systematic review of research on fair play and sporting competition

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Fair play in a context of physical education and sports behaviours.

1. Introduction

2. materials and methods, 2.1. participants and procedure, 2.2. fair play moral dimensions concept scale (fpmdcs), 2.3. my physical education class (mpec), 2.4. ethics, 2.5. statistic, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Ludwiczak, M.; Bronikowska, M. Fair Play in a Context of Physical Education and Sports Behaviours. Int. J. Environ. Res. Public Health 2022 , 19 , 2452. https://doi.org/10.3390/ijerph19042452

Ludwiczak M, Bronikowska M. Fair Play in a Context of Physical Education and Sports Behaviours. International Journal of Environmental Research and Public Health . 2022; 19(4):2452. https://doi.org/10.3390/ijerph19042452

Ludwiczak, Mateusz, and Małgorzata Bronikowska. 2022. "Fair Play in a Context of Physical Education and Sports Behaviours" International Journal of Environmental Research and Public Health 19, no. 4: 2452. https://doi.org/10.3390/ijerph19042452

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• Published: 18 March 2022

Fair play? Participation equity in organised sport and physical activity among children and adolescents in high income countries: a systematic review and meta-analysis

• Katherine B. Owen 1 , 2 ,
• Tracy Nau 2 , 3 ,
• Lindsey J. Reece 1 , 2 ,
• William Bellew 1 , 2 , 3 ,
• Catriona Rose 1 , 2 ,
• Adrian Bauman 1 , 2 , 3 ,
• Nicole K. Halim 4 &
• Ben J. Smith 2 , 3  

International Journal of Behavioral Nutrition and Physical Activity volume  19 , Article number:  27 ( 2022 ) Cite this article

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This article has been updated

Physical activity and sport have numerous health benefits and participation is thought to be lower in disadvantaged children and adolescents. However, evidence for the disparity in physical activity is inconsistent, has not been reviewed recently, and for sport has never been synthesised. Our aim was to systematically review, and combine via meta-analyses, evidence of the socioeconomic disparities in physical activity and sport participation in children and adolescents in high income countries.

We conducted searches of five electronic databases using physical activity, sport, and socioeconomic disparity related terms. Two independent reviewers assessed 21,342 articles for peer-reviewed original research, published in English that assessed socioeconomic disparities in physical activity and sport participation in children and adolescents. We combined evidence from eligible studies using a structural equation modelling approach to multilevel meta-analysis.

From the 104 eligible studies, we meta-analysed 163 effect sizes. Overall, children and adolescents living in higher socioeconomic status households were more likely to participate in sport (OR: 1.87, 95% CIs 1.38, 2.36) and participated for a longer duration ( d  = 0.24, 95% CIs 0.12, 0.35). The socioeconomic disparity in the duration of sport participation was greater in children ( d  = 0.28, 95% CIs 0.15, 0.41) compared with adolescents ( d  = 0.13, 95% CIs − 0.03, 0.30).

Overall, children and adolescents living in higher socioeconomic status households were more likely to meet physical activity guidelines (OR: 1.21, 95% CIs 1.09, 1.33) and participated for a longer duration ( d  = 0.08, 95% CIs 0.02, 0.14). The socioeconomic disparity in the duration of total physical activity between low and high socioeconomic status households was greater in children ( d  = 0.13, 95% CIs 0.04, 0.21) compared with adolescents ( d  = 0.05, 95% CIs − 0.05, 0.15).

There was no significant disparity in leisure time physical activity ( d  = 0.13, 95% CIs − 0.06, 0.32).

Conclusions

There was evidence of socioeconomic disparities in sport participation and total physical activity participation among children and adolescents. Socioeconomic differences were greater in sport compared to total physical activity and greater in children compared with adolescents. These findings highlight the importance of targeting sport programs according to socio-economic gradients, to reduce inequities in access and opportunity to organised sport.

Physically active lifestyles during childhood and adolescence are associated with a wide range of physical, mental and social benefits; these include improved physical fitness, cardiometabolic health, bone health, cognitive outcomes (e.g., academic performance), mental health (e.g., reduced symptoms of depression); and social benefits (e.g., improved self-esteem) [ 1 ]. Current evidence suggests that many of these benefits carry forward into adulthood [ 1 , 2 ]. Despite the known benefits of physical activity, over 80% of adolescents do not meet the current recommendations for daily physical activity [ 3 ].

There is some evidence that socioeconomic status (SES) is associated with physical activity, in that people of high SES are more physically active than those of lower SES. Stalsberg and Pedersen [ 4 ] systematically combined evidence from 62 studies, published up to July 2009, that assessed the association between SES and physical activity in adolescents. The authors concluded that there was an association, and that adolescents with higher SES were more active than those of lower SES. However, results of individual studies were inconsistent, with 42% of studies reporting no or an opposite relation. Sallis, Prochaska [ 5 ] reviewed 54 studies on correlates of children’s physical activity and reported that parental SES and children’s physical activity were not associated in most studies. Similarly, Ferreira, Van Der Horst [ 6 ] conducted a large review on environmental correlates of physical activity, including socioeconomic status, among children and reported inconsistent findings. One possible explanation for these inconsistent findings is that these reviews combined studies assessing physical activity across all domains (i.e., total physical activity, leisure time physical activity, and sport).

To better understand the socioeconomic disparity in physical activity, we need to explore the disparity across different domains of physical activity. There is some evidence to suggest that children and adolescents from lower SES families participate in higher levels of leisure time activities, such as active play and walking, compared with children and adolescents from higher SES families (e.g., [ 7 , 8 ]). This could be due to different facilitators and barriers experienced across children and adolescents in different SES groups [ 9 ]. For example, children and adolescents from high SES families might experience parental encouragement or pressure to prioritise academic tasks, rather than leisure activities. There is also some evidence to suggest that children and adolescents from lower SES families are less likely to participate in organised sport, compared with children and adolescents from high SES families (e.g., [ 10 ]). Children and adolescents from low SES families may face additional barriers to structured sports, such as the associated financial costs (e.g., registration fees and uniforms), transportation issues, and limited or poor availability of quality facilities and activities in the local neighbourhood and at school [ 9 ]. Children and adolescents from high and low SES families experience different barriers across different domains of PA, which may contribute to differing socioeconomic disparities across different physical activity domains.

Equity across the life course is a fundamental guiding principle in the World Health Organization’s Global Action Plan on Physical Activity (GAPPA), requiring countries to prioritise addressing disparities and reducing inequalities in their implementation of the action plan to achieve the proposed 15% reduction in physical inactivity in adolescents (and adults) by 2030 [ 11 ]. To address these disparities, we need a comprehensive understanding of the disparities across physical activity domains. This review aims to provide an up-to-date synthesis of studies concerning socioeconomic differences in physical activity and organised sports participation among children and adolescents in high income countries. The rationale for this review, and the case for a value-adding contribution is as follows: (a) the need to examine and compare differentials in participation in organised sport as a distinct component of physical activity and (b) the equivocal or conflicting results of studies conducted since 2010.

This systematic review and meta-analysis was registered at the Research Registry (ID: reviewregistry1147) and guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement [ 12 ].

Eligibility criteria

To be included in this review, studies were required to:

Examine children or adolescents (i.e., age range or mean age between 4 and 17 or enrolled in school).

Not be limited to selected sub-groups (e.g., those with a medical condition, only overweight or obese, specific cultural or ethnic group).

Quantitively assess sport participation, leisure time physical activity or total physical activity. Sport was defined as a structured activity through an organisation such as a club or school and leisure time physical activity was defined as any unstructured physical activity outside of school hours.

In the case of leisure time and total physical activity, use population sampling at the first or second (depending on the country) subnational administrative level of the country. Due to the limited available data, this criterion was not applied to studies investigating organised sport participation.

Use a quantitative measure of socioeconomic status (i.e., composite measure such as the Family Affluence Scale, household or parental income, parental education, neighbourhood socioeconomic status).

Quantitatively assess socioeconomic differences in sport, leisure time physical activity or total physical activity.

Use a cross-sectional, cohort or experimental (randomised controlled trials and quasi-experimental study design.

Be conducted in one or more of the following countries: Australia, New Zealand, Canada, USA, UK, Switzerland, and member countries of the European Economic Area (EEA). Multi-country studies involving other countries, were eligible if they reported relevant data for the included countries. We recognise that there are also socioeconomic disparities in middle and low income countries [ 3 ], however there is evidence that physical inactivity is higher in high income countries and that the nature and scale of economic and social inequalities differ in high-, middle- and low-income countries [ 13 ]. For example, high-, middle- and low-income countries have different cultures of sport and non-organised physical activity and therefore, different barriers, correlates, and determinants, as well as a different distribution of SES. Further, there are limited data on sports participation available for low- and middle- income countries.

Provide the full-text version in the English language.

Be published between January 2010 and 15 July 2020.

Information sources

Searches were conducted within Scopus, SportDiscus, PubMed, Medline and APA Psych Info in July 2020. Combinations of keywords were used to identify eligible studies.

Search strategy

The search strategy combined terms relating to sport or physical activity, equity, and country limits. We developed the search strategy and validated it by testing whether it identified known relevant studies (e.g., [ 14 , 15 ]. The full search strategy is presented in Supplementary Table  1 .

Selection processes

First, two researchers independently screened titles and abstracts for eligibility. Next, relevant full texts were retrieved and independently screened by two researchers. All discrepancies regarding inclusion criteria fulfillment were resolved by a third researcher.

Data collection processes

Two researchers independently extracted data from eligible studies using a standardised extraction form. When the relevant data was not reported in the study, we contacted the corresponding author and requested the additional information.

Extracted data included study characteristics (authors, year of publication, year of data collection, country in which the study was conducted), methods (study design, sample size, gender of participants), measurement (measure of sport, leisure time physical activity or total physical activity) and results (unadjusted and adjusted statistical results that examined the socioeconomic differences in sport, leisure time physical activity or total physical activity). In experimental and longitudinal studies with multiple timepoints, data was extracted from the first timepoint.

Study risk of bias assessment

Risk of bias within studies was assessed using the Joanna Briggs Institute (JBI) critical appraisal instruments for studies reporting prevalence data, analytical cross-sectional studies, and cohort studies [ 16 ]. Two reviewers independently assessed each study, and any discrepancies were resolved by discussion between the two researchers or the consultation of a third reviewer.

Effect measures

Commonly reported summary measures included means with standard deviations, standardised mean differences, regression coefficients, and odds ratios. All summary measures that assessed binary outcomes (i.e., participation in sport or meeting physical activity guidelines) were converted to odds ratios (comparing the lowest SES group with the highest). All summary measures that assessed continuous outcomes (i.e., duration of sport or physical activity participation) were converted to standardised mean differences (i.e., Cohen’s d; comparing the lowest SES group with the highest). Effect sizes were defined as small (OR = 1.68; d = 0.20), medium (OR = 3.47; d = 0.50), and large (OR = 6.71; d = 0.80) [ 17 , 18 ]. There were 8 studies that did not provide the required information to convert the summary measure to an odds ratio or Cohen’s d. We contacted the 8 corresponding authors of these studies and 4 authors provided the additional information and so these studies were included in the meta-analyses. The other 4 studies could not be included in the meta-analyses.

Synthesis methods

Typically, researchers have conducted meta-analyses using fixed-effects and random-effects models. However, these models are both limited by the assumption of independence, which means that only one effect size can be included per study [ 19 ]. To avoid violating the assumption of independence, researchers will a) average the effect sizes, b) “shift the unit of analysis” (i.e., retaining as many effect sizes as possible from each study while holding violations of the assumption of independence to a minimum), c) select one of the effect sizes or use a combination of the previously mentioned methods, or d) not report how the issue was handled [ 20 ]. These methods lose information and limit the research questions that can be answered and the ability to test moderators [ 21 ].

Two approaches to meta-analysis that are not limited by the assumption of independence are multilevel meta-analysis and structural equation modelling [ 22 , 23 ]. These two approaches can be integrated to provide further methodological advantages [ 21 ]. The structural equation modelling approach to multilevel meta-analysis enables flexible constraints on parameters, constructs more accurate likelihood-based confidence intervals, and handles missing covariate data using full information maximum likelihood [ 21 ]. We took a structural equation modelling approach to multilevel meta-analysis. Unconditional mixed-effects models using maximum likelihood estimation were conducted to calculate the overall pooled effect sizes (pooled odds ratios and Cohen’s d’s). For each pooled effect size, 95% likelihood-based confidence intervals were calculated. All analyses were conducted using the metaSEM package [ 24 ] in R Version 4.1.1.

The I 2 statistic was used to measure heterogeneity (i.e., variability in the effect sizes) [ 25 ]. An I 2 statistic between 0 and 40% might not be important, 30 to 60% might represent moderate heterogeneity, 50 to 90% might represent substantial heterogeneity, and 75 to 100% considerable heterogeneity. These intervals overlap and so interpretations should depend on the magnitude and direction of the effect and the strength of the evidence for heterogeneity [ 25 ]. Heterogeneity can be examined and explained using moderator analyses.

We tested whether age moderated the socioeconomic differences in sport, leisure time physical activity and total physical activity. As sport dropout is highest during adolescence [ 26 ] and physical activity has the steepest decline during adolescence [ 27 ], we compared the socioeconomic differences in children (under 13) and adolescents (age 13 and above) [ 28 , 29 ]. R 2 was used to examine the proportion of variance explained by including age as a moderating variable.

We conducted a sensitivity analysis excluding studies that did not adjust for confounders to assess the role and extent of confounding [ 30 ].

Reporting bias assessment

To examine reporting bias, we used funnel plots [ 31 ] and Egger’s regression asymmetry tests [ 32 ]. Funnel plots plotted the effect sizes on the x-axes and standard errors on the y-axes and resemble a symmetrical inverted funnel when there is no reporting bias. Egger’s regression asymmetry tests regress the normalized effect estimate (effect size divided by its standard error) against precision (reciprocal of the standard error of the effect size) and when the regression line runs through the origin, there is no reporting bias.

Certainty assessment

The certainty of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation approach [ 33 ]. Two researchers qualitatively assessed risk of bias, consistency, and precision and gave a summary rating – high, moderate, or low certainty of evidence.

Study selection

Study selection results are presented in Fig.  1 (flow diagram). Through searches of electronic databases, we identified 21,342 non-duplicate records. After reviewing titles and abstracts, we obtained and reviewed full-text versions for 424 potentially relevant records. Of these 424 full text articles, 104 met the inclusion criteria. However, four of these did not provide enough information to be included in the meta-analyses.

PRISMA flow diagram for study inclusion

We excluded 320 articles in the full-text review phase for the reasons identified in the PRISMA flow diagram (Fig. 1 ). These reasons included ineligible age (e.g. [ 34 ] was excluded because the mean age of the children in the analysis sample was 3.5 years); sample consisting of a ‘special population’ (e.g. [ 35 ] recruited student athletes); not reporting eligible physical activity or sport outcomes (e.g. the outcome reported in [ 36 ] was whether the person lived in a supportive neighbourhood for physical activity); not analysing the physical activity or sport outcome according to an eligible socioeconomic measure (e.g. [ 37 ] assessed physical activity according to weekly spending money). Any physical activity studies that did not use population sampling were excluded (or else only had their sport outcomes extracted) (e.g. [ 38 ] used convenience sampling to select high schools from Aveiro, a relatively small city and municipality in Portugal).

Study characteristics

Study characteristics are detailed in Additional file 3 . Of 104 included studies, 55% (k = 57) were published between 2010 and 2015 and 45% [ 39 ] were published between 2016 and 2020. Studies were conducted in Europe (k = 64 [Scandinavia k = 14; United Kingdom k = 12; other k = 38]), United States (k = 21), Australia or New Zealand (k = 12), and Canada (k = 7).

Across the 104 studies, there were 1,373,580 children and adolescents included. The number of study participants ranged from 200 [ 40 ] to 671,375 [ 41 ]. The mean age of study participants ranged from 4.7 years (SD = 0.9 [ 42 ];) to 17.0 years (SD = 0.9 [ 43 ];).

The majority of studies measured total weekly physical activity (k = 63), followed by sport (k = 40) and leisure time physical activity (k = 13; [12 studies assessed multiple outcomes]). Of the 63 studies that examined total physical activity, 18 studies used objective measures (accelerometers), and the remaining 45 used parent or self-report questionnaires. All studies assessing leisure time physical activity and sport used parent or self-report questionnaires.

Studies assessed socioeconomic status using income (k = 42), parental education (k = 26), a composite measure (e.g., Family Affluence Scale; k = 17), an area level indicator (e.g., Socioeconomic Indexes for Areas; k = 16) and eligibility for free lunch at school (k = 3).

Risk of bias in studies

Complete risk of bias assessments are displayed in Additional file 4 . The interrater agreement for risk of bias ratings was 76%, and all discrepancies were resolved by discussion between two researchers, or the consultation of a third reviewer where appropriate. Studies assessing sport participation met between 18 and 100% of risk of bias items (Mean = 66%). Studies assessing leisure time physical activity met between 62 and 91% of items (Mean = 73%) and studies assessing total physical activity met between 27 and 100% of items (Mean = 79%). Overall, the criteria that were least likely to be met were conducting the data analysis with sufficient coverage of the identified sample (k = 36 met this criteria) and measuring the outcome in a valid and reliable way (k = 57 met this criteria).

Results of syntheses

Overall, children and adolescents living in higher socioeconomic status households were 1.87 times more likely to participate in sport (OR: 1.87, 95% CIs 1.38, 2.36, moderate certainty evidence; Table 1 ). Similarly, children and adolescents living in higher socioeconomic status households spent more time participating in sport ( d  = 0.24, 95% CIs 0.12, 0.35, low certainty evidence). For these pooled effect sizes, there was considerable heterogeneity between studies ( I 2  = 0.84 and 0.90, respectively) and negligible heterogeneity within studies ( I 2  = 0.15 and 0.10, respectively).

Sport across age groups

Age explained a small portion of the heterogeneity found within studies that examined the socioeconomic differences in sport participation ( R 2  = 0.02). Children living in higher socioeconomic status households were 2.03 times more likely to participate in sport (OR: 2.03, 95% CIs 1.41, 2.65), and adolescents living in higher socioeconomic status households were 1.84 times more likely to participate in sport (OR: 1.84, 95% CIs 1.14, 2.55).

Duration of sport participation was also moderated by age ( R 2  = 0.05). There was a small to moderate difference in the duration of sport participation between children living in low and high socioeconomic status households ( d  = 0.28, 95% CIs 0.15, 0.41). Whereas there was a small non-significant difference in the duration of sport participation between adolescents living in low and high socioeconomic status households ( d  = 0.13, 95% CIs − 0.03, 0.30).

Total physical activity

Children and adolescents living in higher socioeconomic status households were 1.21 times more likely to meet physical activity guidelines (OR: 1.21, 95% CIs 1.09, 1.33, high certainty evidence). For this pooled effect, there was moderate heterogeneity between studies ( I 2  = 0.34) and within studies ( I 2  = 0.56). Children and adolescents living in higher socioeconomic status households spent more time participating in physical activity (d = 0.08, 95% CIs 0.02, 0.14, moderate certainty evidence). For this pooled effect, there was substantial heterogeneity between studies ( I 2  = 0.67) and negligible heterogeneity within studies ( I 2  = 0.26).

Total physical activity across age groups

Differences between children and adolescents living in low and high socioeconomic households meeting physical activity guidelines was not moderated by age ( R 2  = 0.00).

Age explained a small portion of the heterogeneity found within studies that examined the socioeconomic differences in duration of total physical activity ( R 2  = 0.02). There was a small difference in the duration of total physical activity between low and high socioeconomic status households in children ( d  = 0.13, 95% CIs 0.04, 0.21), but not adolescents ( d  = 0.05, 95% CIs − 0.05, 0.15).

Leisure time physical activity

Children and adolescents living in higher socioeconomic status households spent more time participating in leisure time physical activity ( d  = 0.13, 95% CIs − 0.06, 0.32, low certainty evidence); however, the confidence intervals crossed zero. For this pooled effect size, there was considerable heterogeneity between studies ( I 2  = 0.70) and negligible heterogeneity within studies ( I 2  = 0.30).

Sensitivity analyses

Supplementary Table  2 presents the pooled effects of studies examining socioeconomic differences in sport and physical activity, excluding studies that did not adjust for confounders. There were no appreciable differences when excluding these studies.

Reporting biases

Funnel plots for studies examining socioeconomic differences in sport participation and duration of sport participation revealed low asymmetry, representing a low risk of bias across studies (Figs.  2 , 3 , 4 , 5 and 6 ). This was confirmed by non-significant Egger’s test results (z = 1.34, p  = 0.18 and z = − 0.80, p  = 0.42, respectively). Similarly, funnel plots for studies examining socioeconomic differences in duration of leisure time physical activity participation revealed low asymmetry, representing a low risk of bias across studies, and this was confirmed by non-significant Egger’s test results (z = − 0.50, p  = 0.62).

Funnel plot for sport participation

Funnel plot for sport duration

Funnel plot for meeting physical activity guidelines

Funnel plot for total physical activity duration

Funnel plot for leisure time physical activity duration

There was some evidence of risk of bias across studies that examined socioeconomic differences in meeting physical activity guidelines and duration of total physical activity participation. Funnel plots for studies examining socioeconomic differences in meeting physical activity guidelines and duration of total physical activity participation revealed moderate asymmetry, representing some risk of bias across studies. This was confirmed by significant Egger’s test results (z = − 2.63, p  = 0.01 and z = − 2.67, p  = 0.01, respectively).

Certainty of evidence

The certainty of evidence for the socioeconomic differences in sport, leisure time physical activity and total physical activity are displayed in Table  2 .

Since the late 1970s, equity in the context of health has become a central objective for the World Health Organization (WHO), largely attributed to the Alma-Ata Declaration of 1978, which emphasised the unacceptable nature of gross global health inequality and called for health for all by the year 2000 [ 44 ]. A little over a decade after the declaration, the WHO commissioned a definition of inequity that has come to be widely cited globally: “differences which are unnecessary and avoidable, but in addition, are considered unfair and unjust” [ 45 , 46 ]. In 2021, WHO published an advocacy brief calling for stronger multisectoral action to address inequities in access and opportunities for physical activity [ 47 ]. Our systematic review and meta-analysis is the first to integrate evidence of the socioeconomic disparity in sport participation in children and adolescents. It is also the most recent and largest systematic review and meta-analysis of the socioeconomic disparity in physical activity. This included 104 studies and 126 effect sizes, with results showing small socioeconomic disparities (i.e., children and adolescents from high SES families are more active) in both sport (low- to moderate-certainty evidence) and total physical activity (moderate- to high-certainty evidence), but not leisure time physical activity (low-certainty evidence). Socioeconomic differences appear to be greater in sport compared to total physical activity or leisure time physical activity, and greater in children compared with adolescents.

While our overall pooled effects suggested inequities in sport and physical activity, there was no significant disparity in leisure time physical activity. It is important to note that only seven of the included studies assessed leisure time physical activity and these studies included a broad range of ages, introducing a high level of heterogeneity. Inconsistent findings of socioeconomic disparities were also found in a recent umbrella review of socioeconomic determinants of physical activity across the life course [ 39 ]. Some reviews identified socioeconomic disparities in physical activity (e.g., [ 48 ]), while others did not (e.g., [ 6 ]). One of the reasons could be that different types of physical activity and sport show unique and distinct socioeconomic disparities [ 15 ]. For example, the disparity tends to be greater in niche activities such as canoeing and rock climbing, compared to more mainstream activities such as cricket and netball, but may also depend on whether they occur in structured or unstructured settings. Children and adolescents from high and low SES families may also experience different barriers and facilitators across different domains of physical activity [ 9 ]. These inconsistencies could also be explained by unassessed confounders, such as culture, social organisation, geographic location, and factors beyond the scope of this review. For example, different activity preferences and participation patterns vary across different geographic regions. Hulteen [ 49 ] found that young people in the Americas (Canada, Jamaica, United States, Brazil) prefer team sports which may be associated with higher participation costs (e.g., Lacrosse), whereas those from the Western Pacific (Australia, China, Japan, Hong Kong) prefer physical activities including many that can be undertaken at little to no cost (e.g., running and walking). Further, each country has their own distinct school system with their own specific curriculum requirements and extracurricular sporting opportunities.

The socioeconomic disparities identified in this review were greatest in sports participation. This can be explained by a combination of individual (e.g., self-efficacy, negative outcome expectations), household (material or social deprivation) and neighbourhood (e.g., access and proximity to facilities) factors [ 14 , 15 ]. Cost is a barrier that is greater for sport participation. Sport has several additional costs such as registration, uniform, travel, and equipment, which can present greater barriers for children and adolescents from disadvantaged backgrounds. This cost barrier to sport has been addressed through financial incentive programs across the world [ 50 ] which have shown some promising findings (e.g., [ 51 ]). However, there are socioeconomic disparities in awareness and engagement in these programs and further targeted work is needed [ 52 ].

It is important that the socioeconomic disparities in sport be reduced. The United Nations has identified sport as an important contributor to sustainable development [ 53 , 54 ]. Sport has economic benefits, providing employment and local development. It can bring individuals and communities together, bridging cultural and ethnic divides. For young people, sport participation can be beneficial for holistic development, physical and emotional health and building valuable social connections [ 55 ]. Sport can also provide a healthy alternative to harmful behaviours such as drug use and crime [ 56 ]. In order to reduce the socioeconomic disparity in sport participation, a systems-based approach is needed that combines upstream policy actions to improve the social, cultural, economic and environmental factors for sport, with downstream actions that focus on the individual [ 11 , 47 ].

Our review found that the socioeconomic disparity was greater in sport compared to physical activity, but this finding is based on evidence that was of low- to moderate certainty. Fewer studies have examined socioeconomic disparities in sport participation and these studies tended to have a higher risk of bias. Of the 40 studies that examined socioeconomic disparities in sport participation, only 5 used a valid or reliable measure of sport and 20 had a representative population sample. We recommend the development of a standardised and validated measure of sport participation that assesses both the frequency and duration of participation. Further high-quality studies with large representative population samples are required that examine the socioeconomic disparity in sport participation.

The socioeconomic disparities in our review were greater for dichotomous variables (i.e., sport participation vs. no participation and meeting physical activity guidelines vs. not meeting guidelines) compared to the continuous variables (i.e., duration of sport and physical activity participation). This suggests that once initial engagement in sport is established, socioeconomic status has less influence on the duration or frequency of participation. This is consistent with a previous study that found that there was a socioeconomic disparity in overall sport participation, but the disparity in regular participation was small [ 15 ]. Population targeted work is needed to establish initial participation in sport for children and adolescents from disadvantaged backgrounds. In relation to physical activity guidelines, the small group of children and adolescents who do meet guidelines (approximately one in five [ 3 , 57 ];) are a select and distinct group. This group tends to have a higher level of advantage [ 58 ] and therefore, differentials could appear greater. Targeted work is needed to enable children and adolescents from disadvantaged backgrounds to meet physical activity guidelines.

The socioeconomic disparities in sport and physical activity were found to be greater in children compared with adolescents. This could be due to parental influence on physical activity and sport participation decreasing with age. As the child grows older, they gain autonomy and independence from their parents and are exposed to new environments and influences [ 39 ]. For example, as adolescents spend less time at home, and more time at school and with peers, the school environment and their peers become more influential in shaping health behaviours [ 39 ]. As such, parental SES (e.g., income or education) have reduced effect upon adolescents physical activity participation. There is evidence to suggest that alternate measures of socioeconomic status such as adolescent’s perception of social status relative to others in their peer group may be a better predictor of their health behaviours compared to the traditional measures [ 59 ]. Future studies should employ alternate measures of social status to further clarify the SES patterns for adolescents’ physical activity and sport participation. Disparities may also be lessened by the trends towards dropout in sport among adolescents [ 26 ] and the steep decline in physical activity across all socioeconomic groups in this age group [ 27 ]. Competing priorities (e.g., academic achievement), and increased responsibilities including schoolwork and employment begin to influence adolescents at this stage and will affect this age group differently across diverse social and cultural contexts internationally.

The differences in participation discussed here surely meet the WHO definition of inequity as “differences which are unnecessary and avoidable, but in addition, are considered unfair and unjust”; these inequities could be reduced by the right mix of government policies [ 60 ]. Progress requires a coordinated and strategic systems approach as outlined in the WHO Global Action Plan on Physical Activity 2018–2030 [ 11 ] and in the 2021 WHO advocacy brief Fair Play [ 47 ] which places particular emphasis on three areas of action (i) innovative and diverse financing mechanisms; (ii) coherent policy, laws, regulatory frameworks, and standards; and (iii) more integrated delivery of physical activity. These three domains indicate the need for progressing a research agenda that can determine whether the state and non-state actors engaged in the delivery of GAPPA have risen to these challenges.

Limitations

A limitation of this study is that there was considerable heterogeneity in the pooled effect sizes. Some of this heterogeneity could be attributed to the variety of socioeconomic status measures (e.g., household income, parental education, area level socioeconomic status) [ 61 ]. Second, there was some publication bias in the meta-analysis of evidence pertaining to the socioeconomic disparity in physical activity. This is expected when conducting searches of published literature [ 62 ]. However, the meta-analysis also included null findings, suggesting that publication bias is likely not severe. Third, our searches may have missed relevant studies as they were limited to full text, conducted in high income and generally Western countries, in the English language between January 2010 and 15 July 2020. Further, the findings of our systematic review and meta-analysis are only relevant to high income Western countries. Further research is needed to investigate the socioeconomic disparities in sport and physical activity in children and adolescents living in low- and middle-income countries. Fourth, when we examined age as a moderator, we divided studies into two categories (children and adolescents). While we used a mean age of 13 as a basis for classification (i.e., younger than 13 years for children, and 13 or older for adolescents), it is likely that studies had participants in both categories. Although this method is limited, it does provide some understanding of how the socioeconomic disparity is different in children and adolescents.

This systematic review and meta-analysis found evidence of small socioeconomic disparities in sport participation and total physical activity participation among children and adolescents in high income countries. Socioeconomic differences were greater in sport compared to total physical activity and greater in children compared with adolescents. These findings highlight the need to integrate an equity focus into programs and policies that are designed to increase sport participation, whilst also addressing inequities in access and opportunities for all children and adolescents to be physically active. Strategies to increase the equitable provision of positive and good quality physical activity and sport opportunities in childhood and adolescence, will help develop and strengthen the health and physical literacy skills needed to promote lifelong participation in sport and physical activity.

Availability of data and materials

The data are available from the corresponding author on request.

Change history

29 april 2022.

In the original publication, the sentence in the Conclusions section of the Abstract consisted of a typo. The article has been updated to rectify the error.

Abbreviations

European Economic Area

Global Action Plan on Physical Activity

Joanna Briggs Institution

Socioeconomic status

World Health Organisation

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Owen, K.B., Nau, T., Reece, L.J. et al. Fair play? Participation equity in organised sport and physical activity among children and adolescents in high income countries: a systematic review and meta-analysis. Int J Behav Nutr Phys Act 19 , 27 (2022). https://doi.org/10.1186/s12966-022-01263-7

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Authors: Serrano-Durá, José 1 ;  Molina, Pere 1 ;  Martínez-Baena, Alejandro 2 ; 

Source: Sport, Education and Society , Volume 26, Number 6, 24 July 2021, pp. 648-662(15)

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Keywords: Sportsmanship ; equality ; ethics ; justice ; sportspersonship

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Affiliations: 1: Facultad de Ciencias de la Actividad Física y del Deporte, Universitat de València, Valencia, Spain 2: Universidad de Granada, Spain

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Moral Identity and Attitudes towards Doping in Sport: Whether Perception of Fair Play Matters

Associated data.

The data presented in this study are available on request from the corresponding author.

Research evidence suggests that athletes’ attitudes towards banned substances are among the strongest predictors of intention to use or actual practice of doping. Previous research has found that personal morality was negatively related to doping attitudes. However, less is known about the role of athletes’ perceptions of fair play on their attitudes towards doping. First, we examined whether moral identity was associated with athletes’ attitudes towards doping and whether their perceptions of fair play mediated this relationship. The second purpose was to determine whether these associations differed among non-athletes. Overall, 365 university students (49.9% males, 55.3% athletes) participated in this study (mean age 22.02, SD = 6.58). They completed questionnaires measuring the aforementioned variables. The results showed that athletes’ moral identity and endorsement of fair play were negatively associated with their attitudes towards doping. The mediation analyses showed that the effect of moral identity on attitudes towards doping was partially mediated by perceptions of fair play (indirect effect, β = −0.10, p < 0.05). Unlike student athletes, non-athletes’ moral identity negatively predicted attitudes towards doping only indirectly, via fair play perception (indirect effect, β = −0.08, p < 0.05). The study provides insights into how a person’s morality and perception of moral values in sport may act as factors related to doping in sport. The practical implications for the promotion of anti-doping attitudes for athletes and separately for student non-athletes were provided together with future research perspectives.

1. Introduction

Sport has to provide opportunities for athletes to compete and thus demonstrate their skills in fair play [ 1 , 2 ]. However, in contemporary highly competitive sport, moral norms are often overlooked for other more selfish interests, such as winning at any cost [ 3 ]. Therefore, some athletes not only try to put effort into improving their mastery in order to succeed, but at the same time take risks by using banned performance-enhancing drugs or methods referred to as doping. Some studies have revealed that up to 57% of elite athletes use doping for performance improvement [ 4 ]. World Anti-Doping Agency doping control tests showed that different groups of banned substances are disproportionately spread in different sports disciplines but doping itself is used in both individual and team sports [ 5 ]. Although efforts are being made to test athletes more, this has not yet yielded significant results [ 6 ]. Therefore, in order to develop and improve doping prevention programs, it is important to have a good understanding of the factors that influence athletes’ choices to use doping in sport [ 7 , 8 ].

Research suggests that various personal and psychosocial factors influence athletes’ choices to use doping [ 9 , 10 ], but the attitudes of athletes play a special role. The evidence suggests that attitudes are reliable predictors of behavior [ 11 ]. A meta-analysis of Ntoumanis et al. [ 10 ] and Blank et al. [ 9 ] found that athletes’ attitudes towards banned substances were among the strongest predictors of intention to use or actual practice of doping. Besides, a recent study by Nicholls et al. [ 12 ] also suggested that athletes’ more positive attitudes towards doping were related to cheating behaviors. Therefore, understanding the factors that influence a person’s attitudes towards doping is important.

To date, much is already known about personal and social contextual and personal factors influencing athletes’ attitudes towards doping. Considering the social context, evidence suggests that the people surrounding the athlete (especially the coach) are important in shaping athletes’ attitudes towards doping. Trust, a respect-based relationship between the coach and athletes [ 13 , 14 ], the coach endorsing anti-doping attitudes [ 14 ] as well as a secure attachment to the coach and teammates influence anti-doping attitudes [ 15 ]. Greater perceived social pressure to engage in doping [ 16 ], athletes’ contacts with doping users also related to more positive attitude towards doping [ 17 ]. Thus, the environments in the team and its standards are of vital importance [ 18 ] likewise sports culture [ 9 ].

However, personal factors are also important. Several studies have revealed positive associations between extrinsic motivation and athletes’ attitudes towards doping [ 17 , 19 , 20 ]. Attitudes towards doping are also related to personality traits. Previous studies found that athletes with extremely high perfectionism levels were more inclined towards doping [ 17 , 21 ]. In addition, it was found that striving for perfectionism negatively predicted, while perfectionism concerns positively predicted athletes’ attitudes towards doping [ 20 , 22 ]. Athletes’ perceptions of competence and success also are important variables. As evidence suggests, task orientation was negatively related, and ego orientation was positively related, to attitudes towards the use of doping [ 18 , 22 ]. It is also important to understand how the essential moral principles or moral values in sport are perceived. How athletes understand and respect the rules, rituals, and traditions of sport and are able to distinguish what is good and bad is associated with the concept of sportsmanship [ 23 ], which is very close to the definition of fair play that represents what is morally right and characterizes good sporting competition [ 24 ]. Some studies showed that sportsmanship orientation is negatively related to the intention to use doping [ 16 ]. So, how a person perceives what is morally right in a sport may reflect certain personal values, and in turn, personal attitudes may reflect the expression of certain values. As previous research has revealed, moral values of athletes in sport activities may be negatively related to their attitudes to deception [ 25 ]. Therefore, the perception of fair play as an expression of what is morally right in sport is an important variable and might affect attitudes to doping in sport, which has so far received insufficient attention in research.

The use of banned performance-enhancing substances in sport is associated with a moral choice, a choice based on principles of right and wrong [ 26 ]. Thus, personal morality, among other factors, is important [ 26 ]. Some scholars indicate that personal morality might be the most important influencing variable in doping attitudes [ 27 , 28 ]. Based on a social cognitive theory [ 29 ], individuals develop moral standards that govern their behaviors. People’s actions depend on their moral standards and therefore we are personally responsible for our actions. Emotions are also a very important factor in regulating moral actions. It has been found that unpleasant emotional consequences such as self-condemnation and guilt can help an athlete refrain from using prohibited substances [ 30 , 31 ]. However, sometimes people behave immorally, violating their personal moral standards without self-sanction via the use of moral disengagement [ 29 ]. It is noteworthy that many studies have been conducted recently that have revealed that moral disengagement is directly or indirectly related to doping attitudes [ 32 , 33 ] and is especially related to the likelihood or intention to use doping [ 34 , 35 , 36 ].

Another important factor related to moral behavior is moral identity. Aquino and Reed [ 37 ] defined moral identity as a self-regulatory mechanism. Specifically, this construct refers to a cognitive schema that people hold about their moral character and reflects the importance that one places on being a moral person [ 37 ]. Moral identity can help to maintain a balance between how we perceive ourselves as a moral self and our actions [ 38 ]. Therefore, a strong moral identity can motivate people to act morally [ 38 ]. In addition, they do not have to activate moral disengagement mechanisms to suppress the effects of negative emotions associated with unethical behavior [ 34 ]. We suppose this construct is also very important as it is beyond sport, i.e., moral identity describes the degree to which being a moral person is central to one’s self-concept, not just a moral person as an athlete [ 37 , 39 ]. However, the ways that the moral identity of athletes is related to doping behavior have been more actively addressed only in recent years. A qualitative study by Erickson et al. [ 13 ] showed that a strong moral stance was important as a protective factor against doping in sport. Other quantitative studies confirmed that moral identity negatively predicted athletes’ doping likelihood [ 34 , 35 , 40 ]. Stanger and Backhouse’s [ 36 ] results also showed that athletes with a stronger moral identity were less likely to use a banned substance even if they were more susceptible to justify doping. These significant studies provided evidence that moral identity was an important factor in analyzing doping issues. Therefore, based on the findings of these studies, it is possible to suppose that athletes with a stronger moral identity may have less-positive attitudes towards doping. However, research has not yet addressed this possibility.

The Present Study

Research evidence suggests that various contextual and personal factors are related to athletes’ attitudes towards doping [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. In this study, we focus more on personal factors, the role of which in doping attitudes is less clear. Specifically, less attention has been paid to the role of moral identity in predicting athletes’ attitudes towards doping. Furthermore, so far, there are contradictory data regarding the perceptions of moral principles in sport as well as values related to the attitudes of athletes towards doping, especially among adult athletes. This is important as it will complement existing data on the importance of moral identity in understanding doping-related behaviors, thus it will also help to better understand the importance of athletes ’perceptions of moral principles in our study—the perception of fair play. Therefore, the purpose of our study was to examine whether moral identity was associated with athletes’ attitudes toward doping and whether perceptions of fair play mediated this relationship. Based on previous findings on the likelihood to use doping [ 34 , 35 , 40 ], we hypothesized that moral identity would be inversely associated with attitudes towards doping. As perceived moral values in sport can negatively predict attitudes on cheating behavior [ 25 ], we also expected that an endorsement of fair play, as an expression of moral principles in sport, would be negatively associated with doping attitudes. Finally, we hypothesized that the perception of fair play would mediate the relationship between moral identity and attitudes towards doping.

Researchers [ 34 ] note that most studies examining the association between moral identity and doping were conducted with British athletes, thus it is relevant to continue such research with participants from other countries. In this sense, our study contributes to this call. However, in our study, we also want to point out that researchers have not examined how moral identity is related to attitudes towards doping in sport among non-athletes, thus whether our hypotheses will also be supported. A better understanding of how doping is perceived and valued not only by athletes but also by people not participating in sport is important for an effective anti-doping strategy [ 41 ]. Not only modern testing tools and financial resources for athletes, but also public support, are important in the fight against doping. Research with adults shows that most of them are against doping in sport [ 41 , 42 ]. Longitudinal observations have also shown that people, like athletes, tend to see doping in sport as a more serious problem [ 41 ]. However, observing the change in students’ attitudes during the study year, it was observed that there was a tendency to support the internationally promoted “zero tolerance” policy less, and students became more tolerant of using doping [ 43 ]. In addition, in the evaluations of doping, there may be a conflict of values where the sports achievements of athletes of a favorite team or country are seen as more important than how fairly they achieved it. People who are very interested in sport have been found to have more liberal attitudes towards doping and related scandals [ 44 ]. In conclusion, research on non-athletes’ attitudes towards doping in sport is also relevant, especially if it is population-based. Our study covers only students and will not be a population-based study of youth attitudes. However, it will provide an answer to the second aim of the study—to establish whether the moral identity of students who do not participate in sports and endorse fair play is associated with attitudes towards doping as they are among student athletes.

2. Materials and Methods

2.1. participants.

Participants in this study were 365 university students (49.9% male) recruited from Universities in Lithuania. At the time of data collection, participants ranged in age from 18 to 30 years (mean age 22.02, SD = 6.58). In the sample, 55.3% (n = 202, 65.3% male) were athletes who competed in individual (n = 118, 50.0% male) and team sports (n = 84). The individual sports included swimming, tennis, table tennis, cycling, athletics, boxing, wrestling, and judo. The team sports included basketball, football, handball, and volleyball. At the time of data collection, participants had competed in their sport for an average of 8.70 (SD = 4.32) years. Among athletes, 32.2% (n = 65) were currently competing or had recently competed at the international level, 46.0% (n = 93) at the national, and 21.8% (n = 44) at the regional or university level.

2.2. Measures

2.2.1. doping attitudes.

The 8-item version of the Performance Enhancement Attitude Scale [ 45 ] was used in this study. The original scale consisted of 17 items [ 8 ], but Nicholls et al. [ 45 ] found a better fit of an 8-item scale within a sample of adult athletes. The Lithuanian version of this scale also resulted in a better fit of an 8-item PEAS [ 46 ]. Therefore, participants were asked to indicate their level of agreement with eight statements (e.g., “Legalizing performance enhancements would be beneficial for sports”, “Athletes should not feel guilty about breaking the rules and taking performance-enhancing drugs”, “Doping is necessary to be competitive”) using a Likert scale, ranging from strongly disagree (1) to strongly agree (6). The mean of the eight item ratings was computed and used as a measure of attitudes to doping. The internal consistency of the scale scores in this study was good (α = 0.81).

2.2.2. Moral Identity

In this study, we used the internalization dimension of the moral identity scale [ 37 ] to measure moral identity. Participants were presented with nine traits (fair, honest, helpful, kind, generous, compassionate, caring, fair, friendly, and hardworking) related to common characteristics of moral individuals and asked to imagine how a person with the given traits would feel, act, and think, and to respond to five statements (e.g., “It would make me feel good to be a person who has these characteristics”, “Being someone who has these characteristics is an important part of who I am”, “I strongly desire to have these characteristics”) on a 7-point Likert scale, ranging from strongly disagree (1) to strongly agree (7). The mean of the five item ratings was used as a measure of moral identity. The internal consistency of the scale scores in this study was good (α = 0.73).

2.2.3. Perception of Fair Play

The perception of fair play was measured using the Fair Play scale [ 47 ]. Participants were given 10 items (e.g., “It is impossible to do well in sports if you play fair”, “You can win playing fair”, “In sports it is acceptable that one tries to bend the rules”). They had to rate each item on a 4-point Likert scale, ranging from “strongly agree” to “strongly disagree”. Some items were rated in reverse scores. Based on Majauskiene’s [ 48 ] study, we used this scale as a unidimensional scale. A higher overall score demonstrated a higher endorsement of fair play. The Internal consistency of the scale scores in this study was good (α = 0.77).

2.3. Procedure

Before the study, approval from the university social research ethics committee was obtained. Oral informed consent was obtained from all research participants. All participants were informed about the research aim, study duration, risk and benefits, and the right to refuse to participate or withdraw from the survey. Students did not have to disclose any personal information (e.g., names, dates of birth, study program, contact details) and were told that all data would be kept anonymous and the information they provided would be used only for research purposes. Those who volunteered to participate were instructed how to complete the measures described above. This whole procedure was performed in a university auditorium at the end of a lecture or a seminar.

2.4. Statistical Analysis

The data obtained through the survey were exported into an SPSS data file and analyzed using the IBM SPSS version 26 package. Before the main statistical analyses, preliminary data screening was conducted in order to check for data normality, missing values, and outliers for each variable. It was found that 0.3% of the data points were missing and were replaced with the mean of the respective variable. Analyses indicated that that skewness and kurtosis for all variables were low (i.e., ≤1). Descriptive statistics (i.e., mean, standard deviation, and Pearson’s correlations) were calculated for all variables. Reliability estimates were calculated for all variables using alpha coefficients. Scores of all variables showed acceptable internal consistency values. Comparisons of study variables between athletes and non-athletes were conducted using one-way ANOVA. Mediation analyses were performed using the PROCESS 2.16 [ 49 ] SPSS macro (model 4) aiming to test direct and indirect effects. Direct effects are the effects of the predictor on the outcome variable that occur separately to the mediator, while indirect effects are the effects of the predictor on the outcome variable via the mediator. Bootstrapping was set at 10,000 samples, and bias-corrected 95% confidence intervals were calculated for all effects. An effect is significant when the CI does not contain zero. The completely standardized indirect effect (CSIE) was reported as the effect size metric and interpreted as 0.01 = small effect, 0.09 = medium effect, and 0.25 = large effect [ 50 ].

3.1. Descriptive Statistics and Correlations

Study results showed that participants could be characterized by a relatively high moral identity, they relatively endorsed fair play, and had negative attitudes to doping in sport ( Table 1 ). Correlations indicated that moral identity was negatively associated with positive attitudes to doping and positively associated with an endorsement of fair play. The fair play variable was also negatively associated with positive attitudes towards doping.

Descriptive statistics and correlations.

Note. ** p < 0.01.

3.2. Comparison between Athletes and Non-Athletes

A one-way ANOVA showed that athletes ( M = 1.53, SD = 0.60), compared to non-athletes ( M = 1.40, SD = 0.46), had significantly more positive attitudes towards doping ( F (1, 363) = 5.32, p < 0.05, partial η 2 = 0.01). However, non-athletes ( M = 3.13, SD = 0.42), compared to athletes ( M = 3.02, SD = 0.38), demonstrated more positive perceptions of fair play ( F (1, 363) = 7,26, p < 0.01, partial η 2 = 0.02). When comparing moral identity, a statistically significant difference was not found ( F (1, 363) = 3,48, p > 0.05).

3.3. Main Analysis

First, we investigated whether moral identity was associated with athletes’ perception of fair play and attitudes towards doping in sport, and whether the effect of moral identity on attitudes to doping was mediated by perception of fair play. It was found that moral identity had significant direct effects on attitudes towards doping (β = −0.14, p < 0.001) and a significant indirect effect via endorsement of fair play on attitudes to doping (β = −0.10, p < 0.05) ( Table 2 and Figure 1 ). The more positive perceptions for fair play that were demonstrated were also significantly related to attitudes to doping (β = −0.51, p < 0.001. These findings provide support for the mediating role of endorsement of fair play on the relationship between moral identity and attitudes to doping ( F = 25.12, p < 0.001, R = 0.45).

The effects of moral identity on attitudes to doping and the mediating role of perception of fair play among athletes. Note: The values presented are the unstandardized regression coefficients. A solid line represents a significant relationship. *** p < 0.001.

Direct and indirect effects of moral identity on attitudes to doping among athletes.

Note: Unstandardized coefficients for the paths are shown. CSIE: completely standardized indirect effect, where 0.01 = small, 0.09 = medium and 0.25 = large. * p < 0.05; *** p < 0.001.

Next, we investigated whether the moral identity of non-athletes was associated with their perception of fair play and attitudes towards doping in sport, and whether the effect of moral identity on attitudes to doping was mediated by the perception of fair play. Analyses showed that the moral identity of non-athlete students was not directly related to attitudes towards doping ( Table 3 and Figure 2 ). Results of the analysis show that moral identity was directly positively related to participants’ endorsement of fair play (β = 0.08, p < 0.05). Importantly, moral identity had a significant indirect effect on attitudes towards doping via the perception of fair play (β = −0.08, p < 0.05) ( F = 18.16, p < 0.001, R = 0.43).

The effects of moral identity on attitudes to doping and the mediating role of perception of fair play among non-athletes. Note: The values presented are the unstandardized regression coefficients. A solid line represents a significant relationship. * p < 0.05; *** p < 0.001.

Direct and indirect effects of moral identity on attitudes to doping among non-athletes.

4. Discussion

It has been proposed that personal morality might be the most influencing variable on doping attitudes [ 27 , 28 ]. Building on research conducted on the role of moral identity on doping likelihood [ 34 , 35 , 40 ], we examined the association between student athletes’ moral identity and attitudes towards doping, and whether their perception of fair play mediated this association. Next, we analyzed the same relationship among non-athlete students. The study provided evidence on these relationships and at the same time allowed for practical guidance to be provided separately for athletes and non-athlete students.

4.1. Athletes’ Moral Identity, Perception of Fair Play and Attitudes towards Doping

In support of our hypothesis, we found that moral identity was negatively associated with athletes’ attitudes towards doping. This result is in line with existing cross-sectional studies [ 28 ] reporting that elite Australian athletes with a weaker moral stance against the use of performance-enhancing substances had more favorable attitudes towards doping. However, it should be noted that in these previous studies, morality was measured as a judgment of cheating or as a moral judgment of doping. In our study, we assessed morality not as a specific action precisely in the context of sport. Rather, our focus was on moral identity, which reveals how morality is important in personal self-perception. Therefore, our finding extends past works by revealing that using doping is viewed as unethical behavior, which is not compatible with the perception of the athlete as a moral person.

Emphasizing the importance of moral values in sport, this study also examined the relationship of the mediating role of the perception of fair play in moral identity and attitudes towards doping. The study data confirmed that endorsement of fair play mediated the relationship between athletes’ moral identity and attitudes towards doping. Thus, athletes with a stronger moral identity may perceive fair play in sport as more important and, as a result, have more negative attitudes towards doping. On the other hand, those athletes showing less respect to fair play may have lower moral standards and thus demonstrate more positive doping attitudes. The current finding is also important because the perception of fair play reveals the individual’s value orientations in sports-related behavior. It is values that shape personal attitudes; in other words, attitudes are characterized by the function of expressing values [ 51 ]. Therefore, when assessing the impact of attitudes on behavioral decisions and behavior, the orientation of the athlete to moral values in sport is also important. Previous studies with adolescents and adult athletes [ 16 , 52 ] revealed not only a correlation between perceived moral values and attitudes to doping, but also that attitudes fully mediated the effect of sportspersonship orientation on doping intention.

The athletes’ study results provide rationale that strengthening the moral identity of athletes and, at the same time, promoting their anti-doping attitudes and internalization of moral values is important, which is in line with other research. A recent randomized control trial provides support that intervention not only involves knowledge about doping, but that moral values in sport also affect the moral identity of athletes [ 53 ]. On the other hand, it is not enough to limit oneself to sport-related values or to emphasize their importance in sport alone, especially in strengthening moral identity. Ring et al. [ 54 ] examined the relationships between Schwartz’s basic values and doping likelihood among university athletes and found that self-enhancement values were positively related, whereas self-transcendence and conservation values were negatively related, to doping likelihood. Therefore, in order to strengthen the moral identity of athletes and promote more negative attitudes to doping in sport, coaches need to not only encourage athletes to analyze situations related to both doping specifically and anti-social behavior in general. In this debate, it is also important to promote personal responsibility in situations that raise ethical dilemmas. Strengthening athletes’ moral identity requires the integration of the moral values of sport with personal values. In other words, the analysis of the compatibility of personal values with those that are important in sport, or the existence of certain contradictions in values, should be encouraged. It also requires the knowledge of the values of athletes that are the most important for them as moral persons and as athletes. Often, various doping prevention recommendations emphasize the role of the coach as a key person in the team. However, in implementing the recommendations made in the context of adult sport, a sports psychologist, if available, can also significantly help.

4.2. Non-Athletes’ Moral Identity, Perception of Fair Play, and Attitudes towards Doping

In this study, we also aimed to measure the relationship between moral identity, perception of fair play, and attitudes among non-athlete students. In other words, we sought to determine if the established relationship between the study variables among athletes would differ, and if it differed, to what extent. To our knowledge, this is the first study examining such relationships among non-athletes, except for studies comparing athletes and non-athletes’ attitudes towards doping [ 41 , 55 ]. Before discussing the main findings it should be noted that our study found athletes’ attitudes towards doping to be more positive compared to those of non-athletes. This is not consistent with other studies, which have demonstrated that athletes hold more negative attitudes than the general population [ 41 , 55 ]. However, research participants’ age and time of the study as well as the cultural context need to be considered when comparing the data. Therefore, we will not further analyze these differences and rather focus on the main results. It should be acknowledged that moral identity was not directly associated with non-athletes’ attitudes towards doping. However, higher endorsement of fair play was negatively related to doping attitudes. Furthermore, we found that the perception of fair play mediated the relationship between moral identity and doping attitudes, suggesting that if people with a stronger moral identity are also more likely to endorse fair play, they would demonstrate more negative attitudes towards doping. It should be acknowledged that the main difference between non-athletes and athletes in our study lies in the direct effect of moral identity on attitudes. Interestingly, the effect of the perception of fair play on doping attitudes is the same among both athletes and non-athletes. These findings highlighted that in people’s moral schema, there may be certain behavior that would be morally wrong in everyday life but perceived as not so wrong in the sports context, as something separate from everyday life. In other words, people may view doping as a minor problem as it is related to a limited number of athletes [ 41 ], and such behavior is related to athletes’ morality [ 56 ]. However, we would still assume that how a person values moral values and moral behavior in sports is important. As our data revealed, if fair play is perceived as important in sport, people with a stronger moral identity tend to evaluate morally wrong behavior in sport more negatively. Thus, our data partially extend previous research suggesting that non-athletes may experience a potential conflict of values when evaluating what is good and bad behavior in sport [ 41 , 56 ], especially regarding their interest in sport [ 44 ].

Our findings on the relationship between non-athletes’ moral identity, the perception of fair play, and attitudes towards doping have some practical implications. As university students were involved in our study, in their study modules, it would be useful to include topics on the use of banned drugs in sport or on cheating in sport in general as morally inadequate behaviors. Moreover, it is necessary to communicate and discuss that such behavior is morally wrong not only in the sports context. In this way, their attitudes towards doping as an essentially moral problem could be encouraged. Students’ negative attitudes towards this are important as some of these students will occupy various sport-related decision-making positions in their future professional lives. If their professional activities are not related to sports, their moral position as citizens would remain important.

4.3. Limitations and Future Research

This study is not without limitation. The study analyzed the attitudes towards doping only and did not include actual behaviors. However, this was partly due to the inclusion of non-athlete subjects in the study whose actual doping behavior could not be investigated. It would be worthwhile to include a variable of intent to use banned drugs in further research. Another limitation of the study is related to the relatively small sample size. A larger sample of athletes, especially involving those in a wider variety of sports branches, would allow one to examine the extent to which the interrelationships between moral identities, perception of fair play, and doping attitudes occur depending on the sport. This claim corresponds with recent research that attempted to analyze attitudes and views on doping in particular sports such as track and field [ 57 ] or, more specifically, elite distance running [ 58 ]. Such analyses are also encouraged by doping control test findings showing that detected banned substances in anti-doping control tests differ depending on the sports discipline [ 5 ].

As this study exclusively examines athletes who are studying at university, it would be worthwhile to examine non-students in sport or those athletes who have already completed their studies. It should also be mentioned that there is still a great lack of longitudinal studies to better understand how athletes’ perceptions of moral values and attitudes towards them, as well as their real behaviors, change. In the study of non-athletes, population surveys remain relevant to reveal how people evaluate the doping problem in sport. Research that allows comparisons to be made of how doping evaluations vary depending on different cultural contexts should also be encouraged.

5. Conclusions

In conclusion, the current study reinforces the assertion that both moral identity and perception of fair play are important constructs affecting athletes’ attitudes towards doping. Our findings suggest that those with a stronger moral identity and higher endorsement of fair play demonstrate more negative attitudes towards doping. The study revealed that the evaluation of fair play is a factor directly related to non-athletes’ attitudes, and moral identity as a single factor is not related to students’ attitudes. This suggests that non-athletes perceive moral issues in sport as possibly more related to the context of sport and less as generally morally wrong behaviors.

Acknowledgments

The authors thank all the students who participated in this study.

Author Contributions

Conceptualization, S.S.; methodology, S.S. and D.M.; investigation, S.S., D.M., D.K., and I.T.; formal analysis, S.S. and D.M., D.K.; writing—original draft preparation, S.S.; writing—review and editing, S.S., D.M., D.K., and I.T. All authors have read and agreed to the published version of the manuscript.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Research Ethics Committee of Lithuanian Sports University (approval number SMTEK-47, released on 29 October 2019).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare that there are no conflict of interest regarding the publication of this article.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

• DOI: 10.1177/1356336X03009002003
• Corpus ID: 144141858

Conceptualizing `Fair Play': A Review of the Literature

• Heather Sheridan
• Published 1 June 2003
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