet, to the best of the authors’ knowledge, this relationship has not been studied in vulnerable youth groups.

The third aim of this study is investigate the stability of the relationship between sports participation and youth developmental outcomes. Research has shown that socially vulnerable youth face a turbulent life characterised by challenges and stressors on a daily basis [ 5 ] which can influence their ability to participate in sport at a given moment [ 6 ]. In addition, how they report on developmental outcomes (e.g., subjective health or well-being) may fluctuate depending on the amount of stressors they are experiencing at a specific moment. To understand better how sports participation is related to youth developmental outcomes, the stability of this relationship should be accounted for. It is for this reason that data were collected among socially vulnerable youth by administering two identical questionnaires with a six-month interval.

Summarising, the following three study aims were formulated:

To investigate, among socially vulnerable youth, the relation between sports participation and indicators of youth development, as measured in: (a) behaviour, (b) school performance, (c) subjective health, and (d) well-being.

To investigate, among socially vulnerable youth, the relation between sports participation and self-regulation skills (i.e., planning, monitoring, effort, and reflection) and sense of coherence.

To investigate the stability of the relationship between sports participation and youth developmental outcomes.

This study is part of the research project Youth, Care and Sport, set up to study the value of sport for socially vulnerable youth (see for a detailed description [ 32 ]). Cross-sectional data were collected with two identical questionnaires administered with a six-month interval among socially vulnerable youth.

Study population

Data were collected via four youth organisations that work with socially vulnerable youth (between 12 and 23 years old). The participating youth organisations provide services to youths who are (temporarily) experiencing problems in their personal development, for example because they have learning or behavioural problems or because they live in settings that hinder this development (e.g., parents incapable of providing proper care). The services provided by these organisations include school social work and educational counselling services as well as more specialised (mental) healthcare. The youth organisations are funded by a complex mix of government subsidies and private funding. The participating youth organisations were a youth care organisation in a large Dutch city and three schools for special education of which two were located in a large Dutch city and one in a rural area.

The youth professionals employed at the participating organisations asked the youths, which were clients of the youth organisations, to participate in the study. This procedure resulted in a non-randomised, purposive sample of participants. At Time 1 (T 1 ), data were collected on 283 youths. Nine youths completed less than half of the baseline questionnaire and were removed from the sample, leading to a sample size of 274 participants (209 boys and 65 girls). The average age of the youths was 14.68 ( SD  = 1.69). At the six-month follow-up (T 2 ), 194 participants completed the questionnaire. After removing seven youths from the sample because they completed less than half of the questionnaire, the remaining 187 participants were used in the analyses (follow-up rate: 68.2%). The main reason for dropout was that the youths had left the youth organisation, for example because their treatment plan was finalised or because they dropped-out of school. The youths that dropped out at T 2 were significantly older at T 1 ( M  = 15.29, SD  = 1.97) than the youths that completed the questionnaire at T 2 ( M  = 14.41, SD  = 1.47), t (267) = 4.062, p  < .001. No other significant differences were found between the youths that did or did not complete the second questionnaire.

Data collection

Data were collected via paper questionnaires that contained questions adapted to the language and cognitive skills of the study population. A pilot test was conducted within one unit of a youth organisation to see whether the questionnaire was understandable for the youths. The five participating youths indicated that the included questions were clear and comprehensible. However, to reduce the burden for the participants, the Motivational Climate Scale for Youth Sports [ 33 ] was removed from the questionnaire. On average, the youths needed between 15 and 20 min to fill in the questionnaire.

Due to the vulnerable nature of the study population, special attention was paid to obtaining informed consent. An information letter that contained detailed information about the aim and the set-up of the study was sent to the parents. The letter included information about the confidential use of the data for this research and guaranteed parents that the data would not be distributed to third parties, would not be discussed with the youth professionals, and would be solely used for the research project Youth, Care and Sport. Parents were asked to contact the youth professional if they objected to their child’s participation in the study (i.e., passive informed consent). The youth professionals involved in the data collection were instructed by the researchers about the data collection procedure. These instructions also included the ethical aspects of administering the questionnaires and the rights of the youths that participated in the study. Consequently, the youth professionals that administered the questionnaires made sure that the youths knew that participation was on a voluntary basis and that they had the right to stop participating at any time without any repercussions. Youths that agreed to take part in the research project (i.e., oral informed consent) received a questionnaire from the youth professional. During the data collection, a youth professional was present to answer any of the youths’ questions regarding the items in the questionnaire. The questionnaires were administered in various settings, but mostly in a classroom setting or at the youth’s home. After completion of the second questionnaire (T 2 ), the youths received a gift voucher for their participation. This project was performed in accordance with the code of conduct for minors [ 34 ] and with general ethical guidelines for behavioural and social research in the Netherlands, peer-reviewed, and approved by the review board of the Wageningen School of Social Sciences.

Demographic data were gathered regarding the participant’s age, sex, and the youth organisation responsible for collecting the data (T 1 ). The following measures were included in the two questionnaires:

Distal youth developmental outcomes

Four distal youth developmental outcomes were included in the questionnaire: (a) behaviour, (b) school performance, (c) subjective health, and (d) well-being. In order to assess behaviour , the Strengths and Difficulties Questionnaire (SDQ) was administered [ 35 ]. This instrument has often been used as a screening tool for behavioural disorders [ 36 , 37 ], and the psychometric properties have previously been found satisfactory in a Dutch sample of non-vulnerable children and adolescents [ 38 ]. The SDQ contains five sub-scales of five items each: hyperactivity (example item: “I am restless, I cannot stay still for long”), emotional symptoms (example item: “I worry a lot”), conduct problems (example item: “I often have temper tantrums or hot tempers”), peer problems (example item: “I have one good friend or more”), and pro-social behaviour (example item: “I am helpful if someone is hurt, upset, or feeling ill”). The items could be scored on a three-point scale: ‘not true’, ‘somewhat true’, and ‘certainly true’. Following Goodman’s [ 35 ] procedures, a total SDQ score was calculated by using the subscales hyperactivity, emotional symptoms, conduct problems, and peer problems (T 1 α = .73; T 2 α = .72). Higher total SDQ scores reflect a higher rate of behavioural disorder. The fifth subscale, pro-social behaviour, was computed by taking the average of the five pro-social items, and higher scores reflect more pro-social behaviour. The internal consistency of the pro-social behaviour scale was marginal (T 1 α = .61; T 2 α = .67). As a self-developed indicator of school performance , youths were asked to report how their teacher was likely to evaluate their work. The five-point scale ranged from ‘bad’ to ‘excellent’. The youths’ subjective health was assessed using a question from the Short Form Health Survey (SF-36) [ 39 ]. Both at T 1 and T 2 , youths answered the following question “In general, how good is your health?” on a five-point scale ranging from ‘bad’ to ‘excellent’. Finally, the youths were asked to answer the following question “How are you currently feeling?” on a five-point scale ranging from ‘bad’ to ‘excellent’, as an indicator of well-being .

Proximal youth developmental outcomes

Two proximal youth developmental outcomes were included in the questionnaire: (a) self-regulation skills, and (b) sense of coherence. The self-regulation skills were assessed using the Self-Regulation of Learning Self-Report Scale [ 18 ]. The original scale consisted of six subscales, but, to reduce the burden for the participants, four subscales were selected for this study. The selection was based on previous research that indicated that participation in sport was most strongly related to these four scales [ 22 ] and on the relevance of these scales for the purpose of this study. All the items could be scored on a four-point scale ranging from ‘almost never’ to ‘almost always’. Example items were: “I determine how to solve a problem before I begin” (planning), “I check how well I am doing when I solve a task” (monitoring), “I concentrate fully when I do a task” (effort), and “I try to think about my strengths and weaknesses” (reflection). Scores on the subscale items were averaged, with higher values representing stronger self-regulatory skills. The internal consistency of the scales was satisfactory: planning (eight items, T 1 α = .85; T 2 α = 87), monitoring (six items, T 1 α = .78; T 2 α = .82), effort (nine items, T 1 α = .83: T 1 α = .83), and reflection (five items, T 1 α = .80; T 2 α = .88). Sense of coherence was measured using the Dutch translation of the Orientation to Life Questionnaire (SOC-13) adapted to young people [ 40 ]. The 13 items of this scale could be scored on a five-point scale from ‘almost never’ to ‘almost always’, with the exception of two items that were positively formulated and could be scored from ‘very bad’ to ‘very good’ (T 1 α = .83; T 2 α = .84). Example items are: “How often has it happened that people who you counted on disappointed you?” and “How often do you have feelings that you’re not sure you can keep under control?” A sum score was calculated for the 13 items, with higher scores reflecting a stronger sense of coherence.

Sports participation

Measurements regarding the youths’ sports participation were based on the Dutch Guideline for Sport Participation Research (Richtlijn Sportdeelname Onderzoek (RSO)), with recall periods adapted to fit the timeframe of this research [ 41 ]. The questions were preceded by a short explanation of the definition of sports participation, to make sure that all participants understood what sports participation entailed: “Examples of sport are football, badminton, fitness, and bike tours, but not doing puzzles, walking a dog, or cycling to school. Physical activity during school times (physical education or playing outside) is not included”. The items included in the questionnaire addressed the (a) frequency of sports participation in the previous month, (b) frequency of sports participation on average per week (c) average duration of sports activity, (d) the type of sports played, and (e) membership of a sports or fitness club. The variable frequency of sports participation in the previous month was an open-ended question. Strong doubts were raised by the youth professionals about the reliability of the variable frequency of sports participation in the previous month as the youths were often unable to correctly answer this question. This observation led to the decision to drop this variable from the analysis. The variable frequency of sports participation on average per week had five answer categories: ‘once a week’, ‘2 times a week’, ‘3 times a week’, ‘4 times a week’, and ‘5 or more times a week’. The variable average duration of sports activity had five answer categories: ‘less than half an hour’, ‘between an half and 1 hour’, ‘between 1 and 2 hours’, ‘between 2 and 3 hours’, and ‘longer than 3 hours’.

Data analysis

All statistical analyses were carried out using IBM SPSS version 23. The internal consistency of the variables was obtained using Cronbach’s alpha. Mean and standard deviations were inspected, as well as the distribution properties of the variables. The following continues variables were not approximately normally distributed: total SDQ score, pro-social behaviour, effort, and reflection. The data for total SDQ score, pro-social behaviour, and effort were transformed using the square root function, after which the variables were approximately normally distributed. The reflection scale remained not normally distributed and was dropped from the analysis since no reliable outcomes would be obtained from a statistical test. To see whether there were differences between the youths across the four youth organisations, the T 1 variables were compared across the participating youth organisations using ANOVA for the normally distributed variables and using Kruskal-Wallis for the ordinal variables school performance, subjective health and well-being. A paired-samples t-test was conducted to see if the average scores differed between T 1 and T 2 for the continues variables and the Wilcoxon signed-rank test for the ordinal variables.

To examine the relationship between sports participation and the total SDQ score and pro-social behaviour, the self-regulation skills , planning, monitoring and effort, and sense of coherence , we used a repeated measures analysis of variance, where the participants’ age and sex were included as covariates (ANCOVA). The between-subjects factor (i.e., Group factor) in the analysis was based on the variable frequency of sports participation on average per week at T 2 . In order to have relatively equal group sizes, participants were divided in three groups of sports participation: no-sport group, moderate-sport group (1 or 2 times a week), high-sport group (3 or more times a week). For all variables, all assumptions for conducting repeated measures ANCOVA were met: no outliers were detected, there was homogeneity of variance (as assessed by Levene’s test), and homogeneity of covariances (as assessed by Box’s test). Eta squared is reported for all the continues variables as a measure of effect size.

For the ordinal variables, school performance , subjective health , and well-being , a Mantel-Haenszel test of trend was run to determine whether a linear association existed between the variables and the frequency of sports participation (i.e., the three groups of sports participation). The three groups of sports participation at T 1 served as the between-subjects factor in the analysis for the T 1 variables and the three groups of sports participation at T 2 served as the between-subjects factor for the T 2 variables. Following the analysis of main group differences, for the ordinal variables school performance , subjective health , and well-being, we calculated a change score indicating a negative development (− 1), no change (0), or a positive development (1). We used the Mantel-Haenszel test of trend to see whether the change scores differed across the three groups of sports participation at T 2 .

Table  1 shows the participants’ characteristics at T 1 and T 2 . Seventy percent of the youths participated in a sport in the previous month at T 1 and at T 2 . At T 1 , the most popular sports were soccer, fitness, swimming, and boxing. Of the 187 youths that completed both questionnaires, 37 youths did not participate in a sport (19.9%), 15 youths started to participate in a sport (8.1%), 20 youths stopped participating in a sport (10.8%) and 114 youths continued participating in a sport (61.3%). Sixty-seven percent of the youths remained in the same sports-group (i.e., no-sport, moderate-sport, and high-sport) between T 1 and T 2 . Of the youths that participated in a sport at T 2 , 42.7% played a sport under supervision of a sports coach or a sports leader. No significant differences were found for the T 1 variables between the four youth organisations. In addition, the paired-samples t-test showed that the average scores on the outcome variables did not differ between T 1 and T 2 ( p  > .29).

The repeated measures ANCOVAs yielded a significant main group effect for pro-social behaviour and sense of coherence (see Table  2 ). A similar trend was observed for the total SDQ score. Post-hoc analysis with Bonferroni correction revealed that, for pro-social behaviour, the high-sport group scored significantly higher than the no-sport group ( p  = .004). For sense of coherence, the moderate-sport group scored significantly higher than the no-sport group ( p  = .001). No significant difference was found for sense of coherence with the high-sport group ( p  = .139). The repeated measures ANCOVA yielded non-significant main effects for Time ( p  > .170) and a non-significant Group x Time interaction effect ( p  > .198) for all the variables. There was a main effect of sex for pro-social behaviour, F (1, 175) = 4.713, p  = .031, ɳ 2  = .026, and effort, F  = (1, 129) = 4.490, p  = .036, ɳ 2  = .034, where girls scored higher than boys on both pro-social behaviour and effort. In addition, there was a main effect of age for planning, F (1, 128) = 6.036, p  = .015, ɳ 2  = .045, and monitoring F (1, 127) = 7.522, p  = .007, ɳ 2  = .056, where older youths scored higher on both self-regulatory skills.

For the ordinal variables (i.e., school performance, subjective health, and well-being) at T 1 , the Mantel-Haenszel test of trend showed a statistically significant linear association between the groups of sports participation and school performance χ 2 (1) = 9.054, p  = .003, r  = .22, subjective health χ 2 (1) = 12.988, p  < .001, r  = .27 and, well-being χ 2 (1) = 12.340, p  < .001, r  = .26. Higher frequency of sports participation was associated with higher scores on school performance, subjective health, and well-being. At T 2 , the Mantel-Haenszel test of trend showed a statistically significant linear association between the groups of sports participation and subjective health χ 2 (1) = 15.649, p  < .001, r  = .29 and well-being χ 2 (1) = 6.145, p  = .013, r  = .18, but not with school performance χ 2 (1) = 0.365, p  = .546, r  = .04. Higher frequency of sports participation was associated with higher scores on subjective health and well-being.

The Mantel-Haenszel test of trend showed a statistically significant linear association between the groups of sports participation at T 2 and the change score of school performance χ 2 (1) = 5.316, p  = .021, r  = .17. There were no significant associations between the groups of sports participation at T 2 and the change scores of subjective health and well-being.

The aim of this article was to examine the relationship between sports participation and youth development outcomes in a Dutch socially vulnerable youth group. Moreover, we examined the stability of this relationship within a 6-month interval. We found that 70% of the socially vulnerable youth participated in sport at least once a week in the month prior to the questionnaire, at both measurements. In addition, almost two thirds of the youths kept on playing a sport in the six months between the two questionnaires. We found a positive relationship between sports participation and pro-social behaviour, subjective health, well-being, and sense of coherence. These findings proved to be stable across the two measurements. We found no evidence for the relationship between sports participation and total SDQ score (i.e., problem behaviour) and the self-regulatory skills. In addition, sports participation was only positively related to school performance at the first, but not at the second, measurement.

Contrary to our expectations [ 24 ], we found no evidence for the positive relationship between sports participation and the self-regulatory skills planning, monitoring and effort. An explanation for the absence of a positive relationship between sports participation and the self-regulatory skills can be grounded in the discussion whether self-regulatory skills are domain-general skills or domain-specific skills. Several authors have suggested that self-regulatory skills are domain-general skills that are relevant for several performance domains [ 22 ]. In other words, self-regulatory skills such as planning and effort can be used in various life domains interchangeably, such as in the sports setting or in the school setting. However, other researchers have found contradicting results suggesting that metacognitive skills, such as the self-regulatory skills, are domain-specific [ 42 ]. This means that young people may report high scores on the self-regulatory skills planning and effort within the sports setting, but at the same time report low scores on these skills in other life domains. The Self-Regulation of Learning Self-Report Scale, included in this study, measured domain-general skills. As the questionnaires were mostly administered in classroom settings, it is possible that youths reflected on their skills in relation to their school performance. This may explain why we did not find a relationship with sports participation. More research is needed to understand the relationship between sports participation and self-regulatory skills among socially vulnerable youth.

In this current study we found that sports participation was positively related to sense of coherence. Sense of coherence reflects a person’s ability to cope with stressful events in a health-promoting way [ 27 , 28 ]. As socially vulnerable youth are confronted with stressors on a daily basis, a stronger sense of coherence may be an important factor in determining the youths ability to deal with these stressors and, subsequently, increasing the chance that they are able to participate in sport. The other way around, the sports setting may be a setting in which socially vulnerable youth have life experiences that are known to be conducive to the strengthening of sense of coherence: consistency, load-balance, and socially-valued decision making. García-Moya et al. [ 43 ] examined the contextual factors contributing to the development of sense of coherence in children aged 13 to 18 years. The most important predictor of sense of coherence was the quality of parent–child relationships, but other contexts (i.e., the school, the neighbourhood, and peer relations) also remained important in predicting sense of coherence. Consequently, the authors [ 43 ] concluded that “ contextual factors seemed to predominantly act in an additive fashion ” (p. 919) suggesting that the sports setting could aid in strengthening the sense of coherence next to other important life domains. Further research on the development of sense of coherence, specifically within the sports setting, may be especially interesting because sense of coherence reflects a life orientation that can be used throughout the life-course, in different settings and situations [ 28 , 44 ]. People with a strong sense of coherence are better able to use the resources they have available to deal with everyday life challenges. Therefore, the influence of the availability of assets (e.g., self-regulation skills) on individuals’ healthy development may depend on the level of sense of coherence. It would, therefore, be interesting to investigate whether young people with a relatively strong sense of coherence are better able than young people with a relatively weak sense of coherence to transfer life skills from the sports setting to other life domains.

The findings in this study partially corroborate existing evidence on the positive relationship between sports participation and youth developmental outcomes (see for an overview for example: [ 3 , 4 , 45 ]). It is important to note that research has pointed out that reciprocal relationships exist between sports participation and the outcomes that were measured in this study. For example, it has been demonstrated that behavioural problems can be a barrier to sports participation [ 46 , 47 ], suggesting that behavioural problems predict sports participation rates, as well as the other way around. Similarly, in a large German cohort study, Manz et al. [ 46 ] found that having psychopathological problems (measured with the Strengths and Difficulties Questionnaire) was a predictor of abstaining from organised sports participation. It was also found that having emotional symptoms correlated with lower levels of physical activity in a cohort study with 10-year-old children [ 47 ]. These findings support the idea that youths’ developmental status may also determine the chance that they participate in sport. It is within this context, that researchers call for inclusive sports activities as a first step in reaching positive youth development, recognising that the youths’ developmental status is also influential in the youths’ potential to participate in sport [ 48 ].

Sports participation is not a unified concept as it can take many shapes and forms. Coalter [ 49 ] makes a distinction between sport activities, sport-plus activities, and plus-sport activities. Sport activities include both recreational and competitive sport, where the focus lies on playing a sport in the hope that this will lead to changes in youth developmental outcomes [ 49 , 50 ]. Sport-plus activities also focus on sport, but within these activities sport is seen as an important setting for positively influencing youth developmental outcomes. Additional non-sport components are added to the activities that aim to facilitate this change process. For example, each training can be organised around a specific life skill in which certain exercises are included to train the particular life skill. Finally, plus-sport activities focus mainly on youth development and use sport as a vehicle to attract young people and to positively influence youth developmental outcomes. Sport in these plus-sport activities is often broadly defined (e.g., game playing). For youths who are more ‘at risk’, it has been suggested that sports activities should shift more towards plus-sport activities in order to achieve positive outcomes [ 50 ]. The current study focused on the Dutch sports sector, which is organised around national sports federations with members going to local sports clubs. These sports clubs are often run by volunteer sports coaches who receive only limited or no formal coaching training and, hence, there is very little or no attention of the pedagogical aspects of the sports setting [ 51 ]. Acknowledging that intentionally structuring and designing the sports setting to reach positive youth development is important [ 52 ], it is not surprising that we did not observe a change in the different youth development outcomes (except for school performance) across time.

Research has suggested that for the positive development of socially vulnerable youth it is perhaps not the frequency or duration of their sports participation that is of importance, but rather the exposure to a supportive, motivational and pedagogical climate [ 53 , 54 ]. A mastery motivational climate, which focuses on personal effort, improvement and mastery, has been positively linked to enjoyment [ 55 ] and the motivation to continue participating in sport. [ 56 ]. Studies have also supported the observation that the motivational climate is an important predictor of the reported youth developmental outcomes [ 57 , 58 , 59 ]. Even more so, a negative or unbalanced sports climate could harm individual players and potentially push youths further down the spiral of vulnerability [ 60 , 61 ]. To fully understand the relationship between sports participation and youth development, assessing the quality of the sports climate, and thus the quality of the developmental experiences for youths [ 62 ], is necessary.

Strengths and limitations

This study is, to the best of the authors’ knowledge, unique in investigating the association between sports participation and youth developmental outcomes for socially vulnerable youth. First of all, 283 young people participated in the first round of the questionnaire thanks to a strong network of youth organisations involved in the project Youth, Care and Sport. This made it possible to assess the association between sports participation and various indicators of youth development for a large group of vulnerable young people. Secondly, this is the first study to assess the outcomes at two time points, allowing us to examine the stability of the association between sports participation and youth developmental outcomes. And finally, whereas previous studies have often focused on specific sports-based interventions or programs, this study has focused on the traditional sports sector that is dominant in many Western European countries. In this respect, this study has contributed to a number of insights into this rapidly developing area of research.

A recent review of the social and emotional well-being of at-risk youth participating in physical activity programs showed that the risk of bias was high in all the included studies, for example because very few studies included a control group or effect sizes [ 15 ]. This current study was unable to overcome these biases. The original study, as described in the study protocol [ 32 ], had a non-equivalent control group design with an intervention implemented in the experimental condition that aimed to increase the sports participation of socially vulnerable youth. However, due to the changing context in which our research project was conducted, it was no longer possible to implement the intervention. The most important change concerned a political transition in the organisation of the Dutch youth care system during which the responsibility of organising youth care shifted from the youth care organisation with whom we worked to the local government. This transition did not only delay the start of the data collection, but also required the researchers to seek collaboration with a new party (i.e., the local government) that was now responsible for deciding on the content of the youth professionals’ work. This ultimately led to the abolishment of the intervention and the connected non-equivalent control group design. The researchers also encountered several challenges such as building trust with the youth professionals, obtaining parental consent, and attrition rates. The challenges that researchers experience when conducting research in vulnerable groups often disrupt research or prevent it from being conducted [ 63 ]. We have tried to deal with these challenges throughout the project in the best possible way in an attempt to gain valuable data of an under-researched population. Nonetheless, a number of limitations have to be borne in mind concerning the results presented in this paper.

First of all, due to changes in the original study design, this current study did not have an intervention group and a control group. The absence of a (quasi-)experimental design prevents us from drawing conclusions about causal relationships. Following Webb’s [ 64 ] recommendations for further research in the positive youth development area, longitudinal and prospective designs are needed to assess developmental changes through sports participation and “ to analytically separate them from the influences of other social and structural factors on youth development ” (p. 178).

Secondly, we divided participants into three groups based on the average number of times per week they participated in sport. Future research may benefit from a more accurate and precise measurement of sports participation, by also including the intensity of the sports activity. Furthermore, the sample size of this study did not allow us to investigate whether youths that started or stopped participating in sport differed on developmental outcomes from youths that kept on participating or did not participate in sport between the two questionnaires. Future research could investigate how youth developmental outcomes may differ across sports participation patterns, using longitudinal designs.

A third limitation that needs to be considered is the heterogeneity of the sample. All the participants faced, temporarily or over a longer period of time, problems in growing up. However, the degree to which the participants were socially vulnerable might have differed to a large extent. The youth organisations involved in this study offer services to youths with a wide range of problems such as being bullied in school, having autism or ADHD, having parents with drug or alcohol problems, and so forth. For ethical reasons, we were unable to collect any information about the problems that the youths were facing. Yet, the extent to which people experience being socially vulnerable is very relevant for how they experience their participation in sport [ 65 ]. More detailed information about the youths’ problems would have allowed us to investigate whether sports participation could have different outcomes for different groups of vulnerable youths. The lack of these insights makes it unrealistic to make generalisations about the positive associations between sports participation and the youth developmental outcomes for socially vulnerable youth. Moreover, a large proportion of this study’s participants were boys. Although boys are over-represented in the Dutch youth care system [ 66 ] – in 2015, 58.5% of all youths receiving youth care were boys – this does limit our ability to generalise the findings of the current study to all socially vulnerable youth and to socially vulnerable girls specifically.

This study did not take into account other extracurricular activities in which the youths may have been involved in addition or alternatively to their participation in sport. A study by Larson et al. [ 67 ] demonstrated that different organised activities have a very distinct profile of developmental experiences. Community-oriented activities, for example, scored high on developmental experiences related to adult networks and social capital. Similarly, performance and fine arts activities scored high on developmental experiences related to initiative. Future research could include a broad set of extracurricular activities to see how sports participation and other extracurricular activities relate to a healthy development among socially vulnerable youth.

This study investigated the relationship between sports participation and youth developmental outcomes in a Dutch socially vulnerable youth population and examined the stability of this relationship with a 6-month interval. We found a positive relationship between sports participation and pro-social behaviour, subjective health, well-being, and sense of coherence. These findings were stable across the two measurements. We found no evidence for the relationship between sports participation and total SDQ score (i.e., problem behaviour) and self-regulatory skills. In addition, sports participation was only positively related to school performance at the first, but not at the second, measurement. Based on the current data no conclusions can be drawn about the causal relationship between sports participation and youth developmental outcomes. Given the focus of policymakers and health professionals on sport as a means to achieve wider social and educational outcomes for young people, including in the Netherlands, further research is needed to shed light on the relationship between sports participation and youth developmental outcomes for socially vulnerable youth. Future research needs to focus specifically on the heterogeneity of the socially vulnerable youth group and the role of a motivational sport climate in achieving positive development outcomes.

Abbreviations

Strengths and Difficulties Questionnaire

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The study is funded by NWO, the Dutch Organisation for Scientific Research (project number: 328–98-007).

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SS, NH, KV and MK were involved in the design of the study. NH and SS contributed to the data collection for this study. SS and KV conducted the statistical analysis. SS wrote the first draft of the manuscript, after NH, KV and MK read and contributed to the revision of the manuscript. All authors read and approved the final manuscript.

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Super, S., Hermens, N., Verkooijen, K. et al. Examining the relationship between sports participation and youth developmental outcomes for socially vulnerable youth. BMC Public Health 18 , 1012 (2018). https://doi.org/10.1186/s12889-018-5955-y

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