research paper about team sport

• Systematic review update
• Open access
• Published: 21 June 2023

The impact of sports participation on mental health and social outcomes in adults: a systematic review and the ‘Mental Health through Sport’ conceptual model

• Narelle Eather   ORCID: orcid.org/0000-0002-6320-4540 1 , 2 ,
• Levi Wade   ORCID: orcid.org/0000-0002-4007-5336 1 , 3 ,
• Aurélie Pankowiak   ORCID: orcid.org/0000-0003-0178-513X 4 &
• Rochelle Eime   ORCID: orcid.org/0000-0002-8614-2813 4 , 5  

Systematic Reviews volume  12 , Article number:  102 ( 2023 ) Cite this article

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Sport is a subset of physical activity that can be particularly beneficial for short-and-long-term physical and mental health, and social outcomes in adults. This study presents the results of an updated systematic review of the mental health and social outcomes of community and elite-level sport participation for adults. The findings have informed the development of the ‘Mental Health through Sport’ conceptual model for adults.

Nine electronic databases were searched, with studies published between 2012 and March 2020 screened for inclusion. Eligible qualitative and quantitative studies reported on the relationship between sport participation and mental health and/or social outcomes in adult populations. Risk of bias (ROB) was determined using the Quality Assessment Tool (quantitative studies) or Critical Appraisal Skills Programme (qualitative studies).

The search strategy located 8528 articles, of which, 29 involving adults 18–84 years were included for analysis. Data was extracted for demographics, methodology, and study outcomes, and results presented according to study design. The evidence indicates that participation in sport (community and elite) is related to better mental health, including improved psychological well-being (for example, higher self-esteem and life satisfaction) and lower psychological ill-being (for example, reduced levels of depression, anxiety, and stress), and improved social outcomes (for example, improved self-control, pro-social behavior, interpersonal communication, and fostering a sense of belonging). Overall, adults participating in team sport had more favorable health outcomes than those participating in individual sport, and those participating in sports more often generally report the greatest benefits; however, some evidence suggests that adults in elite sport may experience higher levels of psychological distress. Low ROB was observed for qualitative studies, but quantitative studies demonstrated inconsistencies in methodological quality.

Conclusions

The findings of this review confirm that participation in sport of any form (team or individual) is beneficial for improving mental health and social outcomes amongst adults. Team sports, however, may provide more potent and additional benefits for mental and social outcomes across adulthood. This review also provides preliminary evidence for the Mental Health through Sport model, though further experimental and longitudinal evidence is needed to establish the mechanisms responsible for sports effect on mental health and moderators of intervention effects. Additional qualitative work is also required to gain a better understanding of the relationship between specific elements of the sporting environment and mental health and social outcomes in adult participants.

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Introduction

The organizational structure of sport and the performance demands characteristic of sport training and competition provide a unique opportunity for participants to engage in health-enhancing physical activity of varied intensity, duration, and mode; and the opportunity to do so with other people as part of a team and/or club. Participation in individual and team sports have shown to be beneficial to physical, social, psychological, and cognitive health outcomes [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Often, the social and mental health benefits facilitated through participation in sport exceed those achieved through participation in other leisure-time or recreational activities [ 8 , 9 , 10 ]. Notably, these benefits are observed across different sports and sub-populations (including youth, adults, older adults, males, and females) [ 11 ]. However, the evidence regarding sports participation at the elite level is limited, with available research indicating that elite athletes may be more susceptible to mental health problems, potentially due to the intense mental and physical demands placed on elite athletes [ 12 ].

Participation in sport varies across the lifespan, with children representing the largest cohort to engage in organized community sport [ 13 ]. Across adolescence and into young adulthood, dropout from organized sport is common, and especially for females [ 14 , 15 , 16 ], and adults are shifting from organized sports towards leisure and fitness activities, where individual activities (including swimming, walking, and cycling) are the most popular [ 13 , 17 , 18 , 19 ]. Despite the general decline in sport participation with age [ 13 ], the most recent (pre-COVID) global data highlights that a range of organized team sports (such as, basketball, netball volleyball, and tennis) continue to rank highly amongst adult sport participants, with soccer remaining a popular choice across all regions of the world [ 13 ]. It is encouraging many adults continue to participate in sport and physical activities throughout their lives; however, high rates of dropout in youth sport and non-participation amongst adults means that many individuals may be missing the opportunity to reap the potential health benefits associated with participation in sport.

According to the World Health Organization, mental health refers to a state of well-being and effective functioning in which an individual realizes his or her own abilities, is resilient to the stresses of life, and is able to make a positive contribution to his or her community [ 20 ]. Mental health covers three main components, including psychological, emotional and social health [ 21 ]. Further, psychological health has two distinct indicators, psychological well-being (e.g., self-esteem and quality of life) and psychological ill-being (e.g., pre-clinical psychological states such as psychological difficulties and high levels of stress) [ 22 ]. Emotional well-being describes how an individual feels about themselves (including life satisfaction, interest in life, loneliness, and happiness); and social well–being includes an individual’s contribution to, and integration in society [ 23 ].

Mental illnesses are common among adults and incidence rates have remained consistently high over the past 25 years (~ 10% of people affected globally) [ 24 ]. Recent statistics released by the World Health Organization indicate that depression and anxiety are the most common mental disorders, affecting an estimated 264 million people, ranking as one of the main causes of disability worldwide [ 25 , 26 ]. Specific elements of social health, including high levels of isolation and loneliness among adults, are now also considered a serious public health concern due to the strong connections with ill-health [ 27 ]. Participation in sport has shown to positively impact mental and social health status, with a previous systematic review by Eime et al. (2013) indicated that sports participation was associated with lower levels of perceived stress, and improved vitality, social functioning, mental health, and life satisfaction [ 1 ]. Based on their findings, the authors developed a conceptual model (health through sport) depicting the relationship between determinants of adult sports participation and physical, psychological, and social health benefits of participation. In support of Eime’s review findings, Malm and colleagues (2019) recently described how sport aids in preventing or alleviating mental illness, including depressive symptoms and anxiety or stress-related disease [ 7 ]. Andersen (2019) also highlighted that team sports participation is associated with decreased rates of depression and anxiety [ 11 ]. In general, these reviews report stronger effects for sports participation compared to other types of physical activity, and a dose–response relationship between sports participation and mental health outcomes (i.e., higher volume and/or intensity of participation being associated with greater health benefits) when adults participate in sports they enjoy and choose [ 1 , 7 ]. Sport is typically more social than other forms of physical activity, including enhanced social connectedness, social support, peer bonding, and club support, which may provide some explanation as to why sport appears to be especially beneficial to mental and social health [ 28 ].

Thoits (2011) proposed several potential mechanisms through which social relationships and social support improve physical and psychological well-being [ 29 ]; however, these mechanisms have yet to be explored in the context of sports participation at any level in adults. The identification of the mechanisms responsible for such effects may direct future research in this area and help inform future policy and practice in the delivery of sport to enhance mental health and social outcomes amongst adult participants. Therefore, the primary objective of this review was to examine and synthesize all research findings regarding the relationship between sports participation, mental health and social outcomes at the community and elite level in adults. Based on the review findings, the secondary objective was to develop the ‘Mental Health through Sport’ conceptual model.

This review has been registered in the PROSPERO systematic review database and assigned the identifier: CRD42020185412. The conduct and reporting of this systematic review also follows the Preferred Reporting for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 30 ] (PRISMA flow diagram and PRISMA Checklist available in supplementary files ). This review is an update of a previous review of the same topic [ 31 ], published in 2012.

Identification of studies

Nine electronic databases (CINAHL, Cochrane Library, Google Scholar, Informit, Medline, PsychINFO, Psychology and Behavioural Sciences Collection, Scopus, and SPORTDiscus) were systematically searched for relevant records published from 2012 to March 10, 2020. The following key terms were developed by all members of the research team (and guided by previous reviews) and entered into these databases by author LW: sport* AND health AND value OR benefit* OR effect* OR outcome* OR impact* AND psych* OR depress* OR stress OR anxiety OR happiness OR mood OR ‘quality of life’ OR ‘social health’ OR ‘social relation*’ OR well* OR ‘social connect*’ OR ‘social functioning’ OR ‘life satisfac*’ OR ‘mental health’ OR social OR sociolog* OR affect* OR enjoy* OR fun. Where possible, Medical Subject Headings (MeSH) were also used.

Criteria for inclusion/exclusion

The titles of studies identified using this method were screened by LW. Abstract and full text of the articles were reviewed independently by LW and NE. To be included in the current review, each study needed to meet each of the following criteria: (1) published in English from 2012 to 2020; (2) full-text available online; (3) original research or report published in a peer-reviewed journal; (4) provides data on the psychological or social effects of participation in sport (with sport defined as a subset of exercise that can be undertaken individually or as a part of a team, where participants adhere to a common set of rules or expectations, and a defined goal exists); (5) the population of interest were adults (18 years and older) and were apparently healthy. All papers retrieved in the initial search were assessed for eligibility by title and abstract. In cases where a study could not be included or excluded via their title and abstract, the full text of the article was reviewed independently by two of the authors.

Data extraction

For the included studies, the following data was extracted independently by LW and checked by NE using a customized Google Docs spreadsheet: author name, year of publication, country, study design, aim, type of sport (e.g., tennis, hockey, team, individual), study conditions/comparisons, sample size, where participants were recruited from, mean age of participants, measure of sports participation, measure of physical activity, psychological and/or social outcome/s, measure of psychological and/or social outcome/s, statistical method of analysis, changes in physical activity or sports participation, and the psychological and/or social results.

Risk of bias (ROB) assessment

A risk of bias was performed by LW and AP independently using the ‘Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies’ OR the ‘Quality Assessment of Controlled Intervention Studies’ for the included quantitative studies, and the ‘Critical Appraisal Skills Programme (CASP) Checklist for the included qualitative studies [ 32 , 33 ]. Any discrepancies in the ROB assessments were discussed between the two reviewers, and a consensus reached.

The search yielded 8528 studies, with a total of 29 studies included in the systematic review (Fig.  1 ). Tables  1 and 2 provide a summary of the included studies. The research included adults from 18 to 84 years old, with most of the evidence coming from studies targeting young adults (18–25 years). Study samples ranged from 14 to 131, 962, with the most reported psychological outcomes being self-rated mental health ( n  = 5) and depression ( n  = 5). Most studies did not investigate or report the link between a particular sport and a specific mental health or social outcome; instead, the authors’ focused on comparing the impact of sport to physical activity, and/or individual sports compared to team sports. The results of this review are summarized in the following section, with findings presented by study design (cross-sectional, experimental, and longitudinal).

Flow of studies through the review process

Effects of sports participation on psychological well-being, ill-being, and social outcomes

Cross-sectional evidence.

This review included 14 studies reporting on the cross-sectional relationship between sports participation and psychological and/or social outcomes. Sample sizes range from n  = 414 to n  = 131,962 with a total of n  = 239,394 adults included across the cross-sectional studies.

The cross-sectional evidence generally supports that participation in sport, and especially team sports, is associated with greater mental health and psychological wellbeing in adults compared to non-participants [ 36 , 59 ]; and that higher frequency of sports participation and/or sport played at a higher level of competition, are also linked to lower levels of mental distress in adults . This was not the case for one specific study involving ice hockey players aged 35 and over, with Kitchen and Chowhan (2016) Kitchen and Chowhan (2016) reporting no relationship between participation in ice hockey and either mental health, or perceived life stress [ 54 ]. There is also some evidence to support that previous participation in sports (e.g., during childhood or young adulthood) is linked to better mental health outcomes later in life, including improved mental well-being and lower mental distress [ 59 ], even after controlling for age and current physical activity.

Compared to published community data for adults, elite or high-performance adult athletes demonstrated higher levels of body satisfaction, self-esteem, and overall life satisfaction [ 39 ]; and reported reduced tendency to respond to distress with anger and depression. However, rates of psychological distress were higher in the elite sport cohort (compared to community norms), with nearly 1 in 5 athletes reporting ‘high to very high’ distress, and 1 in 3 reporting poor mental health symptoms at a level warranting treatment by a health professional in one study ( n  = 749) [ 39 ].

Four studies focused on the associations between physical activity and sports participation and mental health outcomes in older adults. Physical activity was associated with greater quality of life [ 56 ], with the relationship strongest for those participating in sport in middle age, and for those who cycled in later life (> 65) [ 56 ]. Group physical activities (e.g., walking groups) and sports (e.g., golf) were also significantly related to excellent self-rated health, low depressive symptoms, high health-related quality of life (HRQoL) and a high frequency of laughter in males and females [ 60 , 61 ]. No participation or irregular participation in sport was associated with symptoms of mild to severe depression in older adults [ 62 ].

Several cross-sectional studies examined whether the effects of physical activity varied by type (e.g., total physical activity vs. sports participation). In an analysis of 1446 young adults (mean age = 18), total physical activity, moderate-to-vigorous physical activity, and team sport were independently associated with mental health [ 46 ]. Relative to individual physical activity, after adjusting for covariates and moderate-to-vigorous physical activity (MVPA), only team sport was significantly associated with improved mental health. Similarly, in a cross-sectional analysis of Australian women, Eime, Harvey, Payne (2014) reported that women who engaged in club and team-based sports (tennis or netball) reported better mental health and life satisfaction than those who engaged in individual types of physical activity [ 47 ]. Interestingly, there was no relationship between the amount of physical activity and either of these outcomes, suggesting that other qualities of sports participation contribute to its relationship to mental health and life satisfaction. There was also some evidence to support a relationship between exercise type (ball sports, aerobic activity, weightlifting, and dancing), and mental health amongst young adults (mean age 22 years) [ 48 ], with ball sports and dancing related to fewer symptoms of depression in students with high stress; and weightlifting related to fewer depressive symptoms in weightlifters exhibiting low stress.

Longitudinal evidence

Eight studies examined the longitudinal relationship between sports participation and either mental health and/or social outcomes. Sample sizes range from n  = 113 to n  = 1679 with a total of n  = 7022 adults included across the longitudinal studies.

Five of the included longitudinal studies focused on the relationship between sports participation in childhood or adolescence and mental health in young adulthood. There is evidence that participation in sport in high-school is protective of future symptoms of anxiety (including panic disorder, generalised anxiety disorder, social phobia, and agoraphobia) [ 42 ]. Specifically, after controlling for covariates (including current physical activity), the number of years of sports participation in high school was shown to be protective of symptoms of panic and agoraphobia in young adulthood, but not protective of symptoms of social phobia or generalized anxiety disorder [ 42 ]. A comparison of individual or team sports participation also revealed that participation in either context was protective of panic disorder symptoms, while only team sport was protective of agoraphobia symptoms, and only individual sport was protective of social phobia symptoms. Furthermore, current and past sports team participation was shown to negatively relate to adult depressive symptoms [ 43 ]; drop out of sport was linked to higher depressive symptoms in adulthood compared to those with maintained participation [ 9 , 22 , 63 ]; and consistent participation in team sports (but not individual sport) in adolescence was linked to higher self-rated mental health, lower perceived stress and depressive symptoms, and lower depression scores in early adulthood [ 53 , 58 ].

Two longitudinal studies [ 35 , 55 ], also investigated the association between team and individual playing context and mental health. Dore and colleagues [ 35 ] reported that compared to individual activities, being active in informal groups (e.g., yoga, running groups) or team sports was associated with better mental health, fewer depressive symptoms and higher social connectedness – and that involvement in team sports was related to better mental health regardless of physical activity volume. Kim and James [ 55 ] discovered that sports participation led to both short and long-term improvements in positive affect and life satisfaction.

A study on social outcomes related to mixed martial-arts (MMA) and Brazilian jiu-jitsu (BJJ) showed that both sports improved practitioners’ self-control and pro-social behavior, with greater improvements seen in the BJJ group [ 62 ]. Notably, while BJJ reduced participants’ reported aggression, there was a slight increase in MMA practitioners, though it is worth mentioning that individuals who sought out MMA had higher levels of baseline aggression.

Experimental evidence

Six of the included studies were experimental or quasi-experimental. Sample sizes ranged from n  = 28 to n  = 55 with a total of n  = 239 adults included across six longitudinal studies. Three studies involved a form of martial arts (such as judo and karate) [ 45 , 51 , 52 ], one involved a variety of team sports (such as netball, soccer, and cricket) [ 34 ], and the remaining two focused on badminton [ 57 ] and handball [ 49 ].

Brinkley and colleagues [ 34 ] reported significant effects on interpersonal communication (but not vitality, social cohesion, quality of life, stress, or interpersonal relationships) for participants ( n  = 40) engaging in a 12-week workplace team sports intervention. Also using a 12-week intervention, Hornstrup et al. [ 49 ] reported a significant improvement in mental energy (but not well-being or anxiety) in young women (mean age = 24; n  = 28) playing in a handball program. Patterns et al. [ 57 ] showed that in comparison to no exercise, participation in an 8-week badminton or running program had no significant improvement on self-esteem, despite improvements in perceived and actual fitness levels.

Three studies examined the effect of martial arts on the mental health of older adults (mean ages 79 [ 52 ], 64 [ 51 ], and 70 [ 45 ] years). Participation in Karate-Do had positive effects on overall mental health, emotional wellbeing, depression and anxiety when compared to other activities (physical, cognitive, mindfulness) and a control group [ 51 , 52 ]. Ciaccioni et al. [ 45 ] found that a Judo program did not affect either the participants’ mental health or their body satisfaction, citing a small sample size, and the limited length of the intervention as possible contributors to the findings.

Qualitative evidence

Three studies interviewed current or former sports players regarding their experiences with sport. Chinkov and Holt [ 41 ] reported that jiu-jitsu practitioners (mean age 35 years) were more self-confident in their lives outside of the gym, including improved self-confidence in their interactions with others because of their training. McGraw and colleagues [ 37 ] interviewed former and current National Football League (NFL) players and their families about its impact on the emotional and mental health of the players. Most of the players reported that their NFL career provided them with social and emotional benefits, as well as improvements to their self-esteem even after retiring. Though, despite these benefits, almost all the players experienced at least one mental health challenge during their career, including depression, anxiety, or difficulty controlling their temper. Some of the players and their families reported that they felt socially isolated from people outside of the national football league.

Through a series of semi-structured interviews and focus groups, Thorpe, Anders [ 40 ] investigated the impact of an Aboriginal male community sporting team on the health of its players. The players reported they felt a sense of belonging when playing in the team, further noting that the social and community aspects were as important as the physical health benefits. Participating in the club strengthened the cultural identity of the players, enhancing their well-being. The players further noted that participation provided them with enjoyment, stress relief, a sense of purpose, peer support, and improved self-esteem. Though they also noted challenges, including the presence of racism, community conflict, and peer-pressure.

Quality of studies

Full details of our risk of bias (ROB) results are provided in Supplementary Material A . Of the three qualitative studies assessed using the Critical Appraisal Skills Program (CASP), all three were deemed to have utilised and reported appropriate methodological standards on at least 8 of the 10 criteria. Twenty studies were assessed using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies, with all studies clearly reporting the research question/s or objective/s and study population. However, only four studies provided a justification for sample size, and less than half of the studies met quality criteria for items 6, 7, 9, or 10 (and items 12 and 13 were largely not applicable). Of concern, only four of the observational or cohort studies were deemed to have used clearly defined, valid, and reliable exposure measures (independent variables) and implemented them consistently across all study participants. Six studies were assessed using the Quality Assessment of Controlled Intervention Studies, with three studies described as a randomized trial (but none of the three reported a suitable method of randomization, concealment of treatment allocation, or blinding to treatment group assignment). Three studies showed evidence that study groups were similar at baseline for important characteristics and an overall drop-out rate from the study < 20%. Four studies reported high adherence to intervention protocols (with two not reporting) and five demonstrated that.study outcomes were assessed using valid and reliable measures and implemented consistently across all study participants. Importantly, researchers did not report or have access to validated instruments for assessing sport participation or physical activity amongst adults, though most studies provided psychometrics for their mental health outcome measure/s. Only one study reported that the sample size was sufficiently powered to detect a difference in the main outcome between groups (with ≥ 80% power) and that all participants were included in the analysis of results (intention-to-treat analysis). In general, the methodological quality of the six randomised studies was deemed low.

Initially, our discussion will focus on the review findings regarding sports participation and well-being, ill-being, and psychological health. However, the heterogeneity and methodological quality of the included research (especially controlled trials) should be considered during the interpretation of our results. Considering our findings, the Mental Health through Sport conceptual model for adults will then be presented and discussed and study limitations outlined.

Sports participation and psychological well-being

In summary, the evidence presented here indicates that for adults, sports participation is associated with better overall mental health [ 36 , 46 , 47 , 59 ], mood [ 56 ], higher life satisfaction [ 39 , 47 ], self-esteem [ 39 ], body satisfaction [ 39 ], HRQoL [ 60 ], self-rated health [ 61 ], and frequency of laughter [ 61 ]. Sports participation has also shown to be predictive of better psychological wellbeing over time [ 35 , 53 ], higher positive affect [ 55 ], and greater life satisfaction [ 55 ]. Furthermore, higher frequency of sports participation and/or sport played at a higher level of competition, have been linked to lower levels of mental distress, higher levels of body satisfaction, self-esteem, and overall life satisfaction in adults [ 39 ].

Despite considerable heterogeneity of sports type, cross-sectional and experimental research indicate that team-based sports participation, compared to individual sports and informal group physical activity, has a more positive effect on mental energy [ 49 ], physical self-perception [ 57 ], and overall psychological health and well-being in adults, regardless of physical activity volume [ 35 , 46 , 47 ]. And, karate-do benefits the subjective well-being of elderly practitioners [ 51 , 52 ]. Qualitative research in this area has queried participants’ experiences of jiu-jitsu, Australian football, and former and current American footballers. Participants in these sports reported that their participation was beneficial for psychological well-being [ 37 , 40 , 41 ], improved self-esteem [ 37 , 40 , 41 ], and enjoyment [ 37 ].

Sports participation and psychological ill-being

Of the included studies, n  = 19 examined the relationship between participating in sport and psychological ill-being. In summary, there is consistent evidence that sports participation is related to lower depression scores [ 43 , 48 , 61 , 62 ]. There were mixed findings regarding psychological stress, where participation in childhood (retrospectively assessed) was related to lower stress in young adulthood [ 41 ], but no relationship was identified between recreational hockey in adulthood and stress [ 54 ]. Concerning the potential impact of competing at an elite level, there is evidence of higher stress in elite athletes compared to community norms [ 39 ]. Further, there is qualitative evidence that many current or former national football league players experienced at least one mental health challenge, including depression, anxiety, difficulty controlling their temper, during their career [ 37 ].

Evidence from longitudinal research provided consistent evidence that participating in sport in adolescence is protective of symptoms of depression in young adulthood [ 43 , 53 , 58 , 63 ], and further evidence that participating in young adulthood is related to lower depressive symptoms over time (6 months) [ 35 ]. Participation in adolescence was also protective of manifestations of anxiety (panic disorder and agoraphobia) and stress in young adulthood [ 42 ], though participation in young adulthood was not related to a more general measure of anxiety [ 35 ] nor to changes in negative affect [ 55 ]). The findings from experimental research were mixed. Two studies examined the effect of karate-do on markers of psychological ill-being, demonstrating its capacity to reduce anxiety [ 52 ], with some evidence of its effectiveness on depression [ 51 ]. The other studies examined small-sided team-based games but showed no effect on stress or anxiety [ 34 , 49 ]. Most studies did not differentiate between team and individual sports, though one study found that adolescents who participated in team sports (not individual sports) in secondary school has lower depression scores in young adulthood [ 58 ].

Sports participation and social outcomes

Seven of the included studies examined the relationship between sports participation and social outcomes. However, very few studies examined social outcomes or tested a social outcome as a potential mediator of the relationship between sport and mental health. It should also be noted that this body of evidence comes from a wide range of sport types, including martial arts, professional football, and workplace team-sport, as well as different methodologies. Taken as a whole, the evidence shows that participating in sport is beneficial for several social outcomes, including self-control [ 50 ], pro-social behavior [ 50 ], interpersonal communication [ 34 ], and fostering a sense of belonging [ 40 ]. Further, there is evidence that group activity, for example team sport or informal group activity, is related to higher social connectedness over time, though analyses showed that social connectedness was not a mediator for mental health [ 35 ].

There were conflicting findings regarding social effects at the elite level, with current and former NFL players reporting that they felt socially isolated during their career [ 37 ], whilst another study reported no relationship between participation at the elite level and social dysfunction [ 39 ]. Conversely, interviews with a group of indigenous men revealed that they felt as though participating in an all-indigenous Australian football team provided them with a sense of purpose, and they felt as though the social aspect of the game was as important as the physical benefits it provides [ 40 ].

Mental health through sport conceptual model for adults

The ‘Health through Sport’ model provides a depiction of the determinants and benefits of sports participation [ 31 ]. The model recognises that the physical, mental, and social benefits of sports participation vary by the context of sport (e.g., individual vs. team, organized vs. informal). To identify the elements of sport which contribute to its effect on mental health outcomes, we describe the ‘Mental Health through Sport’ model (Fig.  2 ). The model proposes that the social and physical elements of sport each provide independent, and likely synergistic contributions to its overall influence on mental health.

The Mental Health through Sport conceptual model

The model describes two key pathways through which sport may influence mental health: physical activity, and social relationships and support. Several likely moderators of this effect are also provided, including sport type, intensity, frequency, context (team vs. individual), environment (e.g., indoor vs. outdoor), as well as the level of competition (e.g., elite vs. amateur).

The means by which the physical activity component of sport may influence mental health stems from the work of Lubans et al., who propose three key groups of mechanisms: neurobiological, psychosocial, and behavioral [ 64 ]. Processes whereby physical activity may enhance psychological outcomes via changes in the structural and functional composition of the brain are referred to as neurobiological mechanisms [ 65 , 66 ]. Processes whereby physical activity provides opportunities for the development of self-efficacy, opportunity for mastery, changes in self-perceptions, the development of independence, and for interaction with the environment are considered psychosocial mechanisms. Lastly, processes by which physical activity may influence behaviors which ultimately affect psychological health, including changes in sleep duration, self-regulation, and coping skills, are described as behavioral mechanisms.

Playing sport offers the opportunity to form relationships and to develop a social support network, both of which are likely to influence mental health. Thoits [ 29 ] describes 7 key mechanisms by which social relationships and support may influence mental health: social influence/social comparison; social control; role-based purpose and meaning (mattering); self-esteem; sense of control; belonging and companionship; and perceived support availability [ 29 ]. These mechanisms and their presence within a sporting context are elaborated below.

Subjective to the attitudes and behaviors of individuals in a group, social influence and comparison may facilitate protective or harmful effects on mental health. Participants in individual or team sport will be influenced and perhaps steered by the behaviors, expectations, and norms of other players and teams. When individual’s compare their capabilities, attitudes, and values to those of other participants, their own behaviors and subsequent health outcomes may be affected. When others attempt to encourage or discourage an individual to adopt or reject certain health practices, social control is displayed [ 29 ]. This may evolve as strategies between players (or between players and coach) are discussion and implemented. Likewise, teammates may try to motivate each another during a match to work harder, or to engage in specific events or routines off-field (fitness programs, after game celebrations, attending club events) which may impact current and future physical and mental health.

Sport may also provide behavioral guidance, purpose, and meaning to its participants. Role identities (positions within a social structure that come with reciprocal obligations), often formed as a consequence of social ties formed through sport. Particularly in team sports, participants come to understand they form an integral part of the larger whole, and consequently, they hold certain responsibility in ensuring the team’s success. They have a commitment to the team to, train and play, communicate with the team and a potential responsibility to maintain a high level of health, perform to their capacity, and support other players. As a source of behavioral guidance and of purpose and meaning in life, these identities are likely to influence mental health outcomes amongst sport participants.

An individual’s level of self-esteem may be affected by the social relationships and social support provided through sport; with improved perceptions of capability (or value within a team) in the sporting domain likely to have positive impact on global self-esteem and sense of worth [ 64 ]. The unique opportunities provided through participation in sport, also allow individuals to develop new skills, overcome challenges, and develop their sense of self-control or mastery . Working towards and finding creative solutions to challenges in sport facilitates a sense of mastery in participants. This sense of mastery may translate to other areas of life, with individual’s developing the confidence to cope with varied life challenges. For example, developing a sense of mastery regarding capacity to formulate new / creative solutions when taking on an opponent in sport may result in greater confidence to be creative at work. Social relationships and social support provided through sport may also provide participants with a source of belonging and companionship. The development of connections (on and off the field) to others who share common interests, can build a sense of belonging that may mediate improvements in mental health outcomes. Social support is often provided emotionally during expressions of trust and care; instrumentally via tangible assistance; through information such as advice and suggestions; or as appraisal such feedback. All forms of social support provided on and off the field contribute to a more generalised sense of perceived support that may mediate the effect of social interaction on mental health outcomes.

Participation in sport may influence mental health via some combination of the social mechanisms identified by Thoits, and the neurobiological, psychosocial, and behavioral mechanisms stemming from physical activity identified by Lubans [ 29 , 64 ]. The exact mechanisms through which sport may confer psychological benefit is likely to vary between sports, as each sport varies in its physical and social requirements. One must also consider the social effects of sports participation both on and off the field. For instance, membership of a sporting team and/or club may provide a sense of identity and belonging—an effect that persists beyond the immediacy of playing the sport and may have a persistent effect on their psychological health. Furthermore, the potential for team-based activity to provide additional benefit to psychological outcomes may not just be attributable to the differences in social interactions, there are also physiological differences in the requirements for sport both within (team vs. team) and between (team vs. individual) categories that may elicit additional improvements in psychological outcomes. For example, evidence supports that exercise intensity moderates the relationship between physical activity and several psychological outcomes—supporting that sports performed at higher intensity will be more beneficial for psychological health.

Limitations and recommendations

There are several limitations of this review worthy of consideration. Firstly, amongst the included studies there was considerable heterogeneity in study outcomes and study methodology, and self-selection bias (especially in non-experimental studies) is likely to influence study findings and reduce the likelihood that study participants and results are representative of the overall population. Secondly, the predominately observational evidence included in this and Eime’s prior review enabled us to identify the positive relationship between sports participation and social and psychological health (and examine directionality)—but more experimental and longitudinal research is required to determine causality and explore potential mechanisms responsible for the effect of sports participation on participant outcomes. Additional qualitative work would also help researchers gain a better understanding of the relationship between specific elements of the sporting environment and mental health and social outcomes in adult participants. Thirdly, there were no studies identified in the literature where sports participation involved animals (such as equestrian sports) or guns (such as shooting sports). Such studies may present novel and important variables in the assessment of mental health benefits for participants when compared to non-participants or participants in sports not involving animals/guns—further research is needed in this area. Our proposed conceptual model also identifies several pathways through which sport may lead to improvements in mental health—but excludes some potentially negative influences (such as poor coaching behaviors and injury). And our model is not designed to capture all possible mechanisms, creating the likelihood that other mechanisms exist but are not included in this review. Additionally, an interrelationship exits between physical activity, mental health, and social relationships, whereby changes in one area may facilitate changes in the other/s; but for the purpose of this study, we have focused on how the physical and social elements of sport may mediate improvements in psychological outcomes. Consequently, our conceptual model is not all-encompassing, but designed to inform and guide future research investigating the impact of sport participation on mental health.

The findings of this review endorse that participation in sport is beneficial for psychological well-being, indicators of psychological ill-being, and social outcomes in adults. Furthermore, participation in team sports is associated with better psychological and social outcomes compared to individual sports or other physical activities. Our findings support and add to previous review findings [ 1 ]; and have informed the development of our ‘Mental Health through Sport’ conceptual model for adults which presents the potential mechanisms by which participation in sport may affect mental health.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We would like to acknowledge the work of the original systematic review conducted by Eime, R. M., Young, J. A., Harvey, J. T., Charity, M. J., and Payne, W. R. (2013).

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All authors contributed to the conducting of this study and reporting the findings. The titles of studies identified were screened by LW, and abstracts and full text articles reviewed independently by LW and NE. For the included studies, data was extracted independently by LW and checked by NE, and the risk of bias assessment was performed by LW and AP independently. All authors have read and approved the final version of the manuscript and agree with the order of presentation of the authors.

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Eather, N., Wade, L., Pankowiak, A. et al. The impact of sports participation on mental health and social outcomes in adults: a systematic review and the ‘Mental Health through Sport’ conceptual model. Syst Rev 12 , 102 (2023). https://doi.org/10.1186/s13643-023-02264-8

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Factors Influencing Team Performance: What Can Support Teams in High-Performance Sport Learn from Other Industries? A Systematic Scoping Review

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‘Talent wins games, teamwork wins championships’ Michael Jordan.

The primary aim of our systematic scoping review was to explore the factors influencing team function and performance across various industries and discuss findings in the context of the high-performance sport support team setting. These outcomes may also be used to inform future research into high-performance teamwork in sport.

A systematic scoping review of literature published in English since 2000 reporting team-based performance outcomes and included a performance metric that was ‘team outcome based’ was conducted using search of the Academic Search Ultimate, Medline, Business Source Ultimate, APA PsycInfo, CINAHL, SPORTDiscus, and Military database (ProQuest) using the terms: ‘team’, ‘function’ OR ‘dysfunction’, ‘Perform*’ OR ‘outcome’.

Application of the search strategy identified a total of 11,735 articles for title and abstract review. Seventy-three articles were selected for full-text assessment with the aim to extract data for either quantitative or qualitative analysis. Forty-six of the 73 articles met our inclusion criteria; 27 articles were excluded as they did not report a performance metric. Eleven studies explored leadership roles and styles on team performance, three studies associated performance feedback to team performance, and 12 studies explored the relationship between supportive behaviour and performance. Team orientation and adaptability as key figures of team performance outcomes were explored in 20 studies.

Conclusions

Our findings identified 4 key variables that were associated with team function and performance across a variety of industries; (i) leadership styles, (ii) supportive team behaviour, (iii) communication, and (iv) performance feedback. High-performance teams wishing to improve performance should examine these factors within their team and its environment. It is widely acknowledged that the dynamics of team function is important for outcomes in high-performance sport, yet there is little evidence to provide guidance. This inequality between real-world need and the available evidence should be addressed in future research.

Across multiple sectors, four key variables were identified as important for teamwork, team function, team performance and team effectiveness; (i) leadership style (ii) supportive team behaviour (iii) communication, and (iv) performance feedback.

Evidence obtained in this literature review was unable to illicit causal relationships between the four key variables important for high-performance sport support team function and individual athlete or playing team performance.

Considering factors associated with teamwork, team function team performance and team effectiveness from other sectors provides leverage points for high-performance sport support teams to improve functions.

Introduction

Each team has the potential to rise or fall based on the group of people who share the same passion and goals and are working together to achieve success [ 1 ]. This narrative is very common in elite sport, an environment that presents considerable health and performance challenges to the athlete and those charged with the responsibility of supporting them [ 2 ]. Considering that the success of athlete support teams is often measured by athletic performance outcomes [ 3 ], evidence supports the notion that contemporary athlete achievement can be strongly influenced by the function of the athlete support team [ 4 , 5 ]. However, given the enormity of the performance and health challenges, elite sport teams may need further inputs beyond traditional structures of coaching staff and limited number of medical personnel to influence health and athletic performance outcomes [ 6 ]. Research exploring the dynamics of team function and team performance in an elite sporting environment is one under-appreciated area that can assist meeting this increasing challenge. The nature of team function is a complex phenomenon that is far from resolved [ 1 ].

A ‘team’ can be defined as a group of individuals with specified roles and responsibilities interacting adaptively, interdependently, and dynamically towards a valued common outcome and who are together embedded in an encompassing organisational system, with boundaries and linkages to the broader system context and task environment [ 7 ]. Individuals within elite sport support teams include team/athlete coaches and the sports medicine and science team members who are constantly looking for ways to improve the performance and health of the athletes with whom they work [ 8 ]. Although varying in definition across sporting contexts, this team of individuals supporting the athlete form the high-performance team (HPT; see Fig.  1 ) [ 2 , 9 , 10 , 11 ]. Teamwork refers to the behavioural processes that team members (e.g. members of a HPT) use to achieve work within the team (e.g. communication, collaboration, sharing of expertise), and team function refers to a group of people working towards a common objective. That is, the function of a team relates to the ability to coordinate and cooperatively interact with each other to facilitate task objectives through a shared understanding of the team’s resources (e.g., members’ knowledge, skills, and experiences), the team’s goals and objectives, and the constraints within the work environment [ 12 , 13 , 14 ]. Thus, teamwork is a component of team function [ 15 , 16 ]. Team performance accounts for the cumulative outputs of the team’s actions, sometimes irrespective of how the team may have accomplished the task [ 7 ]. The effectiveness of a team, however, takes a holistic perspective in considering not only how the team performed, but also how the team interacted attempting to achieve a desired output (see Additional file 1 ) [ 15 ]. Thus, the performance of support teams in high-performance sport may not be simply reduced to the outcomes of the athletes or teams of athletes they support.

A model of the support team in high-performance sport

Teams that encourage and facilitate each other’s efforts in order to reach a common goal are influenced by issues of leadership [ 17 ], supportive team behaviour [ 18 ], organisational environment [ 19 ] and adaptability [ 20 ]. Teams educated about the mechanisms of teamwork (performance monitoring, adaptation, and facilitative leadership) have better performance outcomes [ 21 ], particularly when team members were able to anticipate each other’s behaviours and had better communication mechanisms. The addition of coordinating mechanisms such as supportive team behaviour, team communication and orientation are necessary facilitators of teamwork for a team to be successful [ 7 , 22 ]. Furthermore, the high-performance sporting environment presents challenges for individuals to function effectively as a team [ 23 ]. Despite increased interest in the teamwork construct [ 24 , 25 ], there are multiple and divergent conceptualisations of teamwork. There is a limited perspective in the present literature regarding the teamwork–team performance relationship [ 26 ]. To the authors’ knowledge, little work has described what the inputs and processes of teamwork are, nor described methodologies to measure the various influences and determine their role in assessing teamwork relative to performance in high-performance sport.

Challenges within HPTs in the elite sport setting arise because of factors such as organisational climate, professional conflict, power and influence challenges coupled with employment insecurities [ 19 ]. Additionally, high risk to reward scenarios, the demand to have a competitive advantage, and the emphasis on winning, have fractured the modern sports culture resulting in disparity and separation of athlete support staff and coaching staff within the same team [ 2 , 27 , 28 ]. Effective team function underpins the achievement of desired outcomes of collaborative work [ 12 ]. Consequently, suboptimal teamwork has at times catastrophic results for outcomes of such work [ 29 ]. While high-performance teams in elite sport have benefited from considerable scientific advances in physical preparation, participation and recovery practices, elite sport in this instance has not benefited from the science of teamwork effectiveness [ 30 ]. The primary aim of our systematic scoping review was to explore the factors influencing team function and performance across various industries and discuss findings in the context of the high-performance sport support team setting. These outcomes may also be used to inform future research into high-performance teamwork in sport.

We adopted the Preferred Reporting Items for Systematic Reviews and Meta-analysis extension (PRISMA-ScR) guidelines [ 31 ] to identify a primary set of articles for data extraction and review. The 5-step process as described by Arksey and O’Malley [ 32 ] with enhancements as described by Levac et al. [ 33 ] was utilised: Identify the research question, identify relevant studies, study selection, chart the data, and collate, summarise, and report the results. In the final step, the review process was supplemented by application of thematic analysis methods [ 34 ] to categorise each article within the themes that emerged from relevant literature on team effectiveness models [ 7 , 35 , 36 , 37 ]. The PRISMA extension for scoping reviews (PRISMA-ScR) checklist was used to ensure complete and transparent reporting [ 31 ].

Identification of Relevant Studies

The article inclusion criteria were; full text, empirical studies published in English, between 2000 and November 2021, and reported objective team-based performance outcomes and included a performance metric that was ‘team outcome based’, e.g., team effectiveness, cohesiveness, efficiency, reflexivity and potency. We chose to explore only articles with an objective performance based outcome to limit theoretical/speculative content. Articles were excluded under the following criteria: the study had no defined metric of performance outcomes, was a literature review or was an opinion piece.

A search of the Academic Search Ultimate, Medline, Business Source Ultimate, APA PsycInfo, CINAHL, SPORTDiscus, and Military database (ProQuest) was conducted in October 2021 using the terms: ‘team’, ‘function’ OR ‘dysfunction’, ‘Perform*’ OR ‘outcome’. All records retrieved by the search query were imported into Endnote X9 (Thompson Reuters, Carlsbad, CA, USA) and duplicates removed.

Final Study Selection

Two authors (BS, BGS) independently reviewed titles and abstracts for potential eligibility. For the potentially eligible records, the full-text articles were thereafter retrieved and assessed according to the inclusion and exclusion criteria. The reference lists of the resulting articles were searched by the lead author (BS) for inclusion of additional articles. Any discrepancies were discussed by the reviewers (BS, BGS). No conflicts were identified. The review of full-text articles revealed that those articles that reported a performance metric provided sufficient content data for a continued analysis.

Collating the Results

Analysis of the methodological and conceptual features of extracted data was thereafter performed by the lead author (BS) to summarise and collate the content of the articles and was subsequently confirmed by a co-author (BGS). Analysis of eligible papers involved describing the type of study which was performed, the occupational domain the study was conducted, where it was conducted, participant characteristics, study aims, performance metric and the category of teamwork. With regards to the conceptual analysis, we focused on examining common and emerging themes among definitions of team performance and their operationalisation (e.g., leadership, team orientation) as well as primary research findings as they pertained to team performance. A critical appraisal was not conducted on our findings as the aim of this review is to identify and map the available evidence [ 32 ].

The operationalisation categories followed the key themes of teamwork that emerged from the literature on team effectiveness models [ 7 , 12 ]. Team leadership roles and styles ; the ability to direct and coordinate the activities of other team members, assess team performance, assign tasks, develop team knowledge, skills, and abilities, performance goals and feedback ; the ability to develop common understandings of the team environment and apply appropriate task strategies to accurately monitor teammate performance, team orientation and adaptability ; the ability to adjust strategies based on information gathered from the environment through the use of supportive team behaviour and reallocation of intrateam resources, supportive team behaviour ; the ability to anticipate other team members’ needs through accurate knowledge about their responsibilities (Fig.  2 ).

Concept chart illustrating the characteristics of teamwork and how they are associated with team performance outcomes

Literature Search

The initial literature search identified a total of 11,734 articles for title and abstract review, and one article was retrieved from another source. Seventy-three articles were selected for full-text assessment with the aim to extract data for either quantitative or qualitative analysis. Forty-six of the 73 articles met our inclusion criteria; 27 articles were excluded as they did not report a performance metric. The article selection process is seen in Fig.  3 .

PRISMA flow chart showing the process for including studies

Study Characteristics

The 46 papers identified from the search process were published across a twenty-year period (2000–October 2021) (Figs.  4 , 5 ). Team performance outcomes were examined within business ( n  = 12), sport ( n  = 8), military ( n  = 6), health and social care ( n  = 3), engineering ( n  = 2), education ( n  = 1) or across multiple sectors ( n  = 14) (Fig.  4 ). In terms of geographical location, the studies were conducted across: North America 61% (USA, n  = 26; Canada, n  = 2), Europe 28% (UK, n  = 4; Netherlands, n  = 3; Spain, n  = 2; Germany, n  = 1; Italy, n  = 1; Portugal, n  = 1; Europe, unknown = 1), Asia Pacific 9% (South Korea, n  = 1; Pakistan, n  = 1; India, n  = 1; Australia, n  = 1), Africa 2% (Tunisia, n  = 1). There was a positive trend of the number of articles produced over the 2-decade period, 2000–2004 ( n  = 7), 2005–2009 ( n  = 10), 2010–2014 ( n  = 12) and 2015–2019 ( n  = 13) (Fig.  5 ).

Trend of the number of articles found between the various workplace domains

Trend of the number of articles found over the two-decade period 2000–2020. Note, articles from 2020 to 2021 are not included in this graph so that we can better demonstrate increased article production over time using evenly distributed time brackets

Studies utilised mixed methods approaches ( n  = 17) (i.e., questionnaires combined with archival data from financial reports and published articles), cross-sectional surveys ( n  = 12), experimental interventions designed to evaluate team performance among participants ( n  = 8), and interview-based approaches ( n  = 1). Other designs included archival analysis ( n  = 5) and laboratory-based experiments ( n  = 3).

Team Leadership Roles and Styles

Eleven studies explored leadership influences on team performance (Table 1 ) [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ]. The role of the team leader is described as pivotal for effective team function, as leaders have responsibility for team members and the direction of all team activity and processes [ 45 , 49 ]. Leaders who displayed higher cognitive ability, conscientiousness and charisma were better able to mediate their teams to enhance team performance outcomes [ 40 , 41 , 43 , 44 , 46 ]. Charismatic and transformational leaders positively change the values and priorities of team members and motivate them to perform beyond their expectations [ 39 , 41 ]. Leaders within a centralised structure where the decision-making authority is concentrated at the top, and all other lower levels follow the directions coming from the top of the organisation structure, have negative effects on conflict and performance. This leadership model also affects relationships between team members [ 38 , 42 , 46 ]. Our literature search revealed that teams will perform better when team leaders are highly involved in the team’s communication and workflow networks [ 42 , 45 ]. Specifically, in diverse work groups, the nature of interpersonal interactions was found to be an important determinant of group member performance and group effectiveness. For example, referring to gender diversity, one of the studies retrieved argued that a diverse group with low leader-member relationships (i.e. where relationships between team leaders and team members were poor) will not perform highly regardless of how well the leader differentiates role assignments because of insufficient attention to relationships [ 46 ]. Leaders act as influential role models, wherein their self-regulatory behaviours directly shape task-related team processes, which was shown to positively influence team performance [ 44 ].

Performance Goals and Feedback

Three studies associated performance monitoring to team performance (Table 2 ) [ 50 , 51 , 52 ]. They explored the use of negative feedback and positive reinforcement as modalities for performance feedback and argued this can help to build the team, the culture, and the capacity for quality improvement [ 50 , 51 , 52 ]. They showed, learning through performance feedback provides team members with the opportunity to learn how to work collaboratively [ 52 ], having the potential to (1) shape team culture or attitudes, (2) establish common team goals, and (3) improved understanding of performance standards [ 51 ]. However, in one study, it was noted that the effect of team performance feedback on intentions to improve performance was hindered by a poor understanding of how the team could use the feedback and how the feedback was perceived [ 51 ].

Supportive Team Behaviour

Eleven studies [ 26 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ] explored how the relationship between supportive team behaviour, the ability to anticipate other team members’ needs through accurate knowledge about their roles and responsibilities [ 7 ], and team performance, complement each other (Table 3 ). Teams with strong group identity, communication and structural cohesion mitigated the adverse consequences of team conflict and collective team failure [ 53 , 56 , 60 , 61 , 63 ], Relationship conflict within teams has negative consequences on task performance [ 57 , 59 ]. Task conflict has positive impacts on team performance in teams exhibiting high levels of openness and emotional stability [ 54 , 55 , 57 ]. Members within teams that engage in more cooperative behaviours become more efficient, effective, and viable [ 55 , 56 , 60 , 61 ]. Supportive team behaviour has additional positive effects on team performance when in combination with performance monitoring [ 26 ].

Team Orientation, Organisational Context and Adaptability

Team orientation, organisational context and adaptability as key features of team performance outcomes were explored in twenty-one studies (Table 4 ) [ 38 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 ]. Team orientation describes how members in teams learn, store, use, and coordinate their knowledge to accomplish team and organisational goals [ 76 ]. Team communication and cohesion were found to be key to collaborative work within teams to enhance team performance [ 63 , 68 , 69 , 75 , 80 ]. Functional diversity within teams had varying implications for team processes and performance depending on how this was utilised [ 83 ]. Specifically, intrapersonal functional diversity—where each member’s experience is distributed over many functional domains (operations, logistics, leadership), rather than focused on one specific functional area—was positively associated with information sharing and collective group performance [ 68 , 83 ]. The right processes and team culture in an organisation promote team commitment [ 37 ]. Organisational context influences team effectiveness, both directly and by determining the initial conditions that promote effective team functioning [ 84 ].

A relationship exists between team performance and measures of demographic similarity; described as the team’s agreeableness, self-efficacy and creativity [ 73 , 75 , 83 ], and demographic diversity of age and sex [ 65 , 74 ], In individuals low on self-efficacy and agreeableness, team climates encouraging exploitation and exploration respectively deliver increasing performance and creative benefits. When team encouragement for exploitation—treating someone unfairly in order to benefit from their work—increases, the returns on such encouragement diminish, and individuals with high levels of self-efficacy and agreeableness show less additional performance and creative returns [ 72 , 73 ]. Age, job tenure and performance dissimilarity are also associated with lower team performance as broader contextual factors in the social world are potential obstacles to effective team functioning [ 65 , 74 ].

This systematic scoping review identified four key variables that were associated with team function and performance across a variety of industries; (i) leadership styles [ 17 ], (ii) supportive team behaviour [ 18 ], (iii) communication, and (iv) performance feedback [ 20 ]. High-performance teams may wish to consider prioritising these variables to improve health and performance outcomes. However, this should be done with caution given limited evidence was identified in sport relative to these factors. Team function and performance in the context of support teams in high-performance sport may be better enhanced if we first work towards understanding the behaviour of those four key variables relative to each other in the broader sports team [ 85 ].

Leadership Styles Influence Team Cohesion and Performance

In sport, leadership behaviour is not just important for individual players; it is important for the team as a whole as it establishes an interpersonal environment characterised by support, respect, trust and appreciation of staff and players [ 86 ], which ultimately have a positive influence on team cohesion and performance [ 86 ]. Leadership styles that promote back up behaviour were suggested to enhance team cohesion. Highly cohesive teams worked together more efficiently and, consequently, performed better than less cohesive teams [ 39 ]. It is well established that leadership serves as a critical input for influencing group processes and output, and that leaders can shape team members’ attitudes, beliefs, and values [ 44 ]. Sports psychology research supports the view that leadership behaviours are associated with higher levels of motivation and performance [ 87 , 88 , 89 ], increased well-being [ 90 ], and increased task/team cohesion [ 87 ]. A study of leadership styles of football coaches indicated that leadership behaviours that communicated a clear and positive vision of the future appeared to reduce the risk of severe injuries by 29%-40% [ 86 ]. This is in line with the idea that transformational leaders develop an image of the future of their organisation and communicate that vision to their subordinates. In contrast, leadership that does not promote supporting behaviour and adaptability might risk insufficient collaboration within the team, poor decision-making and high stress. This is likely to lead to the team underperforming [ 11 ].

Our findings demonstrate that charismatic leadership has positive effects on team performance [ 39 ]. This is contrary to the evidence supporting this style of leadership within the sport setting. In a recent study in the sport of football [ 86 ], no correlation was found between charismatic leadership and injury rates or players’ availability. It is incumbent on the leader to establish positive rapport across the team as this is an important determinant of team performance and effectiveness [ 46 ].

Team Communication and Feedback Influence How a Team May Function

Open communication and feedback about both strengths and weaknesses were identified as a characteristic of well-performing teams, and poor communication was a marker of dysfunctional relationships [ 91 ]. When teams of multidisciplinary practitioners adopt this teamwork approach, they have been described as an ‘interdisciplinary team’, differentiated by their integration of knowledge and collaborative behaviours beyond that seen in ‘multidisciplinary teams’, where individuals work towards their own goals with limited interaction [ 84 , 92 ]. This may be explained by the mechanism through which teams collectively encode, store, and retrieve knowledge; described as transactive memory systems (TMS). TMS facilitates team shared knowledge and communication by developing a structure and organisation [ 64 , 67 , 69 , 77 , 79 , 80 , 81 ], and supporting the development, integration and change of knowledge and its content [ 79 ].

Communication is considered an important mediator of performance in team sports [ 93 ]. This notion is supported by work which highlights the importance of distributed decision-making in groups of people [ 94 ], and in fact, a recent study in the sport of football concluded that the quality of communication within a team was associated with both injury rates and player availability [ 91 ]. Teams with high internal communication quality had lower injury rates and higher player availability than teams with low communication quality [ 91 ]. Low communication quality between the head coach and the medical team was significantly associated with the injury rate; such teams had a 6%–7% lower player availability at training and matches and a 50% higher injury burden, compared with teams with moderate or high communication quality [ 91 ]. High quality communication between individuals in different roles is likely to promote good collaborations and facilitate the benefits derived from multiple perspectives in informed decisions, for instance, return to play decision or major decisions regarding the well-being of players [ 2 , 91 ].

Low-quality communication is likely to increase the risk of misunderstandings and promote one-sided decision-making and high stress, which in the long run might contribute to the risk of injuries [ 11 , 91 ]. Without effective communication and feedback, it is difficult to modify individual training plans (e.g. training load and other environment considerations like training surface) according to athlete age, position and medical history. Good communication, management and training restrictions can assist players to continue playing and performing throughout the season without exacerbating the injury [ 91 ]. The tendency to weight negative information more heavily than positive information during feedback processes could help account for the asymmetrical effects that negative (as opposed to positive) feedback has on group members' implicit performance [ 50 ]. Feedback strongly influences emotional reactions, which in turn affect employees' attitudes and role behaviours. Therefore, leaders may be better off framing their feedback to subordinates in a positive rather than a negative manner as this comes with increased employee commitment and organisational citizenship behaviour [ 95 ]. Considering teamwork factors that have been demonstrated to shape outcomes of teamwork in organisations outside of sport provides leverage points for teams to improve team function [ 25 ].

Team Culture May Mitigate Against Consequences of Team Conflict

Team culture—a shared set of values that inform a group’s behaviour—is considered one of the most prominent contributors to the success of a sporting organisation [ 96 , 97 ]. Teams with strong team culture mitigate the adverse consequences of team conflict and collective team failure [ 53 , 56 , 60 , 61 , 63 ] as it facilitates supportive behaviour and accountability by having clear purpose, well-defined roles and organisational policies [ 10 , 98 ]. In the sport setting, there are established hierarchies based around teamwork [ 2 ]. The organisational culture and climate of elite sport have been described as ‘rife’ with culturally-driven challenges that include interdepartmental communication problems, coach-athlete conflict, interference from owners, negative reporting in the media and staff being required to continually justify how their input impacts performance [ 23 ]. Sports teams that foster acceptance of group goals, promote communication and positive conflict had a positive relationship with team cohesion [ 99 ]. Teams who are able to address conflict directly are better able to develop an open constructive atmosphere and forge a stronger team identity [ 100 ]. However, HPT may exhibit high levels of team conflict, particularly within high pressure environments like that in elite sport [ 23 ] which can interfere with effective team performance [ 101 ]. When team members’ perceptions of their individual role within the team are in alignment with how other team members perceive their roles, HPT can avoid high levels of team conflict and exhibit better team performance [ 101 ].

Bias, Limitations and Future Research

Within our systematic scoping review, we identified commonly interchangeable use of terminology which makes pooling and summarising the results across industries and domains difficult. The studies identified displayed a publication bias towards cross-sectional studies. Such study designs are unable to assess the dynamic nature of working in teams. Teams are complex, dynamic systems that ‘adapt’ to new knowledge, relationships, external events and environment constraints among many other potential inputs. It is therefore important to carefully consider optimal study designs when examining team behaviours and their consequences [ 7 ] through certain study designs. Future research to agree on a taxonomy of definitions will enable research in this area to be applied to a sporting context and compared across investigations. An expected limitation of this review was the lack of existing research that satisfied the search criteria for data extraction. To minimise this limitation, we searched a common array of academic research databases leading to a sensitive search strategy which identified many false positives based on the inclusion criteria. No studies identified in this systematic scoping review investigated causal relationships. Future research investigating whether certain inputs or process improve team function may benefit from utilising causal inference methodology.

We concede this review has explored the effect of support team-teamwork/team effectiveness/team function on injury incidence and availability of athletes; however, its effect on athlete or playing team sporting performance has not been commensurately discussed. To the knowledge of the researchers, no evidence linking support team-team work to individual or playing team sporting performance exists. If we are to consider, however, increased athlete availability increases training opportunity, and that the people in the broader team environment can affect competition performance in athletes [ 102 , 103 , 104 ], it is reasonable to assume support team-teamwork/team effectiveness/team function affects athlete or playing team sporting performance similarly to how it affects athlete injury incidence and availability.

Across various sectors, we identified that improved team function and performance are associated with leadership, supportive team behaviour, communication, and performance feedback. In the context of complex sporting organisations where leaders must respond to multiple stakeholders and meet performance goals across multiple dimensions of effectiveness, addressing the reported challenges and considering the importance of organisational commitment to team development can help ensure that team objectives are effectively designed, delivered, and sustained. While the evidence obtained in this literature review was unable to elicit causal relationships between these factors and enhanced sport performance, it provides a point at which high-performance sport support teams can commence their investigation and interventions to improve team function and performance. This review will pave the way for future research; however, no agreement currently exists on terminology and definitions for performance outcomes to support performance analyses of teamwork and to establish if a performance support team that works effectively will enable better health and performance outcomes for their athletes/sport team. It is widely acknowledged that the dynamics of team function is important for outcomes in high-performance sport, yet there is a dearth of evidence to provide guidance in the high-performance sport context; hence, we have explored team work in alternate sectors. This inequality between real-world need and the available evidence should shape future research to work towards examining team effectiveness related to achieving both health and performance outcomes in elite sport.

Availability of data and materials

All relevant data are included within this article.

Abbreviations

High-performance team

Transactive memory systems

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Salcinovic, B., Drew, M., Dijkstra, P. et al. Factors Influencing Team Performance: What Can Support Teams in High-Performance Sport Learn from Other Industries? A Systematic Scoping Review. Sports Med - Open 8 , 25 (2022). https://doi.org/10.1186/s40798-021-00406-7

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Article contents

Team building and group cohesion in the context of sport and performance psychology.

• Mark Eys Mark Eys Professor, Kinesiology/Physical Education and Psychology, Wilfrid Laurier University
•  and  Jeemin Kim Jeemin Kim Wilfrid Laurier University
• https://doi.org/10.1093/acrefore/9780190236557.013.186
• Published online: 28 June 2017

Over the past 30 years, researchers studying group dynamics in sport have provided insight regarding the importance of considering a team’s environment, structure, and processes for its effective functioning. An emergent property resulting from activities within the group is cohesion. Cohesion is a dynamic property reflecting members’ perceptions of the unity and personal attractions to task and social objectives of the group. Generally speaking, cohesion remains a highly valued group property, and a strong body of evidence exists to support positive links to important individual and group outcomes such as adherence and team performance.

Given the importance attached to cohesion and other group variables for sport teams, coaches and athletes often attempt to engage in activities that facilitate group functioning. Team building is a specific approach designed to facilitate team effectiveness and individual members’ perceptions of their group. Cohesion has been the primary target of team-building interventions in sport, although recent work on team-building outcomes suggested that the effects of these interventions on cohesion may be limited. The most effective team-building approaches include a goal setting protocol, last at least two weeks in duration, and target a variety of outcomes in addition to cohesion, including individual cognitions and team performance. There is a clear need to identify a team’s requirements prior to intervening (i.e., a targeted approach), consider a variety of approaches to team building, and investigate the effects of team building via more stringent research methods.

• group dynamics
• goal setting

Introduction

The 2016 Football (Soccer) European Championships were notable for the emergence and success of two smaller countries (i.e., Iceland and Wales) competing among the giants of the sport. Commentaries about their accomplishments quite often focused on the teams’ ability to work together as a cohesive unit to overcome any deficiencies in individual talent. For example, in a preview of the Icelandic team leading up to the tournament, the magazine WorldSoccer noted:

Since Lars Lagerback took over as coach in October 2011 he has stuck to the 4-4-2 system that he favoured for so many years with his native Sweden. With his current team, the emphasis has very much been on cohesion and team spirit, both in defence and attack … . The old saying that “a chain is never stronger than its weakest link” is acknowledged by everybody on the team. They all accept that each of them has to give 100 per cent, every game; 95 per cent for a side like Iceland is not enough on the big stage. No one is too big for the team. (Hallgrimsson, 2016 , paras. 13 and 15)

The importance of group cohesion is shared among many performance contexts including sport (e.g., Eys, Loughead, Bray, & Carron, 2009 ), business (e.g., Tekleab, Karaca, Quigley, & Tsang, 2016 ), military (e.g., Kanesarajah, Waller, Zheng, & Dobson, 2016 ), and music (e.g., Dobson & Gaunt, 2015 ). As a result, researchers and practitioners working with performance groups also attempt to facilitate perceptions of cohesion through the process of team building. This article focuses on the physical activity context and will provide an overview of the definition and conceptualization of cohesion, identify measurement tools used to assess athletes’ perceptions of cohesion in sport, highlight the extant literature supporting the importance of cohesion in this context, and discuss the suggestions and protocols that are considered to build high functioning teams within the sport environment (i.e., team building).

Definition and Conceptual Model of Cohesion

Generally speaking, cohesion represents the strength of the bonds among group members or, more informally, the degree to which individuals stick together (Carron & Eys, 2012 ). This group property has been the subject of considerable research over the past 60 years and definitions have indicated differing approaches to understanding cohesion. For example, Gross and Martin ( 1952 ) suggested that cohesion represents the collective resistance to disruption of the group (i.e., the degree to which the group can withstand outside pressures or unfavorable events). Alternatively, Festinger, Schachter, and Back ( 1963 ) defined cohesion as the sum of all the forces that cause members to be attracted to, and remain in, the group, and also considered these forces to be related to task and social aspects of the environment.

In sport and exercise research, the most accepted definition of cohesion was provided by Carron, Brawley, and Widmeyer ( 1998 ): “a dynamic process which is reflected in the tendency for a group to stick together and remain united in the pursuit of its instrumental objectives and/or for the satisfaction of member affective needs” (p. 213). This definition implies several characteristics of cohesion that include an ability to change over the span of group development (i.e., dynamic), a focus on both task (i.e., instrumental objectives) and social aspects of the group (i.e., member affective needs), and, relatedly, an assumption that it is multidimensional.

With respect to the latter points, and following from the varied approaches of earlier cohesion research, Carron, Widmeyer, and Brawley ( 1985 ) proposed a four dimension conceptual model that encompasses two different perceptual orientations (i.e., individuals’ perceptions of their own attractions to the group as well their perceptions about the degree to which the group is integrated) regarding two broad aspects of the group environment (i.e., task and social concerns). In combination, the four dimensions represent individuals’ perceptions of their (a) attractions to task aspects of the group (ATG-T), (b) attractions to social aspects of the group (ATG-S), (c) group’s integration regarding task objectives (GI-T), and (d) group’s integration regarding social objectives (GI-S).

Another interesting aspect regarding the concept of cohesion relates to the dynamism of individuals’ perceptions of their group. McEwan and Beauchamp ( 2014 ) proposed that cohesion is an emergent state resulting from (and influencing) other behavioral processes in which the team engages (e.g., teamwork processes). In this sense, cohesion is proposed to be an outcome/antecedent of several group processes (as opposed to being a process unto itself). Regardless, it is interesting to consider whether the various dimensions of cohesion differ with respect to the speed and/or level with which they initially emerge within a group and their ongoing stability. There is some support in the extant literature to suggest that all dimensions of group cohesion do not progress in lockstep. Arrow, Poole, Henry, Wheelen, and Moreland ( 2004 ) proposed that group members’ attractions to their group have elements that develop at different speeds. More global attractions to the group are proposed to develop quickly while more specific interpersonal attractions (i.e., among group members) need more time to be fostered.

In a physical activity context, Dunlop, Falk, and Beauchamp ( 2012 ) tracked 46 group exercise classes and assessed participants’ perceptions of the four dimensions of cohesion during the 2nd, 5th, and 8th week of the session. They found that perceptions of task cohesion remained relatively stable across exercise sessions, while social cohesion perceptions were more variable over those time points. The researchers suggested that their results had implications toward group interventions in exercise (i.e., opportunities to facilitate social connections within the classes) and provided support that cohesion perceptions are malleable. This result (i.e., greater stability for task cohesion perceptions vs. social cohesion) is consistent with Leeson and Fletcher ( 2005 ), who examined cohesion perceptions of 219 elite female netball players across four time points in a competitive season.

Measurement of Cohesion in Sport

The body of knowledge pertaining to cohesion in sport has been aided by several attempts to measure athletes’ perceptions of this group property. These attempts include the Sport Cohesiveness Questionnaire (Martens, Landers, & Loy, 1972 ), the Multidimensional Sport Cohesion Inventory (Yukelson, Weinberg, & Jackson, 1984 ), and the Group Environment Questionnaire (Carron, Brawley, & Widmeyer, 2002 ; Carron et al., 1985 ). The Group Environment Questionnaire (GEQ) has received the most attention and is the operationalization of the four dimensions of cohesion outlined in the previous section. Specifically, the GEQ is an 18-item measure assessing athletes’ perceptions of their attractions to social (5 items) and task (4 items) aspects of the group, as well as their perceptions of how integrated their group is from both social (4 items) and task (5 items) perspectives. Over time, evidence has been provided regarding the validity and reliability of responses to this assessment tool (see Carron et al., 1998 ; Carron et al., 2002 , for summaries), though certain limitations have been identified. For example, Eys, Carron, Bray, and Brawley ( 2007 ) noted that the strategy of using both positively and negatively worded items might create problems for the internal consistency of certain dimensions.

Furthermore, as Carron et al. ( 2002 ) noted, “The GEQ was specifically developed, its psychometric properties investigated, and norms established with recreational and competitive sport teams composed of North American female and male athletes between the ages of approximately 18 to 30 years” (p. 39) and encouraged careful consideration of the context specificity of the questionnaire. To this end, researchers have translated and adapted the GEQ to ensure they had a relevant measure of cohesion for their population. As just a few examples, Heuzé and Fontayne ( 2002 ) used the GEQ as the basis for a French language cohesion questionnaire (Questionnaire sur l’Ambiance du Group), while Estabrooks and Carron ( 2000 ) adapted the measure for use in an exercise class context (Physical Activity Group Environment Questionnaire).

More recently, efforts have been made to examine cohesion in younger athletes including youth (approximately 12 to 17 years of age; Youth Sport Environment Questionnaire; Eys et al., 2009 ) and children (approximately 9 to 12 years of age; Child Sport Cohesion Questionnaire; Martin, Carron, Eys, & Loughead, 2012 ). Eys and colleagues ( 2009 ) noted several advantages of developing age-appropriate cohesion assessment tools including increased readability. Furthermore, for both questionnaires, the researchers found evidence that younger populations did not distinguish between group integration perceptions and their attractions to the group, but rather viewed their group more globally with respect to task and social cohesion (two dimensions vs. four dimensions). Overall, the efforts of researchers to develop appropriate measures of cohesion have led to a large body of literature within sport. The following section briefly highlights this information.

Research on Cohesion in Sport

Without question, cohesion has been the most heavily researched group dynamics concept in sport psychology. The research questions have tackled a variety of issues including the relationship of cohesion with individual cognition/affect/behavior (e.g., individual effort), other features of the group environment (e.g., motivational climate) and structure (e.g., leadership, roles), and performance. In the following sections, examples of this research are provided to highlight the importance of this emergent state, though we note more extensive coverage can be found in other texts (e.g., Carron & Eys, 2012 ).

Cohesion and the Individual Athlete

Research linking perceptions of cohesion to important individual correlates has been extensive and includes cognitive, affective, and behavioral variables. For example, from a cognitive perspective, Shapcott and Carron ( 2010 ) found that the attributions athletes make regarding team performance were related to task cohesion. As one specific aspect, athletes who had higher perceptions of task cohesion attributed team failures to causes that were controllable and changeable (a more positive attributional approach). Bruner, Eys, Wilson, and Côté ( 2014 ) undertook a study to examine cohesion as it relates to positive youth development. Their findings positively linked both task and social cohesion to the development of personal and social skills, initiative, cognitive skills, and goal setting practices.

Affective variables have also been considered and, for the most part, the links have been beneficial. Several studies have examined the association between team cohesion and individual athlete satisfaction. Illustrative of this relationship, Spink, Nickel, Wilson, and Odnokon ( 2005 ) found that athletes’ perceptions of how integrated their team was regarding task aspects (GI-T) were positively related to their satisfaction with the group’s contributions and coordination. More recently, Wolf, Eys, and Kleinert ( 2015 ) found that greater cohesion was predictive of athletes’ facilitative interpretations of their precompetitive state anxiety symptoms over and above the contributions of other important variables (e.g., trait anxiety).

Finally, the perceptions individuals hold regarding the cohesion of their team are believed to influence their behaviors. Earlier research provides evidence of positive links with key variables in the sport environment, including adherence (e.g., returning the following season to one’s team; Spink, Wilson, & Odnokon, 2010 ), sacrifice behaviors (e.g., putting aside personal goals for team goals; Prapavessis & Carron, 1997 ), and social loafing (McKnight, Williams, & Widmeyer, 1991 ). Furthermore, Bruner, Boardley, and Côté ( 2014 ) found that perceptions of task and social cohesion played differential mediating roles between social identity and both pro- and anti-social behaviors toward teammates and opponents. For example, Bruner and colleagues found that stronger perceptions of social identity expressed by athletes were positively related to task cohesion that, in turn, were related to greater prosocial behaviors and lesser antisocial behaviors toward teammates. In contrast, social cohesion perceptions promoted by stronger social identities were predictive of more antisocial behaviors toward opponents.

Cohesion and the Team Environment

Given that cohesion is an emergent group state, it is not surprising that researchers have examined it in light of other important group variables. The volume of studies is too large to cover in-depth within this article, but the existing literature highlights numerous associations with structural, leadership, and environmental variables. From a structural standpoint, greater cohesion has been positively linked with perceptions of group status and roles. For example, Jacob and Carron ( 1998 ) found that athletes perceiving higher cohesion attached less importance to status differences within their team. From a different vantage point, participants in a rugged wilderness trek perceived greater cohesion when group members had congruent perceptions of the status structure in their group (Eys, Ritchie, Little, Slade, & Oddson, 2008 ). With respect to roles, Carron and Eys ( 2012 ) summarized that cohesion and role perceptions (e.g., role ambiguity, acceptance, and performance) appear to act on each other in a reciprocal fashion, though Bosselut, McLaren, Eys, and Heuzé ( 2012 ) found that youth athletes’ perceptions of social cohesion were predictive of their subsequent perceptions of role ambiguity.

Leaders play an essential role in the emergence of cohesion within the group. The degree to which leaders (both coaches and athlete/peer leaders) demonstrate behaviors related to training and instruction, social support, and the provision of positive feedback, as well as engage their followers via a democratic style (vs. autocratic), is positively related to perceptions of cohesion (Jowett & Chaundy, 2004 ; Vincer & Loughead, 2010 ). In addition, coaches not only have responsibilities for interacting effectively with each individual athlete, they also need to act in a manner that is helpful in creating a positive motivational climate. Coaches who provide for a stronger task-involving motivational climate have athletes who perceive greater task and social cohesion (Eys, Jewitt, Evans, Wolf, Bruner, & Loughead, 2013 ; Horn, Byrd, Martin, & Young, 2012 ). In contrast, less cohesion is perceived when an ego-involving climate is promoted. On the basis of these previous findings, McLaren, Eys, and Murray ( 2015 ) conducted an intervention with youth soccer coaches to educate them about what constitutes a positive motivational climate and to provide strategies for them to use throughout the season. Compared to a control group, athletes whose coaches took part in the intervention perceived a stronger task-involving motivational climate as well as greater perceptions of cohesion by the end of the season.

Cohesion and Performance

The question pertaining to whether cohesion is linked to team performance has stretched as far back as the 1960s, with individual sets of empirical results yielding a somewhat ambiguous picture of this relationship. In an attempt to rectify this situation, Carron, Colman, Wheeler, and Stevens ( 2002 ) conducted a meta-analysis of sport studies to determine the general relationship between cohesion and performance as well as potential moderators of this relationship. Specifically, Carron and colleagues examined whether the cohesion-performance relationship differed with respect to type of cohesion (task vs. social cohesion), type of sport (interdependent vs. individual sports), gender (male vs. female), skill level and age, and the direction of the relationship using any lagged longitudinal datasets that were available (cohesion leading to performance vs. performance leading to cohesion). Overall, the researchers found that there was a moderate, positive, and significant relationship between cohesion and performance (effect size = 0.655). This particular relationship held regardless of type of cohesion/sport, skill level, or direction of the relationship. However, there was a moderating effect of gender. In essence, while still significant for males (effect size = 0.556), the positive relationship between cohesion and performance was stronger for females (effect size = 0.949). A follow-up meta-analysis (Filho, Dobersek, Gershgoren, Becker, & Tenenbaum, 2014 ), examining studies conducted between 2000 and 2010 , further supported the general positive relationship between these two variables as well as the moderating effect of gender. However, Filho and colleagues demonstrated there were some differences in the strength of the relationship based on skill level and sport type.

The finding that gender moderates the cohesion-performance relationship was discussed by the groups of researchers. Carron and Colleagues ( 2002 ) suggested that this might be important practical knowledge for coaches and sport psychology professionals to consider when working with teams. From a research perspective, Filho and Colleagues ( 2014 ) encouraged investigators to “focus on asking ‘why’ (e.g., Why do women and men differ in cohesion dynamics?) to provide explanation of the mediating mechanisms underlying gender idiosyncrasies” (p. 174). This question pertaining to why there may be gender differences was pursued in a qualitative study conducted by Eys and Colleagues ( 2015 ). These researchers interviewed 22 Canadian and German coaches who had experience coaching both male and female competitive sport teams over the course of their careers. The researchers asked coaches to comment on the findings and to offer their perspectives regarding why cohesion may be a more important group property for female teams as compared to males. While it is beyond the scope of this article to highlight the results in their totality, coaches tended to agree with the empirical results in the sense that they believed that cohesion was important for both males and females, but that there is a tendency for it to be more important in female teams. Furthermore, coaches offered interesting ideas that could form the basis for future research questions. For example, some coaches observed that the direction of the cohesion-performance relationship might differ for males and females; specifically, that cohesion may drive performance for females while performance may drive perceptions of cohesion for males. This is an interesting proposition that has not yet been tested in the previous meta-analyses. As another example, coaches also felt that there may be temporal differences in the development of cohesion. In essence, male and female teams may differ with respect to the speed that cohesion is facilitated (e.g., faster to develop in male teams).

There are a few limitations to previous research examining the cohesion-performance relationship included in the previous meta-analyses. These include a reliance on young adult populations (+18 years), cross-sectional designs, and sub-elite competitive levels. Benson, Šiška, Eys, Priklerovád, and Slepičkab ( 2016 ) sought to address some of these issues in a prospective investigation of the cohesion-performance relationship with elite Czech and Slovak Republic youth football (soccer) and handball teams. Their study included 246 athletes from 18 teams whose perceptions of cohesion were obtained at mid-season and late season along with their team’s performance. In contrast to the general tone of the extant literature suggesting that cohesion leads to performance, Benson and colleagues found evidence that performance outcomes drive perceptions of cohesion in elite youth sport teams. This finding opens up several research questions regarding this relationship across sport and the researchers encouraged continued investigation of the psychological mechanisms (i.e., mediators) and boundary conditions (i.e., moderators) of the cohesion-performance relationship. Certainly, their study had several limitations (e.g., Czech and Slovak Republic athletes only, predominantly male, limited number of sports). Regardless, their result suggesting that performance leads to cohesion in the elite youth sport environment is tantalizing within a body of research that often suggests a bi-directional relationship and/or promotes cohesion as a performance enhancing necessity.

Cohesion as a Potential Disadvantage

As noted, cohesion is believed to be a force for the good of the group. As previous sections have highlighted, cohesion is associated with several important personal, team, and leadership factors, as well as team performance. However, several researchers have cautioned that there are negative aspects to cohesion that need to be considered. This is a concern that is shared and identified by athletes as well. For example, Hardy, Eys, and Carron ( 2005 ) asked 105 intercollegiate athletes if they viewed any downsides to group cohesion and, if so, to further discuss the specific issues. Overall, 56% of the athletes queried noted that they saw potential disadvantages to high social cohesion, and 31% indicated potential problems to high task cohesion. It is important to note that many of the issues raised by the athletes appeared to be interpreted in light of an imbalance of team cohesion (i.e., high social cohesion with a relatively lower amount of task cohesion and vice versa). However, the perceived disadvantages of high social cohesion included the potential for communication problems among friends (e.g., afraid to be critical of those you are close with), challenges in fully focusing on the task at hand (e.g., social issues dominating task concerns), and the exclusion of those individuals who do not adhere to the social norms of the group. From a task perspective, the challenges included perceived increases in pressure to perform as well as decreased social and personal enjoyment.

Rovio, Eskola, Kozub, Duda, and Lintunen ( 2009 ) added further support to the suggestion that group cohesion can be problematic at times. They conducted a qualitative study with an ice-hockey team over the course of one competitive season. In this case study, the team’s performance decreased as the season progressed though group cohesion appeared to be rather resilient. They suggested that the high social cohesion present on the team might have posed some challenges. In particular, they highlighted the occurrence of several established group dynamics phenomena (i.e., pressures to conform, group polarization, groupthink) that may have led to lower standards of performance. Overall, the issues raised in the Hardy et al. ( 2005 ) and the Rovio et al. ( 2009 ) studies are in line with those raised in a review by Pescosolido and Saavedra ( 2012 ), indicating that cohesion can be yet another strong force to contend with in the group environment that can lead to a reluctance on the part of the individual to violate strong normative pressures to be a team player.

Cohesion as a Target for Intervention

Although there are specific instances in which too much cohesion could be detrimental to a team, the overwhelming evidence suggests that strong group cohesion (both task and social) is a desirable emergent state. The previous section outlining research on cohesion in sport highlighted the many positive individual and group correlates, with arguably the most important connection being the positive association between cohesion and performance. As a result, there have been many attempts designed to enhance athletes’ perceptions of cohesion in their sport teams. In particular, these attempts to improve team effectiveness (i.e., task cohesion) and enhance interpersonal relationships (i.e., social cohesion) are referred to as team building. Brawley and Paskevitch ( 1997 ) provided a specific definition of team building for physical activity contexts that described it as a “method of helping the group to (a) increase effectiveness, (b) satisfy the needs of its members, or (c) improve work conditions” (pp. 13–14).

There is evidence to suggest that cohesion has been the primary target (vs. other group constructs) for team-building interventions. In a unique study examining the origins of team building in sport, Bruner, Eys, Beauchamp, and Côté ( 2013 ) used citation network and citation path analyses to determine the influential texts and articles that have driven team-building research. Essentially, citation network analysis determines the interconnectedness of citations among a series of publications and determines the most central or influential texts. In parallel, citation path analysis links key texts over time to provide a picture of the evolution of thinking around a particular topic. As Bruner and Colleagues ( 2013 ) noted, these two analyses “hold considerable promise to enhance an understanding of [team building] in sport by identifying bodies of literature, and trends, that have shaped the field as well as identifying potential restrictions or omissions that have emerged as the field of enquiry developed” (pp. 31–32).

The major finding from Bruner and Colleagues ( 2013 ) was that the extant literature on team building in sport is largely driven by cohesion-focused research. In particular, the work conducted by Carron and colleagues (e.g., Brawley, Carron, & Widmeyer, 1987 ; Carron et al., 1985 ) was predominant throughout both the citation network and path analyses. On one hand, this result suggests the importance of cohesion as an outcome of team building. On the other hand, the result supports Bruner et al.’s ( 2013 ) cautionary statement that perhaps the field of sport psychology is too narrow with respect to its approach to team building, both in terms of topic (i.e., cohesion) and use of the extant literature (i.e., not giving due consideration to other fields such as organizational psychology). This point was reiterated by McEwan and Beauchamp ( 2014 ) in their review of teamwork processes in sport. They noted that team-building processes should move beyond solely considering cohesion and target additional teamwork behaviors such as coordination, cooperation, and communication.

Critiques concerning the narrowly focused nature of team-building processes aside, if these protocols are focused predominantly on cohesion, what can we say about the effectiveness of intervening with sport teams? Martin, Carron, and Burke ( 2009 ) conducted a meta-analysis to answer this very question. Their analysis included 17 studies and 180 effect sizes emanating from the data in these investigations. Martin and colleagues found a moderate positive effect for team-building interventions when taken in totality across several dependent variables (e.g., social cohesion, task cohesion, performance, enhanced cognitions, roles, anxiety). However, follow-up moderation tests yielded several interesting findings. First, the researchers found that interventions using team goal setting had larger effects than those interventions that took a broader approach to team-building activities (i.e., targeting several components). They surmised that more positive results might be the product of fewer activities that athletes can truly focus on. Second, consistent with past research in both sport psychology and organizational psychology, interventions were less effective when they were shorter in length (i.e., less than two weeks). Third, Martin et al. ( 2009 ) found that team building was particularly effective with independent sport teams (vs. interactive sport teams), but noted that this effect may be due to greater room for developing group interaction in sports that may traditionally offer less opportunities (i.e., a ceiling effect for the interactive teams). Finally, the impact of team-building interventions on perceptions of cohesion (both task and social) were rather muted, which the researchers found interesting given that practitioners and researchers often use team building with the hopes of increasing group cohesion. Certainly, this is a finding with implications that require future research to disentangle and consider regarding the process and targeted outcomes of team building. In the following section, information pertaining to the team-building protocols used in sport are described in further detail.

Team Building Protocols in Sport and Exercise

Given the complex nature of group dynamics and team development, a wide variety of factors must be considered during the creation and delivery of team-building protocols. Thus, researchers and practitioners have taken numerous approaches that have varied on their conceptual basis, delivery medium, types of activities, and outcome measures. This section provides an overview of such protocols and approaches that have been undertaken in the team-building literature in sport and exercise.

At a general level, team-building protocols have largely been categorized as either direct or indirect , based on the role that the interventionist plays in the delivery of the team-building program (Loughead & Bloom, 2013 ). In indirect interventions, the sport psychologist works with the coaching staff to create a team-building program and develop specific strategies, which are subsequently delivered and implemented with the athletes. In other words, the sport psychologist acts as a consultant for the coaching staff, who has the direct responsibility to implement the team-building protocols with their athletes (Loughead & Bloom, 2013 ). On the other hand, direct interventions involve the sport psychologist working directly with all members of the team (i.e., coaching staff and athletes). To this end, the sport psychologist, coaching staff, and athletes share the responsibility of creating and implementing the team-building programs. Thus, the sport psychologist is in direct contact with the athletes during program development and delivery (Loughead & Bloom, 2013 ).

Indirect Interventions

Carron and Spink (Carron & Spink, 1993 ; Spink & Carron, 1993 ) developed and implemented an indirect team-building intervention in exercise settings. Importantly, their intervention was based on a conceptual framework that represented a linear progression of group development that included inputs, throughputs, and outputs (Carron, Spink, & Prapavessis, 1997 ). Specifically, group environment and group structure were the two main categories of input, which influenced the throughput category of group processes . Subsequently, group processes influenced the output, which mainly pertained to the cohesiveness of the group. Each category within the framework included a specific factor that was emphasized and targeted during the team-building intervention. For example, the group environment was targeted by enhancing the group’s distinctiveness , which reflected the extent to which the group appeared unique in comparison to other groups. Group structure mainly related to the norms and positions established within the group, while group processes included interaction, communication , and sacrifices among teammates as the most salient factors. Lastly, the output category of group cohesion included the four sub-dimensions of cohesion (i.e., ATG-T, ATG-S, GI-T, GI-S).

Using this framework, Carron and Spink conducted a set of team-building intervention studies with female exercise class participants over a 13-week period (Carron & Spink, 1993 ; Spink & Carron, 1993 ). In each study, the exercise classes under the experimental condition were led by leaders who were trained to implement team-building protocols in addition to standard exercise programs, whereas leaders in the control condition provided the standard exercise programs only. Specifically, the team-building training was delivered in four stages: introductory, conceptual, practical , and intervention (for full descriptions of the stages, see Carron et al., 1997 ). In the introductory stage, the authors educated the group leaders on the benefits of group cohesion such as greater self-esteem, trust, and adherence to the program, as well as more group stability. In the conceptual stage, the framework of team building was outlined to the group leaders. In this way, the group leaders were able to decide what specific factors within the framework should be targeted in their team-building program. Based on this assessment, in the practical stage, the group leaders brainstormed strategies that would enhance the specific factor. Finally, in the intervention stage, the strategies developed in the previous stage were implemented by the group leader. The four dimensions of group cohesion, as well as satisfaction with the exercise classes, were included as outcome measures. In their results, the participants in the experimental condition showed higher perceptions of ATG-T (Carron & Spink, 1993 ; Spink & Carron, 1993 ) and satisfaction with the classes (Carron & Spink, 1993 ), as well as adherence to the classes represented by the number of dropouts and late arrivals to each class (Spink & Carron, 1993 ). These results provided initial evidence for the usefulness of indirect team-building interventions.

More recently, several sport and exercise psychology researchers extended the early work by Carron and Spink (Bruner & Spink, 2010 ; Bruner & Spink, 2011 ; Newin, Bloom, & Loughead, 2008 ). Bruner and colleagues (Bruner & Spink, 2010 ; Bruner & Spink, 2011 ) used Carron and Spink’s model to conduct team-building interventions in school-based exercise programs. Ten exercise classes with a total of 100 adolescent (13–17 years) participants were randomized into an experimental group or a control group. The exercise classes were run three times per week over a period of eight weeks (i.e., a total of 24 sessions), each lasting approximately an hour. Following Carron and Spink’s protocols, the leaders in the control group ran a standard exercise program only, while the leaders in the experimental condition were trained to conduct team-building activities in addition to the exercise program. Their results revealed that the participants in the experimental condition reported higher task cohesion (Bruner & Spink, 2010 ), group task satisfaction, and session attendance (Bruner & Spink, 2011 ), and that the five specific factors targeted in the intervention significantly improved the prediction of task cohesion (Bruner & Spink 2010 ). Thus, the findings by Bruner and colleagues extended the usefulness of Carron and Spink’s four-stage model of team building to youth populations.

In sport, Newin and Colleagues ( 2008 ) conducted a team-building program with eight youth ice hockey teams. Following Carron and Spink’s ( 1993 ) model, they educated the head coaches on the benefits of team building (i.e., introductory stage), introduced the conceptual framework (i.e., conceptual stage), and developed specific activities that were designed to be engaging and challenging their athletes’ problem-solving and teamwork skills (i.e., practical stage). Then, the coaches led five activities throughout their season, which lasted approximately 30 minutes per activity (i.e., intervention stage). The authors gathered qualitative data using pre- and post-intervention reflection forms completed by coaches, observations of the activities by members of the research team, and individual semi-structured exit interviews with the coaches following the completion of the season. Among their results, coaches reported that their athletes improved their problem-solving skills, abilities to focus and to persist through challenges, and their teamwork skills. Taken together, the recent work by Bruner and colleagues (Bruner & Spink, 2010 ; Bruner & Spink, 2011 ) and Newin et al. ( 2008 ) provide evidence that Carron and Spink’s indirect team-building interventions can be beneficial under both sport and exercise contexts.

Direct Interventions

Based on his work with coaches and athletes at Penn State University, Yukelson ( 1997 ) advocated the use of a direct service approach, where the sport psychologist is in contact with the athletes during team-building interventions. Similar to the indirect approach by Carron and Spink ( 1993 ), Yukelson’s approach consisted mainly of four stages: assessment , education, brainstorm , and implementation (Loughead & Bloom, 2013 ; Yukelson, 1997 ). In the assessment stage, the sport psychologist spends time to learn about the dynamics of the organization, including its goals, needs, norms for productivity, and team atmosphere. Then, the sport psychologist educates the team on the objectives of team building and the nature of group development. Although the brainstorm stage is equivalent to Carron and Spink’s practical stage where specific team-building strategies are developed, Yukelson’s brainstorm stage involves athletes as active participants during strategy development. Finally, the strategies are implemented in the final stage. In addition to the four stages, Yukelson also described the core components that must be included in order to build a successful team. Specifically, the team-building program must promote a shared vision that encompasses the group’s overarching goals and expectations, collaborative and synergetic teamwork as a result of role clarity and acceptance among members, and individual and mutual accountability that reflect their willingness to accept responsibility for their actions and group outcomes. Further, the team must establish a positive team culture and cohesive group atmosphere where the players put the group’s interest ahead of their personal interests, a team identity that includes the team’s distinct characteristics and the extent to which the members feel proud of their membership, and open and honest communication that allows members to freely and effectively express and exchange their feelings and thoughts. Finally, the team members must be willing to provide peer helping and social support (Yukelson, 1997 ).

Following Yukelson’s direct approach, Voight and Callaghan ( 2001 ) conducted a team-building intervention with two NCAA women’s soccer teams. The authors conducted needs assessment for both teams that involved discussions among the coaching staff and the athletes, which led to establishing two primary objectives: team unity and performance. Based on these objectives, the consultant and the team brainstormed specific strategies to be utilized in the team-building interventions, which included individual and team goal setting, pre-performance routines, and establishing re-focusing plans, among others. These interventions were delivered in a four-day workshop during pre-season for the first team, whereas weekly team-building sessions were held for the second. In their results, self-reported intervention feedback revealed that the athletes rated the team-building program generally effective for their team unity, as well as individual and team performance.

More recently, a particular form of team-building activity that involves enhancing mutual understanding among team members has gained research attention (Dunn & Holt, 2003 , 2004 ; Holt & Dunn, 2006 ; Pain & Harwood, 2009 ). According to Crace and Hardy ( 1997 ), team functioning can be improved when individual members go beyond understanding their own values and are able to recognize other members’ values, needs, and strengths. Similarly, Yukelson ( 1997 ) advocated the promotion of mutual understanding among teammates by open and honest communication practices. Building on this approach, Holt and Dunn delivered a pair of personal disclosure mutual sharing (PDMS) interventions, one with a male intercollegiate ice hockey team (Dunn & Holt, 2004 ) and another with a female high performance soccer team (Holt & Dunn, 2006 ). Both teams had qualified to participate in the national championship tournament at the time of the interventions. Specifically, prior to their departure to the national championship tournament, all athletes were asked to prepare a story that was personally significant in their sporting or non-sporting life. Then, the sport psychologist conducted a formal team meeting with the athletes the day before their first game of the tournament, where each athlete shared their stories with their teammates (for detailed descriptions of the intervention, see Holt & Dunn, 2006 ). Following the end of the season, the athletes were invited to participate in semi-structured interviews. Two separate inductive analyses of the interview data revealed that the PDMS intervention had numerous benefits that ranged from understanding self and others, to an enhanced sense of closeness and willingness to play for each other, and to feeling extremely confident in their abilities as a team (Dunn & Holt, 2004 ; Holt & Dunn, 2006 ). These results support the use of PDMS interventions, particularly with an elite group of performers who may benefit from maximizing their group functioning prior to entering a critical performance event.

Despite the encouraging results of the PDMS interventions, Holt and Dunn ( 2006 ) commented that the intervention may not be as useful at other stages of the season where the athletes’ emotional intensity and commitment are not as high, such as mid-season. As such, Pain and Harwood ( 2009 ) took a slightly different approach in their mutual sharing intervention, which involved four weekly team meetings mid-season rather than a single meeting prior to a championship tournament. Further, each meeting involved open team discussions among coaches and athletes regarding various factors related to their team functioning instead of sharing personal stories. The authors collected weekly survey data from the start to the end of the season that captured the athletes’ perceptions of their team environment and performance. Their results suggested that the athletes reported increased social cohesion, trust and confidence in teammates, as well as perceptions of team performance as a result of the intervention. Taken together, although preliminary, research evidence supports the effectiveness of team-building interventions that involve enhancing mutual understanding among the team members. More research is warranted in this regard to establish a stronger base of empirical support and to understand the various contextual factors (e.g., gender, competition level, timing of the season, length of the intervention) that may influence its effectiveness.

Team Goal Setting Approach

Although a sport psychologist may have a long list of team-building strategies to choose from, one particular strategy that seems to have the strongest empirical support is team goal setting. In fact, Martin et al.’s ( 2009 ) meta-analysis of 17 sport team-building interventions revealed that team goal setting was not only one of the most popular strategies employed, it was also one of the most effective strategies.

Based on the early work by Widmeyer and Ducharme ( 1997 ), Eys, Patterson, Loughead, and Carron ( 2006 ) introduced a three-stage team goal setting program. The program starts in stage one by explaining the rationale of the team goal setting to the athletes. Then, the athletes collectively set their team goals, following a sequence of activities that involve breaking down broad, long-term goals into more specific, short-term goals that are more readily achievable by the athletes. Specifically, the athletes first set long-term (e.g., high team standing at the end of the season) and short-term (e.g., winning three out of the next four games) outcome goals. Based on these goals, each individual athlete is then asked to determine specific performance targets (e.g., number of rebounds per game) that must be achieved in order to meet their team goals. These targets are then discussed among a subgroup of three to five players, which are then further discussed and agreed upon the team as a whole. In stage two, these performance targets are monitored on a game-by-game basis, which may involve coach feedback and/or posting the relevant statistics in a locker room. In the final stage, the sport psychologist provides ongoing feedback to the team, and the team can collectively adjust and modify their goals as needed.

An example of a team goal setting program based on the framework by Eys et al. ( 2006 ) was conducted by Senécal, Loughead, and Bloom ( 2008 ) with female high school basketball teams. In their study, eight teams with a total of 86 players were randomly assigned to either an experimental or a control condition. The experimental group was assigned the team goal-setting program described by Eys et al. over a 5-month season, whereas the teams in the control group completed measures of cohesion twice during the season without the team goal setting program. Their results showed that the teams in the experimental condition reported significantly higher perceptions of cohesion on all four dimensions than the control group at the end of the season, a difference that was not observed at the start of the season. A more in-depth analysis of their data showed that the experimental group did not change in their perceptions of cohesion over the course of their season, while the control group significantly decreased their perceptions of cohesion over the course of the season, which was attributed to a ceiling effect due to high levels of cohesion in the beginning of the season (Senécal et al., 2008 ). Thus, it may be concluded that a team goal setting intervention could be useful in maintaining the team’s levels of cohesion over the course of a season, which may naturally decrease otherwise. Similar to other types of team-building interventions, more research studies under various contextual elements (e.g., gender, sport, competition levels) are needed to establish a more solid basis of empirical support and external validity.

Limitations and Future Directions for Team-Building Research

While the team-building literature in sport and exercise has established useful protocols and showed some promising results in enhancing the quality of team functioning, it is also worthwhile to consider several limitations in the current literature as well as directions for future research. First, the most fundamental need within the team-building literature is that more empirical evidence is needed to support the use of team-building protocols with a variety of performance groups. For instance, Martin et al.’s ( 2009 ) meta-analysis of team-building interventions in sport was only able to identify 17 independent studies for review. Although Bruner et al.’s ( 2013 ) recent citation network and path analyses of the team-building literature identified 118 relevant articles, their review included books and book chapters, as well as populations outside sport.

Second, there is clear evidence that most team-building programs in sport have largely focused on group cohesion as an outcome variable (Martin et al., 2009 ). While cohesiveness of a group is an important variable for assessing and improving team functioning, and research based on cohesion has provided fruitful information, this overemphasis on cohesion “suggests that research conducted within the area of team building in sport is relatively narrow” (Bruner et al., 2013 , p. 37), possibly overlooking other important individual (e.g., performance, confidence, anxiety) and team (e.g., role ambiguity, role clarity, collective efficacy) factors that may be affected by team-building interventions. McEwan and Beauchamp ( 2014 ) described in their conceptual framework of teamwork that team-building interventions may benefit from a more process-oriented approach where observable teamwork-related behaviors (e.g., goal setting, member interactions, performance monitoring) are targeted, which could “improve team functioning and effectiveness, with increased cohesion emerging over time as a by-product [emphasis added]” (p. 244). Third, in relation to the second point, future research studies may benefit from employing a more tailored approach. That is, rather than assuming that team functioning will be improved upon increased perceptions of cohesion (or any other variable), a sport psychologist may conduct team-by-team a-priori assessments to understand the specific needs of each team and employ relevant strategies. For instance, a team that needs to improve their communication practices may benefit from conducting formal team meetings to facilitate team discussions, whereas a team with low perceived levels of social cohesion may organize social events to promote positive relationships among team members.

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Sport psychology and performance meta-analyses: A systematic review of the literature

Roles Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

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Affiliations Department of Kinesiology and Sport Management, Texas Tech University, Lubbock, Texas, United States of America, Education Academy, Vytautas Magnus University, Kaunas, Lithuania

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Affiliation Department of Psychological Sciences, Texas Tech University, Lubbock, Texas, United States of America

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Affiliation Department of Kinesiology and Sport Management, Honors College, Texas Tech University, Lubbock, Texas, United States of America

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Affiliation Faculty of Education, Health and Well-Being, University of Wolverhampton, Walsall, West Midlands, United Kingdom

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Affiliation Division of Research & Innovation, University of Southern Queensland, Toowoomba, Queensland, Australia

• Marc Lochbaum, 
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• Peter C. Terry

• Published: February 16, 2022
• https://doi.org/10.1371/journal.pone.0263408
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Sport psychology as an academic pursuit is nearly two centuries old. An enduring goal since inception has been to understand how psychological techniques can improve athletic performance. Although much evidence exists in the form of meta-analytic reviews related to sport psychology and performance, a systematic review of these meta-analyses is absent from the literature. We aimed to synthesize the extant literature to gain insights into the overall impact of sport psychology on athletic performance. Guided by the PRISMA statement for systematic reviews, we reviewed relevant articles identified via the EBSCOhost interface. Thirty meta-analyses published between 1983 and 2021 met the inclusion criteria, covering 16 distinct sport psychology constructs. Overall, sport psychology interventions/variables hypothesized to enhance performance (e.g., cohesion, confidence, mindfulness) were shown to have a moderate beneficial effect ( d = 0.51), whereas variables hypothesized to be detrimental to performance (e.g., cognitive anxiety, depression, ego climate) had a small negative effect ( d = -0.21). The quality rating of meta-analyses did not significantly moderate the magnitude of observed effects, nor did the research design (i.e., intervention vs. correlation) of the primary studies included in the meta-analyses. Our review strengthens the evidence base for sport psychology techniques and may be of great practical value to practitioners. We provide recommendations for future research in the area.

Citation: Lochbaum M, Stoner E, Hefner T, Cooper S, Lane AM, Terry PC (2022) Sport psychology and performance meta-analyses: A systematic review of the literature. PLoS ONE 17(2): e0263408. https://doi.org/10.1371/journal.pone.0263408

Editor: Claudio Imperatori, European University of Rome, ITALY

Received: September 28, 2021; Accepted: January 18, 2022; Published: February 16, 2022

Copyright: © 2022 Lochbaum et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper.

Funding: The author(s) received no specific funding for this work.

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

Introduction

Sport performance matters. Verifying its global importance requires no more than opening a newspaper to the sports section, browsing the internet, looking at social media outlets, or scanning abundant sources of sport information. Sport psychology is an important avenue through which to better understand and improve sport performance. To date, a systematic review of published sport psychology and performance meta-analyses is absent from the literature. Given the undeniable importance of sport, the history of sport psychology in academics since 1830, and the global rise of sport psychology journals and organizations, a comprehensive systematic review of the meta-analytic literature seems overdue. Thus, we aimed to consolidate the existing literature and provide recommendations for future research.

The development of sport psychology

The history of sport psychology dates back nearly 200 years. Terry [ 1 ] cites Carl Friedrich Koch’s (1830) publication titled [in translation] Calisthenics from the Viewpoint of Dietetics and Psychology [ 2 ] as perhaps the earliest publication in the field, and multiple commentators have noted that sport psychology experiments occurred in the world’s first psychology laboratory, established by Wilhelm Wundt at the University of Leipzig in 1879 [ 1 , 3 ]. Konrad Rieger’s research on hypnosis and muscular endurance, published in 1884 [ 4 ] and Angelo Mosso’s investigations of the effects of mental fatigue on physical performance, published in 1891 [ 5 ] were other early landmarks in the development of applied sport psychology research. Following the efforts of Koch, Wundt, Rieger, and Mosso, sport psychology works appeared with increasing regularity, including Philippe Tissié’s publications in 1894 [ 6 , 7 ] on psychology and physical training, and Pierre de Coubertin’s first use of the term sport psychology in his La Psychologie du Sport paper in 1900 [ 8 ]. In short, the history of sport psychology and performance research began as early as 1830 and picked up pace in the latter part of the 19 th century. Early pioneers, who helped shape sport psychology include Wundt, recognized as the “father of experimental psychology”, Tissié, the founder of French physical education and Legion of Honor awardee in 1932, and de Coubertin who became the father of the modern Olympic movement and founder of the International Olympic Committee.

Sport psychology flourished in the early 20 th century [see 1, 3 for extensive historic details]. For instance, independent laboratories emerged in Berlin, Germany, established by Carl Diem in 1920; in St. Petersburg and Moscow, Russia, established respectively by Avksenty Puni and Piotr Roudik in 1925; and in Champaign, Illinois USA, established by Coleman Griffith, also in 1925. The period from 1950–1980 saw rapid strides in sport psychology, with Franklin Henry establishing this field of study as independent of physical education in the landscape of American and eventually global sport science and kinesiology graduate programs [ 1 ]. In addition, of great importance in the 1960s, three international sport psychology organizations were established: namely, the International Society for Sport Psychology (1965), the North American Society for the Psychology of Sport and Physical Activity (1966), and the European Federation of Sport Psychology (1969). Since that time, the Association of Applied Sport Psychology (1986), the South American Society for Sport Psychology (1986), and the Asian-South Pacific Association of Sport Psychology (1989) have also been established.

The global growth in academic sport psychology has seen a large number of specialist publications launched, including the following journals: International Journal of Sport Psychology (1970), Journal of Sport & Exercise Psychology (1979), The Sport Psychologist (1987), Journal of Applied Sport Psychology (1989), Psychology of Sport and Exercise (2000), International Journal of Sport and Exercise Psychology (2003), Journal of Clinical Sport Psychology (2007), International Review of Sport and Exercise Psychology (2008), Journal of Sport Psychology in Action (2010), Sport , Exercise , and Performance Psychology (2014), and the Asian Journal of Sport & Exercise Psychology (2021).

In turn, the growth in journal outlets has seen sport psychology publications burgeon. Indicative of the scale of the contemporary literature on sport psychology, searches completed in May 2021 within the Web of Science Core Collection, identified 1,415 publications on goal setting and sport since 1985; 5,303 publications on confidence and sport since 1961; and 3,421 publications on anxiety and sport since 1980. In addition to academic journals, several comprehensive edited textbooks have been produced detailing sport psychology developments across the world, such as Hanrahan and Andersen’s (2010) Handbook of Applied Sport Psychology [ 9 ], Schinke, McGannon, and Smith’s (2016) International Handbook of Sport Psychology [ 10 ], and Bertollo, Filho, and Terry’s (2021) Advancements in Mental Skills Training [ 11 ] to name just a few. In short, sport psychology is global in both academic study and professional practice.

Meta-analysis in sport psychology

Several meta-analysis guides, computer programs, and sport psychology domain-specific primers have been popularized in the social sciences [ 12 , 13 ]. Sport psychology academics have conducted quantitative reviews on much studied constructs since the 1980s, with the first two appearing in 1983 in the form of Feltz and Landers’ meta-analysis on mental practice [ 14 ], which included 98 articles dating from 1934, and Bond and Titus’ cross-disciplinary meta-analysis on social facilitation [ 15 ], which summarized 241 studies including Triplett’s (1898) often-cited study of social facilitation in cycling [ 16 ]. Although much meta-analytic evidence exists for various constructs in sport and exercise psychology [ 12 ] including several related to performance [ 17 ], the evidence is inconsistent. For example, two meta-analyses, both ostensibly summarizing evidence of the benefits to performance of task cohesion [ 18 , 19 ], produced very different mean effects ( d = .24 vs d = 1.00) indicating that the true benefit lies somewhere in a wide range from small to large. Thus, the lack of a reliable evidence base for the use of sport psychology techniques represents a significant gap in the knowledge base for practitioners and researchers alike. A comprehensive systematic review of all published meta-analyses in the field of sport psychology has yet to be published.

Purpose and aim

We consider this review to be both necessary and long overdue for the following reasons: (a) the extensive history of sport psychology and performance research; (b) the prior publication of many meta-analyses summarizing various aspects of sport psychology research in a piecemeal fashion [ 12 , 17 ] but not its totality; and (c) the importance of better understanding and hopefully improving sport performance via the use of interventions based on solid evidence of their efficacy. Hence, we aimed to collate and evaluate this literature in a systematic way to gain improved understanding of the impact of sport psychology variables on sport performance by construct, research design, and meta-analysis quality, to enhance practical knowledge of sport psychology techniques and identify future lines of research inquiry. By systematically reviewing all identifiable meta-analytic reviews linking sport psychology techniques with sport performance, we aimed to evaluate the strength of the evidence base underpinning sport psychology interventions.

Materials and methods

This systematic review of meta-analyses followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 20 ]. We did not register our systematic review protocol in a database. However, we specified our search strategy, inclusion criteria, data extraction, and data analyses in advance of writing our manuscript. All details of our work are available from the lead author. Concerning ethics, this systematic review received a waiver from Texas Tech University Human Subject Review Board as it concerned archival data (i.e., published meta-analyses).

Eligibility criteria

Published meta-analyses were retained for extensive examination if they met the following inclusion criteria: (a) included meta-analytic data such as mean group, between or within-group differences or correlates; (b) published prior to January 31, 2021; (c) published in a peer-reviewed journal; (d) investigated a recognized sport psychology construct; and (e) meta-analyzed data concerned with sport performance. There was no language of publication restriction. To align with our systematic review objectives, we gave much consideration to study participants and performance outcomes. Across multiple checks, all authors confirmed study eligibility. Three authors (ML, AL, and PT) completed the final inclusion assessments.

Information sources

Authors searched electronic databases, personal meta-analysis history, and checked with personal research contacts. Electronic database searches occurred in EBSCOhost with the following individual databases selected: APA PsycINFO, ERIC, Psychology and Behavioral Sciences Collection, and SPORTDiscus. An initial search concluded October 1, 2020. ML, AL, and PT rechecked the identified studies during the February–March, 2021 period, which resulted in the identification of two additional meta-analyses [ 21 , 22 ].

Search protocol

ML and ES initially conducted independent database searches. For the first search, ML used the following search terms: sport psychology with meta-analysis or quantitative review and sport and performance or sport* performance. For the second search, ES utilized a sport psychology textbook and used the chapter title terms (e.g., goal setting). In EBSCOhost, both searches used the advanced search option that provided three separate boxes for search terms such as box 1 (sport psychology), box 2 (meta-analysis), and box 3 (performance). Specific details of our search strategy were:

Search by ML:

• sport psychology, meta-analysis, sport and performance
• sport psychology, meta-analysis or quantitative review, sport* performance
• sport psychology, quantitative review, sport and performance
• sport psychology, quantitative review, sport* performance

Search by ES:

• mental practice or mental imagery or mental rehearsal and sports performance and meta-analysis
• goal setting and sports performance and meta-analysis
• anxiety and stress and sports performance and meta-analysis
• competition and sports performance and meta-analysis
• diversity and sports performance and meta-analysis
• cohesion and sports performance and meta-analysis
• imagery and sports performance and meta-analysis
• self-confidence and sports performance and meta-analysis
• concentration and sports performance and meta-analysis
• athletic injuries and sports performance and meta-analysis
• overtraining and sports performance and meta-analysis
• children and sports performance and meta-analysis

The following specific search of the EBSCOhost with SPORTDiscus, APA PsycINFO, Psychology and Behavioral Sciences Collection, and ERIC databases, returned six results from 2002–2020, of which three were included [ 18 , 19 , 23 ] and three were excluded because they were not meta-analyses.

• Box 1 cohesion
• Box 2 sports performance
• Box 3 meta-analysis

Study selection

As detailed in the PRISMA flow chart ( Fig 1 ) and the specified inclusion criteria, a thorough study selection process was used. As mentioned in the search protocol, two authors (ML and ES) engaged independently with two separate searches and then worked together to verify the selected studies. Next, AL and PT examined the selected study list for accuracy. ML, AL, and PT, whilst rating the quality of included meta-analyses, also re-examined all selected studies to verify that each met the predetermined study inclusion criteria. Throughout the study selection process, disagreements were resolved through discussion until consensus was reached.

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Data extraction process

Initially, ML, TH, and ES extracted data items 1, 2, 3 and 8 (see Data items). Subsequently, ML, AL, and PT extracted the remaining data (items 4–7, 9, 10). Checks occurred during the extraction process for potential discrepancies (e.g., checking the number of primary studies in a meta-analysis). It was unnecessary to contact any meta-analysis authors for missing information or clarification during the data extraction process because all studies reported the required information. Across the search for meta-analyses, all identified studies were reported in English. Thus, no translation software or searching out a native speaker occurred. All data extraction forms (e.g., data items and individual meta-analysis quality) are available from the first author.

To help address our main aim, we extracted the following information from each meta-analysis: (1) author(s); (2) publication year; (3) construct(s); (4) intervention based meta-analysis (yes, no, mix); (5) performance outcome(s) description; (6) number of studies for the performance outcomes; (7) participant description; (8) main findings; (9) bias correction method/results; and (10) author(s) stated conclusions. For all information sought, we coded missing information as not reported.

Individual meta-analysis quality

ML, AL, and PT independently rated the quality of individual meta-analysis on the following 25 points found in the PRISMA checklist [ 20 ]: title; abstract structured summary; introduction rationale, objectives, and protocol and registration; methods eligibility criteria, information sources, search, study selection, data collection process, data items, risk of bias of individual studies, summary measures, synthesis of results, and risk of bias across studies; results study selection, study characteristics, risk of bias within studies, results of individual studies, synthesis of results, and risk of bias across studies; discussion summary of evidence, limitations, and conclusions; and funding. All meta-analyses were rated for quality by two coders to facilitate inter-coder reliability checks, and the mean quality ratings were used in subsequent analyses. One author (PT), having completed his own ratings, received the incoming ratings from ML and AL and ran the inter-coder analysis. Two rounds of ratings occurred due to discrepancies for seven meta-analyses, mainly between ML and AL. As no objective quality categorizations (i.e., a point system for grouping meta-analyses as poor, medium, good) currently exist, each meta-analysis was allocated a quality score of up to a maximum of 25 points. All coding records are available upon request.

Planned methods of analysis

Several preplanned methods of analysis occurred. We first assessed the mean quality rating of each meta-analysis based on our 25-point PRISMA-based rating system. Next, we used a median split of quality ratings to determine whether standardized mean effects (SMDs) differed by the two formed categories, higher and lower quality meta-analyses. Meta-analysis authors reported either of two different effect size metrics (i.e., r and SMD); hence we converted all correlational effects to SMD (i.e., Cohen’s d ) values using an online effect size calculator ( www.polyu.edu.hk/mm/effectsizefaqs/calculator/calculator.html ). We interpreted the meaningfulness of effects based on Cohen’s interpretation [ 24 ] with 0.20 as small, 0.50 as medium, 0.80 as large, and 1.30 as very large. As some psychological variables associate negatively with performance (e.g., confusion [ 25 ], cognitive anxiety [ 26 ]) whereas others associate positively (e.g., cohesion [ 23 ], mental practice [ 14 ]), we grouped meta-analyses according to whether the hypothesized effect with performance was positive or negative, and summarized the overall effects separately. By doing so, we avoided a scenario whereby the demonstrated positive and negative effects canceled one another out when combined. The effect of somatic anxiety on performance, which is hypothesized to follow an inverted-U relationship, was categorized as neutral [ 35 ]. Last, we grouped the included meta-analyses according to whether the primary studies were correlational in nature or involved an intervention and summarized these two groups of meta-analyses separately.

Study characteristics

Table 1 contains extracted data from 30 meta-analyses meeting the inclusion criteria, dating from 1983 [ 14 ] to 2021 [ 21 ]. The number of primary studies within the meta-analyses ranged from three [ 27 ] to 109 [ 28 ]. In terms of the description of participants included in the meta-analyses, 13 included participants described simply as athletes, whereas other meta-analyses identified a mix of elite athletes (e.g., professional, Olympic), recreational athletes, college-aged volunteers (many from sport science departments), younger children to adolescents, and adult exercisers. Of the 30 included meta-analyses, the majority ( n = 18) were published since 2010. The decadal breakdown of meta-analyses was 1980–1989 ( n = 1 [ 14 ]), 1990–1999 ( n = 6 [ 29 – 34 ]), 2000–2009 ( n = 5 [ 23 , 25 , 26 , 35 , 36 ]), 2010–2019 ( n = 12 [ 18 , 19 , 22 , 27 , 37 – 43 , 48 ]), and 2020–2021 ( n = 6 [ 21 , 28 , 44 – 47 ]).

https://doi.org/10.1371/journal.pone.0263408.t001

As for the constructs covered, we categorized the 30 meta-analyses into the following areas: mental practice/imagery [ 14 , 29 , 30 , 42 , 46 , 47 ], anxiety [ 26 , 31 , 32 , 35 ], confidence [ 26 , 35 , 36 ], cohesion [ 18 , 19 , 23 ], goal orientation [ 22 , 44 , 48 ], mood [ 21 , 25 , 34 ], emotional intelligence [ 40 ], goal setting [ 33 ], interventions [ 37 ], mindfulness [ 27 ], music [ 28 ], neurofeedback training [ 43 ], perfectionism [ 39 ], pressure training [ 45 ], quiet eye training [ 41 ], and self-talk [ 38 ]. Multiple effects were generated from meta-analyses that included more than one construct (e.g., tension, depression, etc. [ 21 ]; anxiety and confidence [ 26 ]). In relation to whether the meta-analyses included in our review assessed the effects of a sport psychology intervention on performance or relationships between psychological constructs and performance, 13 were intervention-based, 14 were correlational, two included a mix of study types, and one included a large majority of cross-sectional studies ( Table 1 ).

A wide variety of performance outcomes across many sports was evident, such as golf putting, dart throwing, maximal strength, and juggling; or categorical outcomes such as win/loss and Olympic team selection. Given the extensive list of performance outcomes and the incomplete descriptions provided in some meta-analyses, a clear categorization or count of performance types was not possible. Sufficient to conclude, researchers utilized many performance outcomes across a wide range of team and individual sports, motor skills, and strength and aerobic tasks.

Effect size data and bias correction

To best summarize the effects, we transformed all correlations to SMD values (i.e., Cohen’s d ). Across all included meta-analyses shown in Table 2 and depicted in Fig 2 , we identified 61 effects. Having corrected for bias, effect size values were assessed for meaningfulness [ 24 ], which resulted in 15 categorized as negligible (< ±0.20), 29 as small (±0.20 to < 0.50), 13 as moderate (±0.50 to < 0.80), 2 as large (±0.80 to < 1.30), and 1 as very large (≥ 1.30).

https://doi.org/10.1371/journal.pone.0263408.g002

https://doi.org/10.1371/journal.pone.0263408.t002

Study quality rating results and summary analyses

Following our PRISMA quality ratings, intercoder reliability coefficients were initially .83 (ML, AL), .95 (ML, PT), and .90 (AL, PT), with a mean intercoder reliability coefficient of .89. To achieve improved reliability (i.e., r mean > .90), ML and AL re-examined their ratings. As a result, intercoder reliability increased to .98 (ML, AL), .96 (ML, PT), and .92 (AL, PT); a mean intercoder reliability coefficient of .95. Final quality ratings (i.e., the mean of two coders) ranged from 13 to 25 ( M = 19.03 ± 4.15). Our median split into higher ( M = 22.83 ± 1.08, range 21.5–25, n = 15) and lower ( M = 15.47 ± 2.42, range 13–20.5, n = 15) quality groups produced significant between-group differences in quality ( F 1,28 = 115.62, p < .001); hence, the median split met our intended purpose. The higher quality group of meta-analyses were published from 2015–2021 (median 2018) and the lower quality group from 1983–2014 (median 2000). It appears that meta-analysis standards have risen over the years since the PRISMA criteria were first introduced in 2009. All data for our analyses are shown in Table 2 .

Table 3 contains summary statistics with bias-corrected values used in the analyses. The overall mean effect for sport psychology constructs hypothesized to have a positive impact on performance was of moderate magnitude ( d = 0.51, 95% CI = 0.42, 0.58, n = 36). The overall mean effect for sport psychology constructs hypothesized to have a negative impact on performance was small in magnitude ( d = -0.21, 95% CI -0.31, -0.11, n = 24). In both instances, effects were larger, although not significantly so, among meta-analyses of higher quality compared to those of lower quality. Similarly, mean effects were larger but not significantly so, where reported effects in the original studies were based on interventional rather than correlational designs. This trend only applied to hypothesized positive effects because none of the original studies in the meta-analyses related to hypothesized negative effects used interventional designs.

https://doi.org/10.1371/journal.pone.0263408.t003

In this systematic review of meta-analyses, we synthesized the available evidence regarding effects of sport psychology interventions/constructs on sport performance. We aimed to consolidate the literature, evaluate the potential for meta-analysis quality to influence the results, and suggest recommendations for future research at both the single study and quantitative review stages. During the systematic review process, several meta-analysis characteristics came to light, such as the number of meta-analyses of sport psychology interventions (experimental designs) compared to those summarizing the effects of psychological constructs (correlation designs) on performance, the number of meta-analyses with exclusively athletes as participants, and constructs featuring in multiple meta-analyses, some of which (e.g., cohesion) produced very different effect size values. Thus, although our overall aim was to evaluate the strength of the evidence base for use of psychological interventions in sport, we also discuss the impact of these meta-analysis characteristics on the reliability of the evidence.

When seen collectively, results of our review are supportive of using sport psychology techniques to help improve performance and confirm that variations in psychological constructs relate to variations in performance. For constructs hypothesized to have a positive effect on performance, the mean effect strength was moderate ( d = 0.51) although there was substantial variation between constructs. For example, the beneficial effects on performance of task cohesion ( d = 1.00) and self-efficacy ( d = 0.82) are large, and the available evidence base for use of mindfulness interventions suggests a very large beneficial effect on performance ( d = 1.35). Conversely, some hypothetically beneficial effects (2 of 36; 5.6%) were in the negligible-to-small range (0.15–0.20) and most beneficial effects (19 of 36; 52.8%) were in the small-to-moderate range (0.22–0.49). It should be noted that in the world of sport, especially at the elite level, even a small beneficial effect on performance derived from a psychological intervention may prove the difference between success and failure and hence small effects may be of great practical value. To put the scale of the benefits into perspective, an authoritative and extensively cited review of healthy eating and physical activity interventions [ 49 ] produced an overall pooled effect size of 0.31 (compared to 0.51 for our study), suggesting sport psychology interventions designed to improve performance are generally more effective than interventions designed to promote healthy living.

Among hypothetically negative effects (e.g., ego climate, cognitive anxiety, depression), the mean detrimental effect was small ( d = -0.21) although again substantial variation among constructs was evident. Some hypothetically negative constructs (5 of 24; 20.8%) were found to actually provide benefits to performance, albeit in the negligible range (0.02–0.12) and only two constructs (8.3%), both from Lochbaum and colleagues’ POMS meta-analysis [ 21 ], were shown to negatively affect performance above a moderate level (depression: d = -0.64; total mood disturbance, which incorporates the depression subscale: d = -0.84). Readers should note that the POMS and its derivatives assess six specific mood dimensions rather than the mood construct more broadly, and therefore results should not be extrapolated to other dimensions of mood [ 50 ].

Mean effects were larger among higher quality than lower quality meta-analyses for both hypothetically positive ( d = 0.54 vs d = 0.45) and negative effects ( d = -0.25 vs d = 0.17), but in neither case were the differences significant. It is reasonable to assume that the true effects were derived from the higher quality meta-analyses, although our conclusions remain the same regardless of study quality. Overall, our findings provide a more rigorous evidence base for the use of sport psychology techniques by practitioners than was previously available, representing a significant contribution to knowledge. Moreover, our systematic scrutiny of 30 meta-analyses published between 1983 and 2021 has facilitated a series of recommendations to improve the quality of future investigations in the sport psychology area.

Recommendations

The development of sport psychology as an academic discipline and area of professional practice relies on using evidence and theory to guide practice. Hence, a strong evidence base for the applied work of sport psychologists is of paramount importance. Although the beneficial effects of some sport psychology techniques are small, it is important to note the larger performance benefits for other techniques, which may be extremely meaningful for applied practice. Overall, however, especially given the heterogeneity of the observed effects, it would be wise for applied practitioners to avoid overpromising the benefits of sport psychology services to clients and perhaps underdelivering as a result [ 1 ].

The results of our systematic review can be used to generate recommendations for how the profession might conduct improved research to better inform applied practice. Much of the early research in sport psychology was exploratory and potential moderating variables were not always sufficiently controlled. Terry [ 51 ] outlined this in relation to the study of mood-performance relationships, identifying that physical and skills factors will very likely exert a greater influence on performance than psychological factors. Further, type of sport (e.g., individual vs. team), duration of activity (e.g., short vs. long duration), level of competition (e.g., elite vs. recreational), and performance measure (e.g., norm-referenced vs. self-referenced) have all been implicated as potential moderators of the relationship between psychological variables and sport performance [ 51 ]. To detect the relatively subtle effects of psychological effects on performance, research designs need to be sufficiently sensitive to such potential confounds. Several specific methodological issues are worth discussing.

The first issue relates to measurement. Investigating the strength of a relationship requires the measured variables to be valid, accurate and reliable. Psychological variables in the meta-analyses we reviewed relied primarily on self-report outcome measures. The accuracy of self-report data requires detailed inner knowledge of thoughts, emotions, and behavior. Research shows that the accuracy of self-report information is subject to substantial individual differences [ 52 , 53 ]. Therefore, self-report data, at best, are an estimate of the measure. Measurement issues are especially relevant to the assessment of performance, and considerable measurement variation was evident between meta-analyses. Some performance measures were more sensitive, especially those assessing physical performance relative to what is normal for the individual performer (i.e., self-referenced performance). Hence, having multiple baseline indicators of performance increases the probability of identifying genuine performance enhancement derived from a psychological intervention [ 54 ].

A second issue relates to clarifying the rationale for how and why specific psychological variables might influence performance. A comprehensive review of prerequisites and precursors of athletic talent [ 55 ] concluded that the superiority of Olympic champions over other elite athletes is determined in part by a range of psychological variables, including high intrinsic motivation, determination, dedication, persistence, and creativity, thereby identifying performance-related variables that might benefit from a psychological intervention. Identifying variables that influence the effectiveness of interventions is a challenging but essential issue for researchers seeking to control and assess factors that might influence results [ 49 ]. A key part of this process is to use theory to propose the mechanism(s) by which an intervention might affect performance and to hypothesize how large the effect might be.

A third issue relates to the characteristics of the research participants involved. Out of convenience, it is not uncommon for researchers to use undergraduate student participants for research projects, which may bias results and restrict the generalization of findings to the population of primary interest, often elite athletes. The level of training and physical conditioning of participants will clearly influence their performance. Highly trained athletes will typically make smaller gains in performance over time than novice athletes, due to a ceiling effect (i.e., they have less room for improvement). For example, consider runner A, who takes 20 minutes to run 5km one week but 19 minutes the next week, and Runner B who takes 30 minutes one week and 25 minutes the next. If we compare the two, Runner A runs faster than Runner B on both occasions, but Runner B improved more, so whose performance was better? If we also consider Runner C, a highly trained athlete with a personal best of 14 minutes, to run 1 minute quicker the following week would almost require a world record time, which is clearly unlikely. For this runner, an improvement of a few seconds would represent an excellent performance. Evidence shows that trained, highly motivated athletes may reach performance plateaus and as such are good candidates for psychological skills training. They are less likely to make performance gains due to increased training volume and therefore the impact of psychological skills interventions may emerge more clearly. Therefore, both test-retest and cross-sectional research designs should account for individual difference variables. Further, the range of individual difference factors will be context specific; for example, individual differences in strength will be more important in a study that uses weightlifting as the performance measure than one that uses darts as the performance measure, where individual differences in skill would be more important.

A fourth factor that has not been investigated extensively relates to the variables involved in learning sport psychology techniques. Techniques such as imagery, self-talk and goal setting all require cognitive processing and as such some people will learn them faster than others [ 56 ]. Further, some people are intuitive self-taught users of, for example, mood regulation strategies such as abdominal breathing or listening to music who, if recruited to participate in a study investigating the effects of learning such techniques on performance, would respond differently to novice users. Hence, a major challenge when testing the effects of a psychological intervention is to establish suitable controls. A traditional non-treatment group offers one option, but such an approach does not consider the influence of belief effects (i.e., placebo/nocebo), which can either add or detract from the effectiveness of performance interventions [ 57 ]. If an individual believes that, an intervention will be effective, this provides a motivating effect for engagement and so performance may improve via increased effort rather than the effect of the intervention per se.

When there are positive beliefs that an intervention will work, it becomes important to distinguish belief effects from the proposed mechanism through which the intervention should be successful. Research has shown that field studies often report larger effects than laboratory studies, a finding attributed to higher motivation among participants in field studies [ 58 ]. If participants are motivated to improve, being part of an active training condition should be associated with improved performance regardless of any intervention. In a large online study of over 44,000 participants, active training in sport psychology interventions was associated with improved performance, but only marginally more than for an active control condition [ 59 ]. The study involved 4-time Olympic champion Michael Johnson narrating both the intervention and active control using motivational encouragement in both conditions. Researchers should establish not only the expected size of an effect but also to specify and assess why the intervention worked. Where researchers report performance improvement, it is fundamental to explain the proposed mechanism by which performance was enhanced and to test the extent to which the improvement can be explained by the proposed mechanism(s).

Limitations

Systematic reviews are inherently limited by the quality of the primary studies included. Our review was also limited by the quality of the meta-analyses that had summarized the primary studies. We identified the following specific limitations; (1) only 12 meta-analyses summarized primary studies that were exclusively intervention-based, (2) the lack of detail regarding control groups in the intervention meta-analyses, (3) cross-sectional and correlation-based meta-analyses by definition do not test causation, and therefore provide limited direct evidence of the efficacy of interventions, (4) the extensive array of performance measures even within a single meta-analysis, (5) the absence of mechanistic explanations for the observed effects, and (6) an absence of detail across intervention-based meta-analyses regarding number of sessions, participants’ motivation to participate, level of expertise, and how the intervention was delivered. To ameliorate these concerns, we included a quality rating for all included meta-analyses. Having created higher and lower quality groups using a median split of quality ratings, we showed that effects were larger, although not significantly so, in the higher quality group of meta-analyses, all of which were published since 2015.

Conclusions

Journals are full of studies that investigate relationships between psychological variables and sport performance. Since 1983, researchers have utilized meta-analytic methods to summarize these single studies, and the pace is accelerating, with six relevant meta-analyses published since 2020. Unquestionably, sport psychology and performance research is fraught with limitations related to unsophisticated experimental designs. In our aggregation of the effect size values, most were small-to-moderate in meaningfulness with a handful of large values. Whether these moderate and large values could be replicated using more sophisticated research designs is unknown. We encourage use of improved research designs, at the minimum the use of control conditions. Likewise, we encourage researchers to adhere to meta-analytic guidelines such as PRISMA and for journals to insist on such adherence as a prerequisite for the acceptance of reviews. Although such guidelines can appear as a ‘painting by numbers’ approach, while reviewing the meta-analyses, we encountered difficulty in assessing and finding pertinent information for our study characteristics and quality ratings. In conclusion, much research exists in the form of quantitative reviews of studies published since 1934, almost 100 years after the very first publication about sport psychology and performance [ 2 ]. Sport psychology is now truly global in terms of academic pursuits and professional practice and the need for best practice information plus a strong evidence base for the efficacy of interventions is paramount. We should strive as a profession to research and provide best practices to athletes and the general community of those seeking performance improvements.

Supporting information

S1 checklist..

https://doi.org/10.1371/journal.pone.0263408.s001

Acknowledgments

We acknowledge the work of all academics since Koch in 1830 [ 2 ] for their efforts to research and promote the practice of applied sport psychology.

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• Published: 15 August 2013

A systematic review of the psychological and social benefits of participation in sport for children and adolescents: informing development of a conceptual model of health through sport

• Rochelle M Eime 1 , 2 ,
• Janet A Young 1 ,
• Jack T Harvey 2 ,
• Melanie J Charity 1 , 2 &
• Warren R Payne 1  

International Journal of Behavioral Nutrition and Physical Activity volume  10 , Article number:  98 ( 2013 ) Cite this article

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There are specific guidelines regarding the level of physical activity (PA) required to provide health benefits. However, the research underpinning these PA guidelines does not address the element of social health. Furthermore, there is insufficient evidence about the levels or types of PA associated specifically with psychological health. This paper first presents the results of a systematic review of the psychological and social health benefits of participation in sport by children and adolescents. Secondly, the information arising from the systematic review has been used to develop a conceptual model.

A systematic review of 14 electronic databases was conducted in June 2012, and studies published since 1990 were considered for inclusion. Studies that addressed mental and/or social health benefits from participation in sport were included.

A total of 3668 publications were initially identified, of which 30 met the selection criteria. There were many different psychological and social health benefits reported, with the most commonly being improved self-esteem, social interaction followed by fewer depressive symptoms. Sport may be associated with improved psychosocial health above and beyond improvements attributable to participation in PA. Specifically, team sport seems to be associated with improved health outcomes compared to individual activities, due to the social nature of the participation. A conceptual model, Health through Sport, is proposed. The model depicts the relationship between psychological, psychosocial and social health domains, and their positive associations with sport participation, as reported in the literature. However, it is acknowledged that the capacity to determine the existence and direction of causal links between participation and health is limited by the fact that the majority of studies identified (n=21) were cross-sectional.

It is recommended that community sport participation is advocated as a form of leisure time PA for children and adolescents, in an effort to not only improve physical health in relation to such matters as the obesity crisis, but also to enhance psychological and social health outcomes. It is also recommended that the causal link between participation in sport and psychosocial health be further investigated and the conceptual model of Health through Sport tested.

Regular participation in physical activity (PA) is imperative for good health. Active people benefit from higher levels of health-related fitness and are at lower risk of developing many different disabling medical conditions than inactive people [ 1 , 2 ]. It is widely acknowledged that the health benefits of participation in PA are not limited to physical health but also incorporate mental components [ 1 , 2 ].

Extensive research has resulted in clear recommendations of the level of PA required to produce health benefits [ 1 , 3 ]. There are specific health-related recommendations for children and adolescents distinct from those for adults. For people aged 5–17 years it is recommended that they undertake moderate or vigorous activities for at least 60 minutes per day [ 4 ]. Regular maintenance of this level of activity by children and adolescents can result in increased physical fitness, reduced body fat, favourable cardiovascular and metabolic disease risk profiles, enhanced bone health and reduced symptoms of depression and anxiety [ 1 ]. Whilst many different health benefits of participation in PA are acknowledged, the vast majority of research has focused on the physical health benefits of participation in PA, with less research focused on the mental and social health aspects. Although mental health benefits have been referenced in recent guidelines, to date ”insufficient evidence precludes conclusions about the minimal or optimal types or amounts of physical activity for mental health” [ 1 ] (Part G Section 8 p39).

Even though the World Health Organisation definition of health (2006) incorporates physical, mental and social health domains, the research providing evidence to the PA guidelines does not specifically address social health. However, the literature informing PA guidelines does suggest that aspects such as social support may contribute to some of the explanations of mental health outcomes [ 1 ].

Leisure-time PA is one domain of PA. Sport is one type of leisure-time PA which is organised and usually competitive and played in a team or as an individual [ 5 ]. Participation in sport is very popular among children. However there is evidence that participation in sport peaks at around 11–13 years before declining through adolescence [ 6 , 7 ]. Conversely, there is research indicating that children who are active through sport are more likely to be physically active in adulthood than those who do not participate in childhood sport [ 8 , 9 ]. Further, substantial public investment in sport development has been justified in terms of a range of health benefits [ 10 ], but without a clear understanding of the best way to achieve maximum health benefits - both mental and physical.

Extensive research has been conducted on the determinants of participation in PA [ 6 , 11 ] and on interventions that attempt to increase PA participation [ 12 ], with relatively little research focusing more specifically on sport [ 9 , 13 ]. Also, with regard to the health benefits of PA, the research has generally not extended to the mental and social health benefits of sport participation in particular.

A conceptual model in the public health area has been defined as “diagram of proposed causal linkages among a set of concepts believed to be related to a specific public health problem” [ 14 ] (p163). Determinants of PA are increasingly being understood using socio-ecological models, whereby intrapersonal, interpersonal, organisational, environmental and policy variables are identified as influences on participation [ 15 – 18 ]. As Earp and Ennett (1991) explain, conceptual models in health do take an ecological perspective, implying that behaviours or health outcomes result from the interaction of both individual and environmental determinants [ 14 , 19 ]. In terms of the sport and health nexus, we are not aware of a conceptual model that depicts the specific mental and social health outcomes of sport participation. Conceptual models have been developed which show the relationship between different types of PA, including sport, and the intensity and context of participation [ 20 ], however they do not extend to the health benefits of participation. In one systematic review of the effectiveness of interventions to increase physical activity, a conceptual model of the relationship between interventions, modifiable determinants, immediate outcomes and health outcomes was developed [ 21 ]. However, this study did not specifically identify sport. Furthermore, there are many clinical conceptual models depicting health outcomes of clinical conditions, however they do not focus on the general population or on preventive health or health promotion [ 22 ].

Firstly, this paper presents the results of a systematic review investigating the psychological and social benefits of participation in sport for children and adolescents. Secondly, the information obtained in the systematic review has been used to develop a conceptual model: the conceptual model of Health through Sport, for children and adolescents.

The criteria for considering studies for this review were as follows.

Inclusion criteria were:

Studies published in English between Jan 1990 and May 2012 inclusive.

Original research or reports published in peer review journals or government or other organisational publications which reported primary data.

Studies that presented data that addressed mental and/or social health benefits from participation in sport. In this context, the following definitions were adopted: ‘sport’ - “a human activity of achieving a result requiring physical exertion and/or physical skill which, by its nature and organisation, is competitive and is generally accepted as being a sport” [ 23 ]. ‘health’ – “a state of complete physical, mental and social well-being and not merely the absence of disease and infirmity” [ 24 ]; ‘mental’ - “of or referring to the mind or to the processes of the mind, such as thinking, feeling, sensing, and the like” [ 25 ] (p475) ‘mental health’ – "Mental Health refers to a broad array of activities directly or indirectly related to the mental well-being component included in the WHO's definition of health…It is related to the promotion of well-being, the prevention of mental disorders, and the treatment and rehabilitation of people affected by mental disorders” [ 26 , 27 ] ‘social’: “Relating to the interactions of individuals, particularly as members of a group or a community ” [ 25 ] (p475); ‘social health’: “That dimension of an individual’s well-being that concerns how he gets along with other people, how other people react to him, and how he interacts with social institutions and societal mores.” [ 28 ] (p 152). In this study, we also used the following terms: ‘psychological’ – as a synonym for ‘mental’; and ‘psychosocial’ - “…any situation in which both psychological and social factors are assumed to play a role” [ 29 ] (p638).

Studies where the data pertained to the individual level (i.e. for persons versus communal or national level).

Exclusion criteria were:

Studies or reports that addressed ‘exercise’ , ‘physical activity’ , ‘physical education’ , or ‘recreation’ , and not sport. Definitions of these terms are: ‘Exercise’ –“physical activity that is planned, structured, repetitive, and purposive in the sense that improvement or maintenance of one or more components of physical fitness is an objective” [ 27 ] (p128); ‘Physical activity’ - “bodily movement produced by skeletal muscles that results in energy expenditure” [ 27 ] (p126); ‘Physical education’ - “a sequential, developmentally appropriate educational experience that engages students in learning and understanding movement activities that are personally and socially meaningful, with the goal of promoting healthy living” [ 30 ] (p8); ‘Recreation’ – “pleasurable activity” [ 31 ] (p. 915).

Research/reports that addressed participation in ‘adapted’ sports (i.e. sport participation for persons with a physical and/or intellectual disability, such as wheelchair tennis).

Research/reports that addressed sub-populations subject to specific risks (i.e. studies with heroin users, ‘at risk’ individuals etc.).

Research/reports that addressed rehabilitation from, or management of, injury or illness.

Research/reports that addressed spectators, coaches or sports administrators.

Research/reports that addressed elite sports participants

Research/reports that addressed ‘sport development’ programs that have an educational objective.

Book chapters, abstracts, dissertations and conference proceedings.

Search methods for identification of studies, reports and publications

A systematic search of 14 electronic databases (AUSPORT, AusportMed, CINAHL, Cochrane Library, EBSCHOHost Research Databases, Health Collection, Informit, Medline Fulltext, PsycARTICLES, Psychology and Behavioral Sciences Collection, PsycINFO, PubMed, Scopus, SPORTDiscus Fulltext) was conducted in June 2012. We also consulted with the Australian Sports Commission to search the National Sports Information Centre records in order to identify relevant reports, publications and research not located through the search of the electronic databases cited above. Further, we conducted an internet search using the Google Scholar search engine ( http://www.googlescholar.com ) to locate studies in the Medicine, Social Sciences, Arts and Humanities subject areas. The Google Scholar search engine was also used to search for recognised International, National and State reports and publications that directly addressed the topic under consideration.

To search the electronic databases a combination of keywords and search terms was adopted. These key words and search terms were formulated by the authors of this systematic review as those they considered directly addressed the topic under consideration. These keywords and search terms constituted four groups, namely:

Group 1: sport

Group 2: health

Group 3: value, benefit, effect, outcome

Group 4: psychology, depression, stress, anxiety, happiness, mood, quality of life, social health, social relations, well, social connect, social functioning, life satisfaction, mental health, sociology, social.

Accordingly where possible, the database searches consisted of key words from Group 1 AND Group 2 AND Group 3 AND Group 4. The truncation symbol was added to the most basic word stem for each keyword to ensure all associated terms were included in the search.

Study selection

Figure  1 provides a summary of the stages of study selection. Titles and abstracts of potentially relevant articles were screened by JY. Authors, JY and RE examined all full-text articles, and assessed the studies to ensure that they met the inclusion criteria. Any discrepancies were resolved through discussion between the two reviewers. Consensus was obtained for all included articles. After reviewing the selected studies it was decided, given the breadth and complexity of the research domain, that studies focusing on children and adolescents should be reviewed separately from studies focusing on adults, This review focuses on children and adolescents only; studies that stated that they specifically investigated children and/or adolescents, but not adults (18 or above), were included.

Stages of study selection.

Data collection and analysis

Data extracted from each of the studies included: study design and methodology; sample size; country of origin; age of participants; cohort of participants; gender of participants; study aim; sport variable; other PA variables; theoretical construct; key findings in relation to psychological and social health outcomes.

Assessment of study quality

Study quality was objectively appraised using the Downs and Black checklist [ 32 ]. This checklist has been used in other systematic reviews within the physical activity and health field [ 33 , 34 ]. This checklist includes 27 items grouped into categories: reporting (10), external validity (3), internal validity - bias (7), internal validity – confounding (6), and power (1). Twenty five items are scored as 1 (compliance) or 0 (non-compliance or inability to determine compliance); one item about confounding is scored as 2 (full compliance), 1 (partial compliance) or 0 (non-compliance or inability to determine compliance); and the item concerning power is scored (via a more complex algorithm) on a scale of 0–5.

Because most of the studies we reviewed did not involve interventions, a number of the items on the Downs and Black checklist were not generally applicable. We substituted a simpler power item (presence or absence of reference to a power analysis), and scored all items as 0, 1 or NA (not applicable). We calculated a summary quality score for each paper (except the two qualitative papers for which only five items were applicable) by expressing the number of compliant items as a percentage of the number of applicable items. We included these scores (ranging from 33% to 88%) in Table  1 , and used the insights we gained through the scoring process in our discussion of study quality.

Conceptual model development

Based upon the literature presented in this review, a conceptual model of Health through Sport has been developed (Figure  2 ). The model depicts the relationship between determinants driving sport participation and the reported psychological and social health benefits of participation. The terminology used in this conceptual model is as defined in the inclusion criterion 3 above. The determinants are represented as per the Socio-Ecological Model [ 19 , 65 ]. Upon reviewing the studies, two dimensions of sport participation were identified, and it became evident that some reported health benefits were more likely to be associated with some contexts of sport participation than others. Therefore, a model was developed to represent the two contextual dimensions of sport participation and the different strengths of association between different contexts of sport participation and the three health aspects (physical, psychological and social).

Health through Sport conceptual model.

With regard to causality, we note that most studies have been cross-sectional and observational in nature, and hence do not provide strong evidence of causality. The literature suggests that sport can have positive health benefits; however it is also the case that better health may predispose people to initiate and maintain participation in sport. A few longitudinal studies provide stronger evidence of causality. However, in the absence of randomised and controlled experimental studies, which are challenging to implement in this domain, it will remain difficult to unequivocally determine the nature and direction of causality. Notwithstanding this, terms like ‘outcome’ and ‘benefit’ of sport participation have been used to describe the results of many of the studies reviewed, and we have used the same terminology in reviewing these studies.

Results and discussion

A total of 3668 publications were initially identified. Table  1 provides a summary of the 30 studies that met the inclusion criteria. Since the studies were generally conducted within schools, they included school age children and adolescents, generally 18 years or less. Most studies were quantitative (n=26) rather than qualitative (n=3), with one study incorporating both quantitative and qualitative methods. There were no randomised controlled trials, and the majority of studies were cross-sectional and observational (n=21). Of the longitudinal studies (n=9), the time between data collection was generally between 1 and 3 years (n=7), with one study reporting 12 years between data collection periods. The sample sizes ranged considerably, from 22 participants to large national surveys of over 50,000 participants. The United States of America was the country where most studies were conducted (n=21), followed by Canada (n=4), Switzerland (n=3), and Germany, United Kingdom and Puerto Rica (n=1). One study was conducted with participants across two countries, the USA and Puerto Rica. The age ranges of the children and adolescents differed considerably across studies. Six studies incorporated data from both the child or adolescent and their parent(s).

Most studies scored highly on the modified Downs and Black scale of study quality (median 75 percent; range 33–88 percent). Those studies within the highest tertile score range were all cross-sectional quantitative studies [ 39 , 41 – 43 , 46 , 49 , 51 – 53 , 62 ]. Only one of the 10 studies in the highest tertile score range incorporated a theoretical approach - the Theory of Youth Development [ 41 ]. Half of these 10 studies investigated differences in health measures between participants in sport/club sport and either other organised activities or no sport [ 41 , 43 , 49 , 53 , 62 ]; the other half more specifically investigated team sport participation in comparison to less or no team sport [ 39 , 42 , 46 , 51 , 52 ]. There was no clear distinction between the key findings of higher and lower ranked studies; both high and lower quality studies reported similar associations between sport participation and the psychological and social health domains.

Prima facie, longitudinal studies can provide greater strength of evidence regarding causality than can cross-sectional research. However, all of the longitudinal studies reviewed [ 35 , 40 , 44 , 50 , 58 ] had other methodological limitations, and as a consequence were not represented in the highest tertile of study quality scores. The results of these studies were consistent with those of the cross-sectional studies.

There were few (n=2) qualitative studies, and similar health benefits of participation in sport were also reported in the quantitative studies. The study by Holt et al., (2011) provided more depth than was captured in the other studies reviewed. Interviews with parents and children unearthed a wide range of developmental benefits, both personal and social benefits [ 36 ]. Psychological aspects of emotional control and exploration were reportedly related to sport participation. In addition, social benefits of relationships with coaches and friends were reported in this study [ 36 ].

The investigation of health benefits through participation in physical activity mainly involved cross-sectional surveys conducted through schools. In most cases the students were not allocated to a participation group prior to the study, and as such there were no control groups. This limits the capacity to attribute causality of participation on health outcomes.

The psychological and social health measures in each study were diverse (Tables  1 and 2 ). The most common variables related to psychosocial functioning and emotional wellbeing (n=6), followed by risk of depression and mental ill health (n=5), developmental aspects/behaviour (n=4), social anxiety and shyness (n=3), self-esteem (n=3) and suicidal behaviour (n=3). Some studies (n=15) investigated the differences between sports and non-sports participants, but many did not distinguish between sport and other categories of PA. In the studies involving adolescents, it was common to investigate differences in youth behaviour and development according to their participation (or not) in out-of-school extracurricular activities. Sport was sometimes defined as ‘school sport’ , ‘club sport’ or ‘team sport’; however no studies investigated associations between specific types of sport and psychological or social health domains.

Table  2 provides a broad overview of the health outcomes found to be significantly and positively associated with sports participation, and lists the studies that reported each health outcome. The most common positive outcomes were higher self-esteem (n=6 studies), better social skills (n=5 studies), fewer depressive symptoms (n=4 studies), higher confidence (n=3 studies) and higher competence (n=3 studies) amongst sport participants than non-sport participants. In total 40 different psychological and social health factors were reportedly associated with participation in sport.

In general, there were few theoretical constructs used to frame or explain the research findings. Only six studies (20%) incorporated theoretical or conceptual constructs. The most frequently adopted construct (n=3) was the theory of Positive Youth Development [ 36 , 40 , 41 ], which propounds the notion that children are ‘resources to be developed’ rather than ‘problems to be solved’, and that all youth have the potential for positive development [ 66 ].

One study that incorporated the theory of Positive Youth Development [ 36 ] also utilised an ecological approach, whereby the study was exploratory and not guided by one specific theory. In this case these researchers investigated the intrapersonal and interpersonal benefits of participation in sport. Similarly, an ecological approach has been combined with other theories such as the Socialisation Theory [ 57 ]. Brettschneider (2001) proposed that there are many contributing factors to the relationship between sports club participation and adolescent development [ 57 ]. As such, a multivariate structure, as well as cumulative and interactive effects, needs to be taken into account. Secondly, within his theoretical framework Brettschneider proposes that each individual is assumed to be the creator of his/her development. Whilst studies often discussed theories underpinning the research, it was not always clear how particular theories were incorporated into the methodology. For example Holt et al., introduced the Positive Youth Development theory in their introduction, but there was no mention of how this was applied in the methodology of data collection or in the analysis and interpretation [ 36 ]. On the other hand, Zarrett et al. clearly defined how they measured and indexed Positive Youth Development [ 40 ].

A recent study [ 37 ] incorporated Antonovsky’s Salutogenesis model [ 67 ] and Bandura’s theory of Social Learning [ 68 ]. The foundation of Antonovsky’s model is that heterostasis, ageing and progressive entropy are core characteristics of all living organisms. The model focuses on what makes a person maintain good health rather than focusing on the aetiology of sickness. In terms of the Social Learning theory, it is suggested that organised sport, particularly in teams, could be an important factor in a child’s social development [ 37 ]. However, this was a general discussion comment, and it is not clear how the Social Learning Theory was applied in the methodology of this study [ 43 ].

The theoretical perspective of Marsh [ 64 ] was adopted from Coleman’s [ 69 ] seminal work which “implies a zero-sum model in which greater involvement in extracurricular activities necessitates a decreased involvement in more narrowly defined academic pursuits” (p.19) in a way that is complementary rather than multiple roles being in conflict [ 64 ]. Stemming from Coleman’s earlier work, Marsh discussed Snyder et al. (1995) Multiple Role theory [ 70 ] which proposes that adolescents take on multiple roles as both a student and an athlete. Marsh suggests that “multiple roles may create psychological stress based in part on time and energy limitations, multiple roles may be complementary and may lead to energy expansion” (p19). In essence Marsh attempts to capsulate the perspective that sport participation as an additional extracurricular activity can have positive outcomes, rather than sport being seen, as depicted in earlier theoretical perspectives, as a burden, taking time away from academic pursuits. However, as with a number of other studies reviewed, it was not clear how the particular behavioural theory was applied in the study [ 64 ].

Few differences were evident between the conclusions of studies of higher and lower quality or of different study design. There were however, clear differences in the reported health outcomes associated with different contexts of participation. Therefore the following presents and discusses the reported psychological and social health benefits of participation in sport in the different contexts of: extracurricular activities; team sport; school or club sport; and sport in general. These categories, which are not mutually exclusive, were based upon the definitions or categorisation made within each individual study. Furthermore, the health benefits according to different types of participation are discussed. Lastly, given the greater strength of evidence regarding causality in longitudinal versus cross-sectional research, the key findings from the longitudinal studies are summarised.

Extracurricular activities

Several studies have investigated the influence of sport, as one type of extracurricular activity, on positive youth development [ 36 , 40 , 41 ] general behaviours [ 39 ] and personal development [ 53 ]. Other extracurricular activity categories considered were school-based activities, religious activities, youth groups, performing arts, volunteering, paid work, band and music lessons [ 40 , 41 , 52 ]. The definition of ‘sport’ as an extracurricular activity varied considerably. Sport was sometimes defined as including both team and individual sports [ 40 , 53 ] or encompassing different categorical groups for both team and individual sports participants [ 37 ], whilst others categorised groups as structured versus unstructured activities [ 55 ]. Howie et al. (2010) investigated extracurricular (outside school) activities - sports teams/lessons, sports clubs/organisations, or both - in the previous year [ 39 ].

While the qualitative study of Holt et al. (2011) did not compare sports participation with other activities, parents reported benefits for their children in personal and social development from sport participation. Social benefits included positive relationships with coaches, making new friends, and developing teamwork and social skills. Personal benefits included children being emotionally controlled, enjoying exploration, having confidence and discipline, performing well academically, managing their weight and being ‘kept busy’ [ 36 ].

Similarly, Bartko and Eccles (2003) reported that structured activities (sport being one of them) led to higher positive functioning for participants [ 55 ]. Howie et al. (2010) reported that children participating in both sports and clubs had higher social skill scores compared with children who did not participate in any outside-school activity [ 39 ]. Concurring with these findings, Linver et al. (2009) found that participation in sport and other organised activities had the greatest youth development outcomes, and low involvement in organised activities outside school was associated with less positive development across the board [ 41 ]. Sports participation alone had more developmental benefits than non-participation or other types of extracurricular activities, however the greatest benefits were seen for those involved with both sport and other activities [ 39 , 41 ].

Whilst positive social aspects of participation in sport have been consistently reported, it has also been found that young people involved with sport had higher rates of negative peer interaction [ 53 ]. These researchers concluded that this may be due to the competitive nature of sports activities compared to other activities. Even so, they found that, in addition to physical benefits, those involved with sport had higher rates of self-knowledge and emotional regulation than those involved with other activities [ 53 ]. While Harrison et al. (2003) defined team sport separately from other activities, their results were collated as sports only, activities only and sports and activities [ 52 ]. Contrary to some other findings, they found that sports alone (and also in combination with other activities) were associated with significantly better health outcomes, including higher healthy self-image and lower risk of emotional distress, suicidal behaviour and substance abuse.

Two longitudinal studies, one with a year between measurements and another three years, investigated the effects of participation in extracurricular activity on youth development [ 40 ] and social anxiety [ 37 ]. Dimech and Seiler (2011) investigated sport only, categorised as non-participation, individual or team involvement [ 37 ], whereas Zarrett et al. (2009) investigated team or individual sport participation in comparison to participation in development programs, performing arts, arts and crafts, school clubs, volunteering, religious groups, and paid work [ 40 ]. Consistent with the cross-sectional results of Linver et al. (2009) and Howie et al. (2010), Zarrett et al. (2009) concluded that a combination of sport plus other youth development programs was related to positive youth development, even after controlling for total time spent in the activities and the duration of sport participation.

Dimech and Seiler (2011) measured the effects of extracurricular participation in sport on social anxiety [ 37 ]. Comparing team sport, individual sport and no sport, they reported an interaction between sport mode and time, with team sport participants having reductions in social anxiety scores over time, whilst anxiety scores in the no-sport and individual-sport groups actually increased. Dimech and Seiler concluded that sport practice had a positive effect as a buffer against anxiety, but only team sport and not individual sport.

Whilst some studies highlighted the benefits of extracurricular sport, the focus was more commonly on ‘team sport’ in general, without distinguishing between in-school and out-of-school settings [ 42 , 43 , 46 , 50 , 51 , 58 , 59 , 61 ].

The psychological and social health aspects measured included mental health benefits [ 61 ], social isolation [ 59 ], depressed mood and symptoms of depression [ 46 , 58 ], self-esteem [ 50 ], life satisfaction [ 51 ], hopelessness and suicidality [ 42 ] and emotional self-efficacy [ 43 ].

Cross-sectional studies included a survey of US high school students, in which participation in team sport was associated with lower general risk-taking and fewer mental health and general health problems compared with non-participation [ 61 ]. In another cross-sectional survey, team sport involvement was positively associated with social acceptance and negatively associated with depressive symptoms [ 46 ]. Boone and Leadbeater concluded that benefits from team sport may be related to the effect of positive experiences (in coaching, skill development, peer support) in enhancing perceived social acceptance and reducing body dissatisfaction [ 46 ]. Team sport participation has also been reported to protect against feelings of hopelessness and suicidality, even after controlling for levels of physical activity [ 42 ]. Another reported health benefit of participation in team sport (both school and extracurricular participation) is life satisfaction [ 51 ]. A study investigated the relationship between different physical activity behaviours, distinguishing between vigorous and moderate levels as well as strength/toning and team participation contexts, and found that meeting recommended levels of PA and participation in sports teams was significantly associated with better emotional self-efficacy [ 43 ].

In a longitudinal study of adolescents with measurements one year apart, team sport participation was found to be protective against depressed mood associated with school performance levels [ 58 ]. In a longitudinal study of females, team sport achievement experiences in early adolescence were positively associated with self-esteem three years later in middle adolescence [ 50 ]. Another longitudinal study spanning 12 years found that participation in team sport (specifically school teams) was associated with lower social isolation later in life, compared with other activities categorised as pro-social, arts, and school-based [ 59 ].

School and/or club sport

Some studies distinguished between participation in ‘school sport’ and ‘club sport’ [ 38 , 54 , 56 , 57 , 62 ]. Snyder et al. (2010) while reporting school and club participation, then combined them into a single ‘athletes’ category and compared them to non-athletes on health-related quality of life measures. The athletes reported higher scores on physical functioning, general health, social functioning and mental health scales and a mental composite score, and lower on a bodily pain scale, than non-athletes [ 38 ]. Similarly, in a Swiss study, Ferron and colleagues classified adolescents as ‘athletes’ or ‘non-athletic’ on the basis of sports club participation. The athletes had superior well-being, including being better adjusted, feeling less nervous or anxious, being more often full or energy and happy about their life, feeling sad or depressed less often and having higher body image and fewer suicide attempts [ 62 ].

One longitudinal study of club sport participation over a three year period during adolescence in Germany, as well as identifying physical benefits, showed that sport club activities had a positive influence on the development of self-esteem, with girls discovering sports as a source of self-esteem earlier than boys [ 57 ]. In terms of relationships with peers and parents, club sport members did not differ significantly from non-members. Brettschneider and colleagues concluded that although sports club participants had better health outcomes, these benefits were due to self-selection bias rather than a sport club effect [ 57 ]. These researchers also acknowledged that research into the impact of sports by discipline, and studies of longer duration, are required.

In relation to school sport specifically, participation was found to be significantly associated with self-esteem in Latino subgroups of students living in the United States of America [ 56 ]. This was true for Mexican girls and boys, Puerto Rican girls and Cuban boys but not Puerto Rican boys and Cuban girls. Pyle and colleagues investigated participation in school sports defined as being high or low intensity. Participation in competitive sports was found to be associated with lower frequency of mental health problems [ 54 ].

Level of sport involvement

Most studies defined sport participation as a binary categorical variable without further information regarding level of involvement. However, a few studies have investigated psychological and social health outcomes in relation to different levels of intensity of sport activities (low, moderate, vigorous, or high) [ 60 , 63 ] or frequency of participation and number of sport activities [ 48 ].

Steptoe and Butler (1996) assessed the association between extent of participation in sport or vigorous recreational PA and emotional wellbeing in adolescents [ 63 ]. Without distinguishing between sport and other vigorous PA, Steptoe and Butler reported that greater participation in vigorous activities was associated with lower risk of emotional distress [ 63 ]. Sanders and colleagues found that for high school senior students moderate sport participation (3–6 hours per week) was associated with lower depression scores than low sport involvement (0–2 hours) [ 60 ]. Donaldson and Ronan (2006) investigated participation in both “formal” and “informal” sports and reported that greater participation was related to enhanced emotional and behavioural well-being. Those participating in more formal sports reported significantly lower levels of emotional and social problems compared to those participating in fewer formal sports [ 47 ]. Another study investigated frequency of extracurricular sport and perceived health, health attitudes and behaviour [ 49 ]. Those with greater frequency of participation (at least twice per week) had better feelings of well-being compared to those who participated less than once per week [ 49 ]. One study looked at number of sports, type of sport, and years participating in sport, and found that sport participation was positively related to self-assessments of physical appearance and physical competence, physical self-esteem and general self-esteem [ 48 ]. Furthermore, these researchers found that differences between competitive and non-competitive sports was minimal, and suggested that for young adolescents, it is more important to consider the total number of sports and total number of years in sports-related activities [ 48 ].

Sport in general

A few studies used a broad definition of sport without providing further context of participation [ 35 , 44 , 64 ]. Sport participation versus no sport participation was found to be significantly associated with enhanced self-concept [ 64 ]. A longitudinal study also reported benefits of participation in sport compared to no participation, in relation to lower rates of suicidal ideation including both thoughts and intentions [ 35 ]. In terms of the effect of sport participation on shyness, a longitudinal study with measurement at baseline and one year later found that sport was positively associated with positive adjustment (e.g. social skills and self-esteem) and that sport played a uniquely protective role for shy children, with shy children who participated in sport over time reporting significant decreases in anxiety [ 44 ]. Similarly, in a qualitative study of focus groups of parents of young people participating in sport, social factors as well as life skills and self-concept were stated as benefits of participation [ 45 ].

Longitudinal studies

Longitudinal studies can provide stronger evidence of causality than cross-sectional studies. However, the longitudinal studies reviewed were generally short in duration, usually with only two measurement points, one or two years apart [ 35 , 40 , 44 , 50 , 58 ]. They were all observational in nature, with no control groups, and with limited measurement of the level of participation and frequency or duration of sport activities. All studies were based on surveys conducted through schools, with participation in sport and other extracurricular activities reported mainly in binary categories.

The main findings were that, after controlling for factors such as income, parents’ education, age and ethnicity, compared to no participation or participation in individual sports, participation in team sport had resulted in benefits such as lower social anxiety [ 37 ], lower social isolation [ 59 ], better social self-concept [ 64 ], and improved self-esteem [ 50 ]. Sport in general has also been associated with positive youth development [ 40 ]; the young people who were highly engaged in general, and those who participated primarily in sports and youth development programs, had the highest positive youth development scores.

In a recent study undertaken longitudinally over a one-year period, where sport participation was generally reported to be of 1–2 hour duration per week, there was no effect of weekly hours of sport on social anxiety [ 37 ]. Similarly, Findlay and Coplan (2008) in a longitudinal analysis over a one-year period, did not find significant effects of sport participation over the year (neither main effects of time or participation-time interactions) on social skills, self-esteem, positive adjustment or externalising problem behaviours [ 44 ]. However, shy children who participated in sport over a one-year period demonstrated a decrease in anxiety over time. Sport was associated with positive psychological and social outcomes, including higher positive affect and well-being and greater social skills. Shy and aggressive children who participated in sport reported higher self-esteem [ 44 ]. A study of club sport members compared to non-club members also did not show a systematic effect of club membership on most measures of psychological and social health in adolescents over three years [ 57 ]. Notwithstanding, clubs had a positive effect on adolescent self-esteem and were reported, on the basis of high membership rates, to be a highly integrative social force [ 57 ].

A US study in which high school students were interviewed at two time points one year apart, showed that for females, but not for males, team sport involvement was protective against depressed mood state associated with poor school performance [ 58 ]. Another US study of female adolescents over three years found that sports achievement experiences in early adolescence were positively associated with self-esteem in middle adolescence [ 50 ]. Team sports achievements, team sports self-evaluations and individual sports self-evaluations tended to be significantly and positively associated both cross-sectionally and longitudinally. Team sport achievement in early adolescence was related to girls’ global self-esteem in middle adolescence, and team sport self-evaluations mediated the relation between achievement and self-esteem. In addition, the relationship between achievement and self-esteem was partially mediated by girls’ perceptions of competence and interest in team sport, and mastery in team sport contributed to global self-esteem development [ 50 ].

Another longitudinal study showed that adolescents involved with team sport had lower suicide ideation with regard to both contemplation and intention [ 35 ]. These researchers suggested that when young people discontinue playing sport they lose the protective social networks, as well as connections to caring adults and pro-social peers, that help to promote healthy youth development and reduce the risk of suicide.

Conceptual model

A conceptual model of Health through Sport is proposed that is based on three primary categories of outcome: physical, psychological and social, and two secondary categories: physical/psychological – aspects involving both the physical and psychological elements, and psychosocial – aspects involving both psychological and social elements.

While our model incorporates all five categories and thus depicts the full range of health aspects, the ‘physical’ aspects have been well reviewed elsewhere [ 1 ], and so this paper in focused on the psychological and social aspects, as defined above. Furthermore, while the present review was limited to research into children and adolescents, the general form of the Health through Sport model is believed to also apply to adults, although it is likely there would be some change in the specific elements of each component.

The model includes three major elements: determinants of sports participation, sport itself, and health outcomes of sport participation. The ‘determinants’ element is based on the well-established social ecological model [ 19 , 65 ] and is represented as concentric rings spreading out from the individual’s intrapersonal characteristics to widening spheres of influence. The sport element incorporates two dimensions of context: individual – team, and informal – organised, each of which is almost dichotomous, but also has some intermediate variants (e.g. running alone, running in an informal group, running for a club team, running in a club relay team). The three types of health outcomes - physical, psychological and social, are shown as overlapping, representing the fact that there may be interactions and interrelationships between physical and psychological aspects and between psychological and social health aspects. For example, there are relationships between physical fitness and mental state; and interpersonal relationships may satisfy needs for belongingness and, as such, influence psychological health. Another example is resilience, whereby psychological health may influence an individual’s capacity to engage in interpersonal relationships.

The different strengths of the various linkages between the sport element and the health outcomes represent the notion that all forms of sport contribute strongly to physical health, but that while organised and/or team forms also contribute strongly to psychological and social outcomes, informal and/or individual forms contribute somewhat less to psychological outcomes and relatively little to social outcomes. Finally, we have noted the limited evidence of causality in the literature reviewed. This ambiguity or reciprocity could perhaps be represented by double-headed arrows linking the physical, psychological and social elements to the sport element, but we have represented it by ‘feedback loops’ from the three outputs to the intrapersonal and interpersonal determinants.

Limitations

This systematic review has some limitations. Whilst the search strategy, based on a-priori inclusion and exclusion criteria, was comprehensive and encompassed grey literature which reported primary data, conference proceedings were not included. Nor were non-English language articles included. The studies reviewed included a wide range of aims, focuses, measurement tools and indicators of both sport participation and health outcomes. This diversity of focus and methodology limited the extent of synthesis and precluded meta-analysis. Most studies were cross-sectional and used self-report measures. Therefore results should be interpreted with caution, and any conclusions regarding causation are conjectural.

There is substantive evidence of many different psychological and social health benefits of participation in sport by children and adolescents. Furthermore, there is a general consensus that participation in sport for children and adolescence is associated with improved psychological and social health, above and beyond other forms of leisure-time PA. More specifically, there are reports that participation in team sports rather than individual activities is associated with better health. It is conjectured that this is due to the social nature of team sport, and that the health benefits are enhanced through positive involvement of peers and adults. However, the research is predominantly based on cross-sectional studies.

In light of the research evidence, acknowledging that research to date is predominantly based on cross-sectional studies, it is recommended that community sport participation is advocated as a form of leisure time PA for children and adolescents; in an effort to not only improve the obesity crisis associated with low PA levels, but to enhance other psychological and social health outcomes. It is also recommended that the causal link between participation in sport and health be further investigated and the conceptual model of health through sport tested. Furthermore, in light of the fact that our assessment of the quality of the studies to date has revealed considerable variation in study quality, it is recommended that researchers should give more attention to protocols such as CONSORT [ 71 ] and STROBE [ 72 ] in order to ensure high levels of methodological rigor in future studies.

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RME is supported by a VicHealth Research Practice Fellowship.

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RME contributed to the study design, the review of literature, analysis of literature, model conceptualisation, manuscript conceptualisation and preparation. JAY contributed to the study design, the review of literature, analysis of literature, model conceptualisation, manuscript conceptualisation and preparation. JTH contributed to analysis of literature, model conceptualisation and representation, and manuscript preparation. MJC contributed to analysis of study quality and critical review of the manuscript. WRP contributed to the study design and critical review of the manuscript. All authors read and approved the final manuscript.

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Eime, R.M., Young, J.A., Harvey, J.T. et al. A systematic review of the psychological and social benefits of participation in sport for children and adolescents: informing development of a conceptual model of health through sport. Int J Behav Nutr Phys Act 10 , 98 (2013). https://doi.org/10.1186/1479-5868-10-98

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Perspectives of applied collaborative sport science research within professional team sports

Affiliations.

• 1 a School of Health Sciences , Liverpool Hope University , Liverpool , UK.
• 2 b Human and Health Sciences , University of Huddersfield , Huddersfield , UK.
• 3 c Institute for Sport, Physical Activity and Leisure , Leeds Beckett University , Leeds , UK.
• 4 d The Rugby Football League , Leeds , UK.
• 5 e Yorkshire Carnegie Rugby Union Club , Leeds , UK.
• 6 f Leeds Rhinos Rugby League Club , Leeds , UK.
• 7 g Mary Immaculate College , Limerick , Ireland.
• 8 h CB Sports Performance Ltd , Rugeley , UK.
• 9 i Sport, Health and Exercise Science , University of Hull , Hull , UK.
• PMID: 30009684
• DOI: 10.1080/17461391.2018.1492632

The purpose of the study was to examine the perspectives of both academics and practitioners in relation to forming applied collaborative sport science research within team sports. Ninety-three participants who had previously engaged in collaborative research partnerships within team sports completed an online survey which focused on motivations and barriers for forming collaborations using blinded sliding scale (0-100) and rank order list. Research collaborations were mainly formed to improve the team performance (Academic: 73.6 ± 23.3; Practitioner: 84.3 ± 16.0; effect size (ES = 0.54), small). Academics ranked journal articles' importance significantly higher than practitioners did (Academic: M rank = 53.9; Practitioner: 36.0; z = -3.18, p = .001, p < q). However, practitioners rated one-to-one communication as more preferential (Academic: M rank = 41.3; Practitioner 56.1; z = -2.62, p = .009, p < q). Some potential barriers were found in terms of staff buy in (Academic: 70.0 ± 25.5; Practitioner: 56.8 ± 27.3; ES = 0.50, small) and funding (Academic: 68.0 ± 24.9; Practitioner: 67.5 ± 28.0; ES = 0.02, trivial). Both groups revealed low motivation for invasive mechanistic research (Academic: 36.3 ± 24.2; Practitioner: 36.4 ± 27.5; ES = 0.01, trivial), with practitioners have a preference towards 'fast' type research. There was a general agreement between academics and practitioners for forming research collaborations. Some potential barriers still exist (e.g. staff buy in and funding), with practitioners preferring 'fast' informal research dissemination compared to the 'slow' quality control approach of academics.

Keywords: Coaching; barriers; education; performance; sport science; survey.

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Many social and sport psychologists consider that group/ team cohesion as well as athletes' satisfaction has a major impact on team performance. The aim of this study was to examine if there were significant differences in group cohesion and athletes' satisfaction as a function of gender (male, female), type of team sports (soccer, basketball, volleyball, handball, water polo) and sport division (professional, semi-professional). A second aim was to analyze the relationship between group cohesion and athletes' satisfaction in team sports. Participants were 615 professional and semi-professional team-sport athletes from Greece, aged 15 to 36. They completed two questionnaires: (a) the Group Environment Questionnaire (Individual Attraction to Group-Task: ATG-T; Individual Attraction to Group-Social: ATG-S; Group Integration-Task: GI-T; Group Integration-Social: GI-S) and (b) the Athletes' Satisfaction Scale (Personal Outcome, Leadership). Separate three-way MANOVAs revealed that type of team sports, but not gender or sport division, had a significant effect on group cohesion and athletes' satisfaction. Moreover, canonical correlation analysis revealed significant multivariate relationship between group cohesion and athletes' satisfaction. Overall, results indicated the important role of group cohesion, gender and team sports on Greek athletes' satisfaction.

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May 29, 2024 | Risa F. Isard, E. Nicole Melton, and Katie Sveinson

To Celebrate the Women’s Sports Moment, Look to the Movement

Recent research provides insights into the experiences of the workers who’ve dedicated themselves to growing women’s sport and the steps leaders can take to continue the progress of the movement

"For all of the recent growth, disparities still remain, in athlete salaries, media rights, facility access, employee compensation, and more," writes Risa Isard and co-researchers. iStock images

Editor’s Note: This article about experiences of women who work in women’s sport teams and leagues by Risa Isard, E. Nicole Melton, and Katie Sveinson was originally featured in Sports Business Journal.

Caitlin Clark’s  $28 million deal with Nike is setting history — again. While the many recently broken records across women’s sport are worth celebrating, there’s a chance the industry is so caught up in the moment that we’re at risk of forgetting how we got here.

This is important for growing fan communities to consider, and even more critical for today’s business leaders. Overlooking what’s come before the current moment threatens to alienate key stakeholders — especially women’s sport employees, many of whom are experiencing this record-setting time through a unique lens.

Our recent research provides insights into the experiences of the workers who’ve dedicated themselves to growing women’s sport and the steps leaders can take to continue the progress of the movement. Over the past three-and-a-half years, we interviewed employees working for women’s sport teams and leagues. We heard from people across the gender spectrum, nearly 40% of whom were people of color and nearly one-third of whom identified as LGBTQ+. Respondents ranged from 24-57 and spanned departments and hierarchies.

Despite these differences, their experiences working in women’s sport were remarkably similar: the joy in recent developments is juxtaposed against the pain of decades of disregard and mistreatment.

Their experiences working in women’s sport were remarkably similar: the joy in recent developments is juxtaposed against the pain of decades of disregard and mistreatment. — Risa F. Isard, E. Nicole Melton, and Katie Sveinson

In one breath, employees would rattle off enthusiasm for recent records broken: attendance, viewership, valuation, media rights, sales, and expansion fees. In the next, they shared frustrations about recent and historic experiences with limited media coverage, disrespect from prospective partners, being deprioritized by venues, poor pay and treatment for athletes, and more. Notably, they also shared stories of the ridicule they personally faced for working in women’s sport — times they were cursed out on the job when making calls on behalf of the organization, and times off the job when friends and family disparaged their career choices.

The research illustrates a sports industry turning point, with visible progress and growing respect contrasted with a traumatic past for workers that cannot be erased with a few (or many) records.

That’s why industry leaders celebrating during this moment in time must also embrace the movement — the path to getting here and the path ahead.

Educate Yourself on Women’s Sport’s Canon

Leaders who want to celebrate today’s wins have to know why these wins matter — and why they didn’t happen sooner. Seek out the history, in all its nuances.

Apply a mindset to understand and learn, expanding your horizons to include foundational knowledge of the community that has built and sustained women’s sport for decades. Leaders who do will be better equipped to take part in and contribute to the movement.

One of the best ways to learn is simply to listen to the stories told by those who have been part of the movement. Listen to the athletes, front office employees, agents, journalists, and fans who have devoted themselves to women’s sport for the past years and decades. All these people have stories to tell, and many share them publicly. Following new voices on social media, consuming message boards, and reading think pieces by those who have paved this path can help fill in the backstory.

Commit to Creating Tomorrow While Celebrating Today

The movement is also about the future. Indeed, employees we spoke with see this moment as a “tipping point” — not as a final destination. Chances are, if you are reading this piece, you also understand that the narrative around women’s sport is shifting, not shifted. For all of the recent growth, disparities still remain, in athlete salaries ,  media rights , facility access, employee compensation, and more.

Mia Hamm, who a quarter-of-a-century ago led our country through its last women’s sport awakening, said, “Take your victories, whatever they may be, cherish them, use them, but don’t settle for them.” Indeed, we have so much farther to go.

Leaders should recognize this and make a commitment to the women’s sport movement, signing on for long-term investments to continually propel the game so that today’s records are just tomorrow’s benchmark. — Risa F. Isard, E. Nicole Melton, and Katie Sveinson

Leaders should recognize this and make a commitment to the women’s sport movement, signing on for long-term investments to continually propel the game so that today’s records are just tomorrow’s benchmarks. Media companies should  invest in full-time positions , hiring beat writers for the women’s sport teams and staffing journalists who came up through the women’s game, in addition to creating expanded content. Brands should spend intentionally, building partnerships with women’s teams, athletes, and players’ associations, along with making concentrated ad buys that support media coverage. Innovators can find gaps and double down in creating something new, like this new women’s track invitational .

Tell the Whole Story

Sport loves underdogs, and there may be no bigger upset than the one women’s sport has recently achieved. The underdog story acknowledges what athletes, employees, and fans have been through, while celebrating that even decades of corporate disinvestment and seemingly impenetrable stereotypes couldn’t keep down the force that is women’s sport. It’s precisely this confidence — the belief derived from overcoming, paired with an authentic recognition — that will advance women’s sport.

WNBA legend Nneka Ogwumike said it best : “We’re at a very pivotal moment for the history of our league… I love to see all the new fans. I would implore upon them to do their homework on the history just so that they can better understand things, because we have people that are working hard in the front office, in the offices of these teams, we have all these players that have been around for a long time that played with OGs who are no longer playing that we’re standing on the backs of.”

For all that so many in the industry have given, women’s sport deserves nothing less than the latest records and attention of the day. It also still deserves so much more.

Risa F. Isard ( @RisaLovesSports ) is an assistant professor at the University of Connecticut; E. Nicole Melton ( @Doc_Melton ) is a professor at the University of Massachusetts; and Katie Sveinson ( @KatieSveinson ) is an assistant professor at the University of Massachusetts. This piece is crafted in partnership with The Collective Think Tank: a global consortium of academic minds and industry leaders focused on gender parity and improving diversity. The collaboration is led by The Collective, Wasserman’s women-focused division.

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• Published: 17 October 2023

The impact of founder personalities on startup success

• Paul X. McCarthy 1 , 2 ,
• Xian Gong 3 ,
• Fabian Braesemann 4 , 5 ,
• Fabian Stephany 4 , 5 ,
• Marian-Andrei Rizoiu 3 &
• Margaret L. Kern 6  

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This article has been updated

Startup companies solve many of today’s most challenging problems, such as the decarbonisation of the economy or the development of novel life-saving vaccines. Startups are a vital source of innovation, yet the most innovative are also the least likely to survive. The probability of success of startups has been shown to relate to several firm-level factors such as industry, location and the economy of the day. Still, attention has increasingly considered internal factors relating to the firm’s founding team, including their previous experiences and failures, their centrality in a global network of other founders and investors, as well as the team’s size. The effects of founders’ personalities on the success of new ventures are, however, mainly unknown. Here, we show that founder personality traits are a significant feature of a firm’s ultimate success. We draw upon detailed data about the success of a large-scale global sample of startups (n = 21,187). We find that the Big Five personality traits of startup founders across 30 dimensions significantly differ from that of the population at large. Key personality facets that distinguish successful entrepreneurs include a preference for variety, novelty and starting new things (openness to adventure), like being the centre of attention (lower levels of modesty) and being exuberant (higher activity levels). We do not find one ’Founder-type’ personality; instead, six different personality types appear. Our results also demonstrate the benefits of larger, personality-diverse teams in startups, which show an increased likelihood of success. The findings emphasise the role of the diversity of personality types as a novel dimension of team diversity that influences performance and success.

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Introduction.

The success of startups is vital to economic growth and renewal, with a small number of young, high-growth firms creating a disproportionately large share of all new jobs 1 , 2 . Startups create jobs and drive economic growth, and they are also an essential vehicle for solving some of society’s most pressing challenges.

As a poignant example, six centuries ago, the German city of Mainz was abuzz as the birthplace of the world’s first moveable-type press created by Johannes Gutenberg. However, in the early part of this century, it faced several economic challenges, including rising unemployment and a significant and growing municipal debt. Then in 2008, two Turkish immigrants formed the company BioNTech in Mainz with another university research colleague. Together they pioneered new mRNA-based technologies. In 2020, BioNTech partnered with US pharmaceutical giant Pfizer to create one of only a handful of vaccines worldwide for Covid-19, saving an estimated six million lives 3 . The economic benefit to Europe and, in particular, the German city where the vaccine was developed has been significant, with windfall tax receipts to the government clearing Mainz’s €1.3bn debt and enabling tax rates to be reduced, attracting other businesses to the region as well as inspiring a whole new generation of startups 4 .

While stories such as the success of BioNTech are often retold and remembered, their success is the exception rather than the rule. The overwhelming majority of startups ultimately fail. One study of 775 startups in Canada that successfully attracted external investment found only 35% were still operating seven years later 5 .

But what determines the success of these ‘lucky few’? When assessing the success factors of startups, especially in the early-stage unproven phase, venture capitalists and other investors offer valuable insights. Three different schools of thought characterise their perspectives: first, supply-side or product investors : those who prioritise investing in firms they consider to have novel and superior products and services, investing in companies with intellectual property such as patents and trademarks. Secondly, demand-side or market-based investors : those who prioritise investing in areas of highest market interest, such as in hot areas of technology like quantum computing or recurrent or emerging large-scale social and economic challenges such as the decarbonisation of the economy. Thirdly, talent investors : those who prioritise the foundation team above the startup’s initial products or what industry or problem it is looking to address.

Investors who adopt the third perspective and prioritise talent often recognise that a good team can overcome many challenges in the lead-up to product-market fit. And while the initial products of a startup may or may not work a successful and well-functioning team has the potential to pivot to new markets and new products, even if the initial ones prove untenable. Not surprisingly, an industry ‘autopsy’ into 101 tech startup failures found 23% were due to not having the right team—the number three cause of failure ahead of running out of cash or not having a product that meets the market need 6 .

Accordingly, early entrepreneurship research was focused on the personality of founders, but the focus shifted away in the mid-1980s onwards towards more environmental factors such as venture capital financing 7 , 8 , 9 , networks 10 , location 11 and due to a range of issues and challenges identified with the early entrepreneurship personality research 12 , 13 . At the turn of the 21st century, some scholars began exploring ways to combine context and personality and reconcile entrepreneurs’ individual traits with features of their environment. In her influential work ’The Sociology of Entrepreneurship’, Patricia H. Thornton 14 discusses two perspectives on entrepreneurship: the supply-side perspective (personality theory) and the demand-side perspective (environmental approach). The supply-side perspective focuses on the individual traits of entrepreneurs. In contrast, the demand-side perspective focuses on the context in which entrepreneurship occurs, with factors such as finance, industry and geography each playing their part. In the past two decades, there has been a revival of interest and research that explores how entrepreneurs’ personality relates to the success of their ventures. This new and growing body of research includes several reviews and meta-studies, which show that personality traits play an important role in both career success and entrepreneurship 15 , 16 , 17 , 18 , 19 , that there is heterogeneity in definitions and samples used in research on entrepreneurship 16 , 18 , and that founder personality plays an important role in overall startup outcomes 17 , 19 .

Motivated by the pivotal role of the personality of founders on startup success outlined in these recent contributions, we investigate two main research questions:

Which personality features characterise founders?

Do their personalities, particularly the diversity of personality types in founder teams, play a role in startup success?

We aim to understand whether certain founder personalities and their combinations relate to startup success, defined as whether their company has been acquired, acquired another company or listed on a public stock exchange. For the quantitative analysis, we draw on a previously published methodology 20 , which matches people to their ‘ideal’ jobs based on social media-inferred personality traits.

We find that personality traits matter for startup success. In addition to firm-level factors of location, industry and company age, we show that founders’ specific Big Five personality traits, such as adventurousness and openness, are significantly more widespread among successful startups. As we find that companies with multi-founder teams are more likely to succeed, we cluster founders in six different and distinct personality groups to underline the relevance of the complementarity in personality traits among founder teams. Startups with diverse and specific combinations of founder types (e. g., an adventurous ‘Leader’, a conscientious ‘Accomplisher’, and an extroverted ‘Developer’) have significantly higher odds of success.

We organise the rest of this paper as follows. In the Section " Results ", we introduce the data used and the methods applied to relate founders’ psychological traits with their startups’ success. We introduce the natural language processing method to derive individual and team personality characteristics and the clustering technique to identify personality groups. Then, we present the result for multi-variate regression analysis that allows us to relate firm success with external and personality features. Subsequently, the Section " Discussion " mentions limitations and opportunities for future research in this domain. In the Section " Methods ", we describe the data, the variables in use, and the clustering in greater detail. Robustness checks and additional analyses can be found in the Supplementary Information.

Our analysis relies on two datasets. We infer individual personality facets via a previously published methodology 20 from Twitter user profiles. Here, we restrict our analysis to founders with a Crunchbase profile. Crunchbase is the world’s largest directory on startups. It provides information about more than one million companies, primarily focused on funding and investors. A company’s public Crunchbase profile can be considered a digital business card of an early-stage venture. As such, the founding teams tend to provide information about themselves, including their educational background or a link to their Twitter account.

We infer the personality profiles of the founding teams of early-stage ventures from their publicly available Twitter profiles, using the methodology described by Kern et al. 20 . Then, we correlate this information to data from Crunchbase to determine whether particular combinations of personality traits correspond to the success of early-stage ventures. The final dataset used in the success prediction model contains n = 21,187 startup companies (for more details on the data see the Methods section and SI section  A.5 ).

Revisions of Crunchbase as a data source for investigations on a firm and industry level confirm the platform to be a useful and valuable source of data for startups research, as comparisons with other sources at micro-level, e.g., VentureXpert or PwC, also suggest that the platform’s coverage is very comprehensive, especially for start-ups located in the United States 21 . Moreover, aggregate statistics on funding rounds by country and year are quite similar to those produced with other established sources, going to validate the use of Crunchbase as a reliable source in terms of coverage of funded ventures. For instance, Crunchbase covers about the same number of investment rounds in the analogous sectors as collected by the National Venture Capital Association 22 . However, we acknowledge that the data source might suffer from registration latency (a certain delay between the foundation of the company and its actual registration on Crunchbase) and success bias in company status (the likeliness that failed companies decide to delete their profile from the database).

The definition of startup success

The success of startups is uncertain, dependent on many factors and can be measured in various ways. Due to the likelihood of failure in startups, some large-scale studies have looked at which features predict startup survival rates 23 , and others focus on fundraising from external investors at various stages 24 . Success for startups can be measured in multiple ways, such as the amount of external investment attracted, the number of new products shipped or the annual growth in revenue. But sometimes external investments are misguided, revenue growth can be short-lived, and new products may fail to find traction.

Success in a startup is typically staged and can appear in different forms and times. For example, a startup may be seen to be successful when it finds a clear solution to a widely recognised problem, such as developing a successful vaccine. On the other hand, it could be achieving some measure of commercial success, such as rapidly accelerating sales or becoming profitable or at least cash positive. Or it could be reaching an exit for foundation investors via a trade sale, acquisition or listing of its shares for sale on a public stock exchange via an Initial Public Offering (IPO).

For our study, we focused on the startup’s extrinsic success rather than the founders’ intrinsic success per se, as its more visible, objective and measurable. A frequently considered measure of success is the attraction of external investment by venture capitalists 25 . However, this is not in and of itself a good measure of clear, incontrovertible success, particularly for early-stage ventures. This is because it reflects investors’ expectations of a startup’s success potential rather than actual business success. Similarly, we considered other measures like revenue growth 26 , liquidity events 27 , 28 , 29 , profitability 30 and social impact 31 , all of which have benefits as they capture incremental success, but each also comes with operational measurement challenges.

Therefore, we apply the success definition initially introduced by Bonaventura et al. 32 , namely that a startup is acquired, acquires another company or has an initial public offering (IPO). We consider any of these major capital liquidation events as a clear threshold signal that the company has matured from an early-stage venture to becoming or is on its way to becoming a mature company with clear and often significant business growth prospects. Together these three major liquidity events capture the primary forms of exit for external investors (an acquisition or trade sale and an IPO). For companies with a longer autonomous growth runway, acquiring another company marks a similar milestone of scale, maturity and capability.

Using multifactor analysis and a binary classification prediction model of startup success, we looked at many variables together and their relative influence on the probability of the success of startups. We looked at seven categories of factors through three lenses of firm-level factors: (1) location, (2) industry, (3) age of the startup; founder-level factors: (4) number of founders, (5) gender of founders, (6) personality characteristics of founders and; lastly team-level factors: (7) founder-team personality combinations. The model performance and relative impacts on the probability of startup success of each of these categories of founders are illustrated in more detail in section  A.6 of the Supplementary Information (in particular Extended Data Fig.  19 and Extended Data Fig.  20 ). In total, we considered over three hundred variables (n = 323) and their relative significant associations with success.

The personality of founders

Besides product-market, industry, and firm-level factors (see SI section  A.1 ), research suggests that the personalities of founders play a crucial role in startup success 19 . Therefore, we examine the personality characteristics of individual startup founders and teams of founders in relationship to their firm’s success by applying the success definition used by Bonaventura et al. 32 .

Employing established methods 33 , 34 , 35 , we inferred the personality traits across 30 dimensions (Big Five facets) of a large global sample of startup founders. The startup founders cohort was created from a subset of founders from the global startup industry directory Crunchbase, who are also active on the social media platform Twitter.

To measure the personality of the founders, we used the Big Five, a popular model of personality which includes five core traits: Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Emotional stability. Each of these traits can be further broken down into thirty distinct facets. Studies have found that the Big Five predict meaningful life outcomes, such as physical and mental health, longevity, social relationships, health-related behaviours, antisocial behaviour, and social contribution, at levels on par with intelligence and socioeconomic status 36 Using machine learning to infer personality traits by analysing the use of language and activity on social media has been shown to be more accurate than predictions of coworkers, friends and family and similar in accuracy to the judgement of spouses 37 . Further, as other research has shown, we assume that personality traits remain stable in adulthood even through significant life events 38 , 39 , 40 . Personality traits have been shown to emerge continuously from those already evident in adolescence 41 and are not significantly influenced by external life events such as becoming divorced or unemployed 42 . This suggests that the direction of any measurable effect goes from founder personalities to startup success and not vice versa.

As a first investigation to what extent personality traits might relate to entrepreneurship, we use the personality characteristics of individuals to predict whether they were an entrepreneur or an employee. We trained and tested a machine-learning random forest classifier to distinguish and classify entrepreneurs from employees and vice-versa using inferred personality vectors alone. As a result, we found we could correctly predict entrepreneurs with 77% accuracy and employees with 88% accuracy (Fig.  1 A). Thus, based on personality information alone, we correctly predict all unseen new samples with 82.5% accuracy (See SI section  A.2 for more details on this analysis, the classification modelling and prediction accuracy).

We explored in greater detail which personality features are most prominent among entrepreneurs. We found that the subdomain or facet of Adventurousness within the Big Five Domain of Openness was significant and had the largest effect size. The facet of Modesty within the Big Five Domain of Agreeableness and Activity Level within the Big Five Domain of Extraversion was the subsequent most considerable effect (Fig.  1 B). Adventurousness in the Big Five framework is defined as the preference for variety, novelty and starting new things—which are consistent with the role of a startup founder whose role, especially in the early life of the company, is to explore things that do not scale easily 43 and is about developing and testing new products, services and business models with the market.

Once we derived and tested the Big Five personality features for each entrepreneur in our data set, we examined whether there is evidence indicating that startup founders naturally cluster according to their personality features using a Hopkins test (see Extended Data Figure  6 ). We discovered clear clustering tendencies in the data compared with other renowned reference data sets known to have clusters. Then, once we established the founder data clusters, we used agglomerative hierarchical clustering. This ‘bottom-up’ clustering technique initially treats each observation as an individual cluster. Then it merges them to create a hierarchy of possible cluster schemes with differing numbers of groups (See Extended Data Fig.  7 ). And lastly, we identified the optimum number of clusters based on the outcome of four different clustering performance measurements: Davies-Bouldin Index, Silhouette coefficients, Calinski-Harabas Index and Dunn Index (see Extended Data Figure  8 ). We find that the optimum number of clusters of startup founders based on their personality features is six (labelled #0 through to #5), as shown in Fig.  1 C.

To better understand the context of different founder types, we positioned each of the six types of founders within an occupation-personality matrix established from previous research 44 . This research showed that ‘each job has its own personality’ using a substantial sample of employees across various jobs. Utilising the methodology employed in this study, we assigned labels to the cluster names #0 to #5, which correspond to the identified occupation tribes that best describe the personality facets represented by the clusters (see Extended Data Fig.  9 for an overview of these tribes, as identified by McCarthy et al. 44 ).

Utilising this approach, we identify three ’purebred’ clusters: #0, #2 and #5, whose members are dominated by a single tribe (larger than 60% of all individuals in each cluster are characterised by one tribe). Thus, these clusters represent and share personality attributes of these previously identified occupation-personality tribes 44 , which have the following known distinctive personality attributes (see also Table  1 ):

Accomplishers (#0) —Organised & outgoing. confident, down-to-earth, content, accommodating, mild-tempered & self-assured.

Leaders (#2) —Adventurous, persistent, dispassionate, assertive, self-controlled, calm under pressure, philosophical, excitement-seeking & confident.

Fighters (#5) —Spontaneous and impulsive, tough, sceptical, and uncompromising.

We labelled these clusters with the tribe names, acknowledging that labels are somewhat arbitrary, based on our best interpretation of the data (See SI section  A.3 for more details).

For the remaining three clusters #1, #3 and #4, we can see they are ‘hybrids’, meaning that the founders within them come from a mix of different tribes, with no one tribe representing more than 50% of the members of that cluster. However, the tribes with the largest share were noted as #1 Experts/Engineers, #3 Fighters, and #4 Operators.

To label these three hybrid clusters, we examined the closest occupations to the median personality features of each cluster. We selected a name that reflected the common themes of these occupations, namely:

Experts/Engineers (#1) as the closest roles included Materials Engineers and Chemical Engineers. This is consistent with this cluster’s personality footprint, which is highest in openness in the facets of imagination and intellect.

Developers (#3) as the closest roles include Application Developers and related technology roles such as Business Systems Analysts and Product Managers.

Operators (#4) as the closest roles include service, maintenance and operations functions, including Bicycle Mechanic, Mechanic and Service Manager. This is also consistent with one of the key personality traits of high conscientiousness in the facet of orderliness and high agreeableness in the facet of humility for founders in this cluster.

Founder-Level Factors of Startup Success. ( A ), Successful entrepreneurs differ from successful employees. They can be accurately distinguished using a classifier with personality information alone. ( B ), Successful entrepreneurs have different Big Five facet distributions, especially on adventurousness, modesty and activity level. ( C ), Founders come in six different types: Fighters, Operators, Accomplishers, Leaders, Engineers and Developers (FOALED) ( D ), Each founder Personality-Type has its distinct facet.

Together, these six different types of startup founders (Fig.  1 C) represent a framework we call the FOALED model of founder types—an acronym of Fighters, Operators, Accomplishers, Leaders, Engineers and D evelopers.

Each founder’s personality type has its distinct facet footprint (for more details, see Extended Data Figure  10 in SI section  A.3 ). Also, we observe a central core of correlated features that are high for all types of entrepreneurs, including intellect, adventurousness and activity level (Fig.  1 D).To test the robustness of the clustering of the personality facets, we compare the mean scores of the individual facets per cluster with a 20-fold resampling of the data and find that the clusters are, overall, largely robust against resampling (see Extended Data Figure  11 in SI section  A.3 for more details).

We also find that the clusters accord with the distribution of founders’ roles in their startups. For example, Accomplishers are often Chief Executive Officers, Chief Financial Officers, or Chief Operating Officers, while Fighters tend to be Chief Technical Officers, Chief Product Officers, or Chief Commercial Officers (see Extended Data Fig.  12 in SI section  A.4 for more details).

The ensemble theory of success

While founders’ individual personality traits, such as Adventurousness or Openness, show to be related to their firms’ success, we also hypothesise that the combination, or ensemble, of personality characteristics of a founding team impacts the chances of success. The logic behind this reasoning is complementarity, which is proposed by contemporary research on the functional roles of founder teams. Examples of these clear functional roles have evolved in established industries such as film and television, construction, and advertising 45 . When we subsequently explored the combinations of personality types among founders and their relationship to the probability of startup success, adjusted for a range of other factors in a multi-factorial analysis, we found significantly increased chances of success for mixed foundation teams:

Initially, we find that firms with multiple founders are more likely to succeed, as illustrated in Fig.  2 A, which shows firms with three or more founders are more than twice as likely to succeed than solo-founded startups. This finding is consistent with investors’ advice to founders and previous studies 46 . We also noted that some personality types of founders increase the probability of success more than others, as shown in SI section  A.6 (Extended Data Figures  16 and 17 ). Also, we note that gender differences play out in the distribution of personality facets: successful female founders and successful male founders show facet scores that are more similar to each other than are non-successful female founders to non-successful male founders (see Extended Data Figure  18 ).

The Ensemble Theory of Team-Level Factors of Startup Success. ( A ) Having a larger founder team elevates the chances of success. This can be due to multiple reasons, e.g., a more extensive network or knowledge base but also personality diversity. ( B ) We show that joint personality combinations of founders are significantly related to higher chances of success. This is because it takes more than one founder to cover all beneficial personality traits that ‘breed’ success. ( C ) In our multifactor model, we show that firms with diverse and specific combinations of types of founders have significantly higher odds of success.

Access to more extensive networks and capital could explain the benefits of having more founders. Still, as we find here, it also offers a greater diversity of combined personalities, naturally providing a broader range of maximum traits. So, for example, one founder may be more open and adventurous, and another could be highly agreeable and trustworthy, thus, potentially complementing each other’s particular strengths associated with startup success.

The benefits of larger and more personality-diverse foundation teams can be seen in the apparent differences between successful and unsuccessful firms based on their combined Big Five personality team footprints, as illustrated in Fig.  2 B. Here, maximum values for each Big Five trait of a startup’s co-founders are mapped; stratified by successful and non-successful companies. Founder teams of successful startups tend to score higher on Openness, Conscientiousness, Extraversion, and Agreeableness.

When examining the combinations of founders with different personality types, we find that some ensembles of personalities were significantly correlated with greater chances of startup success—while controlling for other variables in the model—as shown in Fig.  2 C (for more details on the modelling, the predictive performance and the coefficient estimates of the final model, see Extended Data Figures  19 , 20 , and 21 in SI section  A.6 ).

Three combinations of trio-founder companies were more than twice as likely to succeed than other combinations, namely teams with (1) a Leader and two Developers , (2) an Operator and two Developers , and (3) an Expert/Engineer , Leader and Developer . To illustrate the potential mechanisms on how personality traits might influence the success of startups, we provide some examples of well-known, successful startup founders and their characteristic personality traits in Extended Data Figure  22 .

Startups are one of the key mechanisms for brilliant ideas to become solutions to some of the world’s most challenging economic and social problems. Examples include the Google search algorithm, disability technology startup Fingerwork’s touchscreen technology that became the basis of the Apple iPhone, or the Biontech mRNA technology that powered Pfizer’s COVID-19 vaccine.

We have shown that founders’ personalities and the combination of personalities in the founding team of a startup have a material and significant impact on its likelihood of success. We have also shown that successful startup founders’ personality traits are significantly different from those of successful employees—so much so that a simple predictor can be trained to distinguish between employees and entrepreneurs with more than 80% accuracy using personality trait data alone.

Just as occupation-personality maps derived from data can provide career guidance tools, so too can data on successful entrepreneurs’ personality traits help people decide whether becoming a founder may be a good choice for them.

We have learnt through this research that there is not one type of ideal ’entrepreneurial’ personality but six different types. Many successful startups have multiple co-founders with a combination of these different personality types.

To a large extent, founding a startup is a team sport; therefore, diversity and complementarity of personalities matter in the foundation team. It has an outsized impact on the company’s likelihood of success. While all startups are high risk, the risk becomes lower with more founders, particularly if they have distinct personality traits.

Our work demonstrates the benefits of personality diversity among the founding team of startups. Greater awareness of this novel form of diversity may help create more resilient startups capable of more significant innovation and impact.

The data-driven research approach presented here comes with certain methodological limitations. The principal data sources of this study—Crunchbase and Twitter—are extensive and comprehensive, but there are characterised by some known and likely sample biases.

Crunchbase is the principal public chronicle of venture capital funding. So, there is some likely sample bias toward: (1) Startup companies that are funded externally: self-funded or bootstrapped companies are less likely to be represented in Crunchbase; (2) technology companies, as that is Crunchbase’s roots; (3) multi-founder companies; (4) male founders: while the representation of female founders is now double that of the mid-2000s, women still represent less than 25% of the sample; (5) companies that succeed: companies that fail, especially those that fail early, are likely to be less represented in the data.

Samples were also limited to those founders who are active on Twitter, which adds additional selection biases. For example, Twitter users typically are younger, more educated and have a higher median income 47 . Another limitation of our approach is the potentially biased presentation of a person’s digital identity on social media, which is the basis for identifying personality traits. For example, recent research suggests that the language and emotional tone used by entrepreneurs in social media can be affected by events such as business failure 48 , which might complicate the personality trait inference.

In addition to sampling biases within the data, there are also significant historical biases in startup culture. For many aspects of the entrepreneurship ecosystem, women, for example, are at a disadvantage 49 . Male-founded companies have historically dominated most startup ecosystems worldwide, representing the majority of founders and the overwhelming majority of venture capital investors. As a result, startups with women have historically attracted significantly fewer funds 50 , in part due to the male bias among venture investors, although this is now changing, albeit slowly 51 .

The research presented here provides quantitative evidence for the relevance of personality types and the diversity of personalities in startups. At the same time, it brings up other questions on how personality traits are related to other factors associated with success, such as:

Will the recent growing focus on promoting and investing in female founders change the nature, composition and dynamics of startups and their personalities leading to a more diverse personality landscape in startups?

Will the growth of startups outside of the United States change what success looks like to investors and hence the role of different personality traits and their association to diverse success metrics?

Many of today’s most renowned entrepreneurs are either Baby Boomers (such as Gates, Branson, Bloomberg) or Generation Xers (such as Benioff, Cannon-Brookes, Musk). However, as we can see, personality is both a predictor and driver of success in entrepreneurship. Will generation-wide differences in personality and outlook affect startups and their success?

Moreover, the findings shown here have natural extensions and applications beyond startups, such as for new projects within large established companies. While not technically startups, many large enterprises and industries such as construction, engineering and the film industry rely on forming new project-based, cross-functional teams that are often new ventures and share many characteristics of startups.

There is also potential for extending this research in other settings in government, NGOs, and within the research community. In scientific research, for example, team diversity in terms of age, ethnicity and gender has been shown to be predictive of impact, and personality diversity may be another critical dimension 52 .

Another extension of the study could investigate the development of the language used by startup founders on social media over time. Such an extension could investigate whether the language (and inferred psychological characteristics) change as the entrepreneurs’ ventures go through major business events such as foundation, funding, or exit.

Overall, this study demonstrates, first, that startup founders have significantly different personalities than employees. Secondly, besides firm-level factors, which are known to influence firm success, we show that a range of founder-level factors, notably the character traits of its founders, significantly impact a startup’s likelihood of success. Lastly, we looked at team-level factors. We discovered in a multifactor analysis that personality-diverse teams have the most considerable impact on the probability of a startup’s success, underlining the importance of personality diversity as a relevant factor of team performance and success.

Data sources

Entrepreneurs dataset.

Data about the founders of startups were collected from Crunchbase (Table  2 ), an open reference platform for business information about private and public companies, primarily early-stage startups. It is one of the largest and most comprehensive data sets of its kind and has been used in over 100 peer-reviewed research articles about economic and managerial research.

Crunchbase contains data on over two million companies - mainly startup companies and the companies who partner with them, acquire them and invest in them, as well as profiles on well over one million individuals active in the entrepreneurial ecosystem worldwide from over 200 countries and spans. Crunchbase started in the technology startup space, and it now covers all sectors, specifically focusing on entrepreneurship, investment and high-growth companies.

While Crunchbase contains data on over one million individuals in the entrepreneurial ecosystem, some are not entrepreneurs or startup founders but play other roles, such as investors, lawyers or executives at companies that acquire startups. To create a subset of only entrepreneurs, we selected a subset of 32,732 who self-identify as founders and co-founders (by job title) and who are also publicly active on the social media platform Twitter. We also removed those who also are venture capitalists to distinguish between investors and founders.

We selected founders active on Twitter to be able to use natural language processing to infer their Big Five personality features using an open-vocabulary approach shown to be accurate in the previous research by analysing users’ unstructured text, such as Twitter posts in our case. For this project, as with previous research 20 , we employed a commercial service, IBM Watson Personality Insight, to infer personality facets. This service provides raw scores and percentile scores of Big Five Domains (Openness, Conscientiousness, Extraversion, Agreeableness and Emotional Stability) and the corresponding 30 subdomains or facets. In addition, the public content of Twitter posts was collected, and there are 32,732 profiles that each had enough Twitter posts (more than 150 words) to get relatively accurate personality scores (less than 12.7% Average Mean Absolute Error).

The entrepreneurs’ dataset is analysed in combination with other data about the companies they founded to explore questions about the nature and patterns of personality traits of entrepreneurs and the relationships between these patterns and company success.

For the multifactor analysis, we further filtered the data in several preparatory steps for the success prediction modelling (for more details, see SI section  A.5 ). In particular, we removed data points with missing values (Extended Data Fig.  13 ) and kept only companies in the data that were founded from 1990 onward to ensure consistency with previous research 32 (see Extended Data Fig.  14 ). After cleaning, filtering and pre-processing the data, we ended up with data from 25,214 founders who founded 21,187 startup companies to be used in the multifactor analysis. Of those, 3442 startups in the data were successful, 2362 in the first seven years after they were founded (see Extended Data Figure  15 for more details).

Entrepreneurs and employees dataset

To investigate whether startup founders show personality traits that are similar or different from the population at large (i. e. the entrepreneurs vs employees sub-analysis shown in Fig.  1 A and B), we filtered the entrepreneurs’ data further: we reduced the sample to those founders of companies, which attracted more than US$100k in investment to create a reference set of successful entrepreneurs (n \(=\) 4400).

To create a control group of employees who are not also entrepreneurs or very unlikely to be of have been entrepreneurs, we leveraged the fact that while some occupational titles like CEO, CTO and Public Speaker are commonly shared by founders and co-founders, some others such as Cashier , Zoologist and Detective very rarely co-occur seem to be founders or co-founders. To illustrate, many company founders also adopt regular occupation titles such as CEO or CTO. Many founders will be Founder and CEO or Co-founder and CTO. While founders are often CEOs or CTOs, the reverse is not necessarily true, as many CEOs are professional executives that were not involved in the establishment or ownership of the firm.

Using data from LinkedIn, we created an Entrepreneurial Occupation Index (EOI) based on the ratio of entrepreneurs for each of the 624 occupations used in a previous study of occupation-personality fit 44 . It was calculated based on the percentage of all people working in the occupation from LinkedIn compared to those who shared the title Founder or Co-founder (See SI section  A.2 for more details). A reference set of employees (n=6685) was then selected across the 112 different occupations with the lowest propensity for entrepreneurship (less than 0.5% EOI) from a large corpus of Twitter users with known occupations, which is also drawn from the previous occupational-personality fit study 44 .

These two data sets were used to test whether it may be possible to distinguish successful entrepreneurs from successful employees based on the different patterns of personality traits alone.

Hierarchical clustering

We applied several clustering techniques and tests to the personality vectors of the entrepreneurs’ data set to determine if there are natural clusters and, if so, how many are the optimum number.

Firstly, to determine if there is a natural typology to founder personalities, we applied the Hopkins statistic—a statistical test we used to answer whether the entrepreneurs’ dataset contains inherent clusters. It measures the clustering tendency based on the ratio of the sum of distances of real points within a sample of the entrepreneurs’ dataset to their nearest neighbours and the sum of distances of randomly selected artificial points from a simulated uniform distribution to their nearest neighbours in the real entrepreneurs’ dataset. The ratio measures the difference between the entrepreneurs’ data distribution and the simulated uniform distribution, which tests the randomness of the data. The range of Hopkins statistics is from 0 to 1. The scores are close to 0, 0.5 and 1, respectively, indicating whether the dataset is uniformly distributed, randomly distributed or highly clustered.

To cluster the founders by personality facets, we used Agglomerative Hierarchical Clustering (AHC)—a bottom-up approach that treats an individual data point as a singleton cluster and then iteratively merges pairs of clusters until all data points are included in the single big collection. Ward’s linkage method is used to choose the pair of groups for minimising the increase in the within-cluster variance after combining. AHC was widely applied to clustering analysis since a tree hierarchy output is more informative and interpretable than K-means. Dendrograms were used to visualise the hierarchy to provide the perspective of the optimal number of clusters. The heights of the dendrogram represent the distance between groups, with lower heights representing more similar groups of observations. A horizontal line through the dendrogram was drawn to distinguish the number of significantly different clusters with higher heights. However, as it is not possible to determine the optimum number of clusters from the dendrogram, we applied other clustering performance metrics to analyse the optimal number of groups.

A range of Clustering performance metrics were used to help determine the optimal number of clusters in the dataset after an apparent clustering tendency was confirmed. The following metrics were implemented to evaluate the differences between within-cluster and between-cluster distances comprehensively: Dunn Index, Calinski-Harabasz Index, Davies-Bouldin Index and Silhouette Index. The Dunn Index measures the ratio of the minimum inter-cluster separation and the maximum intra-cluster diameter. At the same time, the Calinski-Harabasz Index improves the measurement of the Dunn Index by calculating the ratio of the average sum of squared dispersion of inter-cluster and intra-cluster. The Davies-Bouldin Index simplifies the process by treating each cluster individually. It compares the sum of the average distance among intra-cluster data points to the cluster centre of two separate groups with the distance between their centre points. Finally, the Silhouette Index is the overall average of the silhouette coefficients for each sample. The coefficient measures the similarity of the data point to its cluster compared with the other groups. Higher scores of the Dunn, Calinski-Harabasz and Silhouette Index and a lower score of the Davies-Bouldin Index indicate better clustering configuration.

Classification modelling

Classification algorithms.

To obtain a comprehensive and robust conclusion in the analysis predicting whether a given set of personality traits corresponds to an entrepreneur or an employee, we explored the following classifiers: Naïve Bayes, Elastic Net regularisation, Support Vector Machine, Random Forest, Gradient Boosting and Stacked Ensemble. The Naïve Bayes classifier is a probabilistic algorithm based on Bayes’ theorem with assumptions of independent features and equiprobable classes. Compared with other more complex classifiers, it saves computing time for large datasets and performs better if the assumptions hold. However, in the real world, those assumptions are generally violated. Elastic Net regularisation combines the penalties of Lasso and Ridge to regularise the Logistic classifier. It eliminates the limitation of multicollinearity in the Lasso method and improves the limitation of feature selection in the Ridge method. Even though Elastic Net is as simple as the Naïve Bayes classifier, it is more time-consuming. The Support Vector Machine (SVM) aims to find the ideal line or hyperplane to separate successful entrepreneurs and employees in this study. The dividing line can be non-linear based on a non-linear kernel, such as the Radial Basis Function Kernel. Therefore, it performs well on high-dimensional data while the ’right’ kernel selection needs to be tuned. Random Forest (RF) and Gradient Boosting Trees (GBT) are ensembles of decision trees. All trees are trained independently and simultaneously in RF, while a new tree is trained each time and corrected by previously trained trees in GBT. RF is a more robust and straightforward model since it does not have many hyperparameters to tune. GBT optimises the objective function and learns a more accurate model since there is a successive learning and correction process. Stacked Ensemble combines all existing classifiers through a Logistic Regression. Better than bagging with only variance reduction and boosting with only bias reduction, the ensemble leverages the benefit of model diversity with both lower variance and bias. All the above classification algorithms distinguish successful entrepreneurs and employees based on the personality matrix.

Evaluation metrics

A range of evaluation metrics comprehensively explains the performance of a classification prediction. The most straightforward metric is accuracy, which measures the overall portion of correct predictions. It will mislead the performance of an imbalanced dataset. The F1 score is better than accuracy by combining precision and recall and considering the False Negatives and False Positives. Specificity measures the proportion of detecting the true negative rate that correctly identifies employees, while Positive Predictive Value (PPV) calculates the probability of accurately predicting successful entrepreneurs. Area Under the Receiver Operating Characteristic Curve (AUROC) determines the capability of the algorithm to distinguish between successful entrepreneurs and employees. A higher value means the classifier performs better on separating the classes.

Feature importance

To further understand and interpret the classifier, it is critical to identify variables with significant predictive power on the target. Feature importance of tree-based models measures Gini importance scores for all predictors, which evaluate the overall impact of the model after cutting off the specific feature. The measurements consider all interactions among features. However, it does not provide insights into the directions of impacts since the importance only indicates the ability to distinguish different classes.

Statistical analysis

T-test, Cohen’s D and two-sample Kolmogorov-Smirnov test are introduced to explore how the mean values and distributions of personality facets between entrepreneurs and employees differ. The T-test is applied to determine whether the mean of personality facets of two group samples are significantly different from one another or not. The facets with significant differences detected by the hypothesis testing are critical to separate the two groups. Cohen’s d is to measure the effect size of the results of the previous t-test, which is the ratio of the mean difference to the pooled standard deviation. A larger Cohen’s d score indicates that the mean difference is greater than the variability of the whole sample. Moreover, it is interesting to check whether the two groups’ personality facets’ probability distributions are from the same distribution through the two-sample Kolmogorov-Smirnov test. There is no assumption about the distributions, but the test is sensitive to deviations near the centre rather than the tail.

Privacy and ethics

The focus of this research is to provide high-level insights about groups of startups, founders and types of founder teams rather than on specific individuals or companies. While we used unit record data from the publicly available data of company profiles from Crunchbase , we removed all identifiers from the underlying data on individual companies and founders and generated aggregate results, which formed the basis for our analysis and conclusions.

Data availability

A dataset which includes only aggregated statistics about the success of startups and the factors that influence is released as part of this research. Underlying data for all figures and the code to reproduce them are available on GitHub: https://github.com/Braesemann/FounderPersonalities . Please contact Fabian Braesemann ( [email protected] ) in case you have any further questions.

Change history

07 may 2024.

A Correction to this paper has been published: https://doi.org/10.1038/s41598-024-61082-7

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Acknowledgements

We thank Gary Brewer from BuiltWith ; Leni Mayo from Influx , Rachel Slattery from TeamSlatts and Daniel Petre from AirTree Ventures for their ongoing generosity and insights about startups, founders and venture investments. We also thank Tim Li from Crunchbase for advice and liaison regarding data on startups and Richard Slatter for advice and referrals in Twitter .

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Contributions

All authors designed research; All authors analysed data and undertook investigation; F.B. and F.S. led multi-factor analysis; P.M., X.G. and M.A.R. led the founder/employee prediction; M.L.K. led personality insights; X.G. collected and tabulated the data; X.G., F.B., and F.S. created figures; X.G. created final art, and all authors wrote the paper.

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Correspondence to Fabian Braesemann .

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The original online version of this Article was revised: The Data Availability section in the original version of this Article was incomplete, the link to the GitHub repository was omitted. Full information regarding the corrections made can be found in the correction for this Article.

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McCarthy, P.X., Gong, X., Braesemann, F. et al. The impact of founder personalities on startup success. Sci Rep 13 , 17200 (2023). https://doi.org/10.1038/s41598-023-41980-y

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Oliver Vonnegut, Tufts undergrad, wins top prize in Cornell journal

5/31/2024 By | Kathy Hovis , A&S Communications

Sophia Gottfried ’25 and Ethan Kovnat ’24 were a little flummoxed when one of the 101 submissions for LOGOS , Cornell’s undergraduate philosophy journal, mentioned BDSM (bondage and discipline, dominance and submission, sadism and masochism) in its first sentence.

“Who am I going to assign to read this paper?” Kovnat remembers thinking.

But after reading it, they and the other judges were no longer surprised, only impressed, as the paper offered “a nuanced examination of why people relinquish control.” They awarded it the top prize in this year’s journal, which will come out this summer and includes five papers. 

Kovnat knew that the submission came from Oliver Vonnegut , a rising senior at Tufts and grandson of the famous author Kurt Vonnegut, but the other judges had no idea, as the author’s names are removed from papers before they’re passed along to the judges, undergraduate members of the philosophy club. Cornell students are not eligible to submit papers to the journal.

Vonnegut’s essay, “I Do Not Want to Choose the Restaurant, Honey,” explores the dynamic between being a subject or an object in the context of BDSM and fascism. Its first line is this: “BDSM is the cure to fascism.”

“This paper embodies the philosophical values of clarity and argumentation, while also being an accessible read,” LOGOS editors note in their forward to this year’s journal edition. “It takes on the assumption in the wide philosophical canon that the most morally important part of being a good human being is to be a subject, and the will to objecthood is either perverted or just fundamentally not part of the human experience.”

Vonnegut said he wrote the paper after reading a host of philosophers, including Hannah Arendt, Friedrich Nietzsche, Simone de Beauvoir, Georg Hegel, Karl Marx, Sigmund Freud and Søren Kierkegaard. 

The paper isn’t really about BDSM or fascism, he said, but he uses those as tools to explain his thoughts. “I started noticing that a lot of philosophical theories really do focus on power and control, on humans who want power or have power,” he said, but he knew from research and his own personal experiences that sometimes people like to be told what to do. 

“It seems to me that at some point in life, everyone has wished to be smaller, less important—to disappear, even,” Vonnegut writes. “Everyone has made a mistake and wished for freedom from the consequences their autonomy elicited. Everyone has wished someone had stopped them from making that choice.”

His conclusion: “The characteristics of BDSM make it a space to express the will to object-hood while ultimately validating subject-hood, whereas the characteristics of fascism make it alluring to the will to object-hood, but ultimately self-sabotaging and ungratifying, analogous to an addiction.”

Along with Vonnegut’s arguments, LOGOS editors were also taken by the style of his writing.

“It was very tame and academic, but it was well written, it engaged with Beauvoir, it was entertaining and risqué enough to draw people in,” Gottfried said.

Vonnegut likes to engage with readers in a personal way, he said, and he likes to have fun, as evidenced by lines like these from the paper:

• “Look, look—just hear me out on this one. Okay, I know that it’s actually strong community ties and support [that are the cures to fascism]; but BDSM can at least help.”
• “When I speak about my theories and it seems as though I am saying, ‘The world is this way,’ please understand me as saying what I truly mean to, which is, ‘It helps me understand the world if I think of it as being this way’ … If this approach means my perspective only resonates with a few people, so be it. If it only resonates with me, well… won’t I feel special.”
• “Yes, it is finally time to talk about BDSM. My apologies to those of you who have been waiting patiently for the exciting bit.”

With much philosophical writing, Vonnegut said “it’s always a wrestling match trying to understand what’s being said.” He didn’t want this paper to be another example of that.

“If this paper is to have a point, let it be this: pay attention to yourself,” he writes. “We cannot just be one thing or another, we must always be a jumbled mess of subject and object. So be it! Just be careful, because the drive behind those pushes outward and inward is strong, and it can take you anywhere.”

Vonnegut said he didn’t really know his grandfather, who passed away in 2007 when he was 4 years old, but he’s read some of his work and heard stories from his father, Mark, who is Vonnegut’s son and has published three memoirs of his own.

“There’s tons of philosophical examination in his work,” he said of his grandfather, who studied at Cornell from 1940-43, but was drafted into World War II and never finished his degree. 

Vonnegut was honored that his paper was selected for the top spot in LOGOS.

“Philosophy has always been deeply personal to me,” he said. “I’ve always been very confused by a lot of things and never satisfied by the answers. I wanted to know why things work the way they do and philosophy seemed the most tolerant way to go about doing that.”

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New coral disease forecasting system led by UH team

Using ecological and marine environmental conditions, the Multi-Factor Coral Disease Risk product predicts the risk of two diseases across reefs in the central and western Pacific and along the east coast of Australia. — Photo courtesy UH

KAHULUI — Research led by the University of Hawaii at Manoa Hawaii Institute of Marine Biology (HIMB) has led to a new tool for forecasting coral disease that could help conservationists step in at the right times with key interventions. Ecological forecasts are critical tools for conserving and managing marine ecosystems, but few forecasting systems can account for the wide range of ecological complexities in near-real-time.

Using ecological and marine environmental conditions, the Multi-Factor Coral Disease Risk product predicts the risk of two diseases across reefs in the central and western Pacific and along the east coast of Australia. An article introducing the new tool was published in Ecological Applications.

The tool can be accessed through the U.S. National Oceanic and Atmospheric Administration (NOAA) Coral Reef Watch program, and can help end users detect early changes in the environment and better protect coral reef ecosystems.

“Partnering with NOAA Coral Reef Watch, our team developed ecological forecasts to predict the times and conditions when coral disease outbreaks are most likely to occur,” said NASA-funded Principal Investigator and HIMB Interim Director Megan Donahue.

“We are really excited about this new tool,” said lead author, Jamie Caldwell, of High Meadows Environmental Institute at Princeton University. “Users can employ this tool to make decisions about how to manage coral health, similar to how we use weather forecasts to decide how to pack for an upcoming trip.”

More than half a billion people depend on Earth’s coral reefs, and ensuring their resilience in the face of many threats is an ongoing challenge for managers. Tools like this help ensure these vital ecosystems survive.

Understanding localized risks

Insights gleaned from the tool can help managers better understand localized risks of coral disease and develop timely strategies for intervention.

“A key project element was the consultation with and input from coral reef managers from across the Pacific, including here in Australia,” said Professor Scott Heron, a collaborator from James Cook University. “We’ve also provided several training sessions in the various aspects of how the tool is used so that stakeholders in the varying locations have the best opportunity to inform effective reef management.”

Coral reefs are the most biologically diverse, species-rich marine ecosystem on Earth. They are culturally significant to Indigenous people throughout the world, and they provide food, jobs, recreation, medicine and coastline protection from storms and erosion. While disease is a natural part of marine ecosystems, increased runoff, global climate change and a slough of human impacts stress corals and cause disease.

The Multi-Factor Coral Disease Risk Product was developed by HIMB, in close collaboration with NOAA Coral Reef Watch, James Cook University, University of Newcastle and University of New South Wales.

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Modular, scalable hardware architecture for a quantum computer

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Quantum computers hold the promise of being able to quickly solve extremely complex problems that might take the world’s most powerful supercomputer decades to crack.

But achieving that performance involves building a system with millions of interconnected building blocks called qubits. Making and controlling so many qubits in a hardware architecture is an enormous challenge that scientists around the world are striving to meet.

Toward this goal, researchers at MIT and MITRE have demonstrated a scalable, modular hardware platform that integrates thousands of interconnected qubits onto a customized integrated circuit. This “quantum-system-on-chip” (QSoC) architecture enables the researchers to precisely tune and control a dense array of qubits. Multiple chips could be connected using optical networking to create a large-scale quantum communication network.

By tuning qubits across 11 frequency channels, this QSoC architecture allows for a new proposed protocol of “entanglement multiplexing” for large-scale quantum computing.

The team spent years perfecting an intricate process for manufacturing two-dimensional arrays of atom-sized qubit microchiplets and transferring thousands of them onto a carefully prepared complementary metal-oxide semiconductor (CMOS) chip. This transfer can be performed in a single step.

“We will need a large number of qubits, and great control over them, to really leverage the power of a quantum system and make it useful. We are proposing a brand new architecture and a fabrication technology that can support the scalability requirements of a hardware system for a quantum computer,” says Linsen Li, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this architecture.

Li’s co-authors include Ruonan Han, an associate professor in EECS, leader of the Terahertz Integrated Electronics Group, and member of the Research Laboratory of Electronics (RLE); senior author Dirk Englund, professor of EECS, principal investigator of the Quantum Photonics and Artificial Intelligence Group and of RLE; as well as others at MIT, Cornell University, the Delft Institute of Technology, the U.S. Army Research Laboratory, and the MITRE Corporation. The paper appears today in Nature .

Diamond microchiplets

While there are many types of qubits, the researchers chose to use diamond color centers because of their scalability advantages. They previously used such qubits to produce integrated quantum chips with photonic circuitry.

Qubits made from diamond color centers are “artificial atoms” that carry quantum information. Because diamond color centers are solid-state systems, the qubit manufacturing is compatible with modern semiconductor fabrication processes. They are also compact and have relatively long coherence times, which refers to the amount of time a qubit’s state remains stable, due to the clean environment provided by the diamond material.

In addition, diamond color centers have photonic interfaces which allows them to be remotely entangled, or connected, with other qubits that aren’t adjacent to them.

“The conventional assumption in the field is that the inhomogeneity of the diamond color center is a drawback compared to identical quantum memory like ions and neutral atoms. However, we turn this challenge into an advantage by embracing the diversity of the artificial atoms: Each atom has its own spectral frequency. This allows us to communicate with individual atoms by voltage tuning them into resonance with a laser, much like tuning the dial on a tiny radio,” says Englund.

This is especially difficult because the researchers must achieve this at a large scale to compensate for the qubit inhomogeneity in a large system.

To communicate across qubits, they need to have multiple such “quantum radios” dialed into the same channel. Achieving this condition becomes near-certain when scaling to thousands of qubits. To this end, the researchers surmounted that challenge by integrating a large array of diamond color center qubits onto a CMOS chip which provides the control dials. The chip can be incorporated with built-in digital logic that rapidly and automatically reconfigures the voltages, enabling the qubits to reach full connectivity.

“This compensates for the in-homogenous nature of the system. With the CMOS platform, we can quickly and dynamically tune all the qubit frequencies,” Li explains.

Lock-and-release fabrication

To build this QSoC, the researchers developed a fabrication process to transfer diamond color center “microchiplets” onto a CMOS backplane at a large scale.

They started by fabricating an array of diamond color center microchiplets from a solid block of diamond. They also designed and fabricated nanoscale optical antennas that enable more efficient collection of the photons emitted by these color center qubits in free space.

Then, they designed and mapped out the chip from the semiconductor foundry. Working in the MIT.nano cleanroom, they post-processed a CMOS chip to add microscale sockets that match up with the diamond microchiplet array.

They built an in-house transfer setup in the lab and applied a lock-and-release process to integrate the two layers by locking the diamond microchiplets into the sockets on the CMOS chip. Since the diamond microchiplets are weakly bonded to the diamond surface, when they release the bulk diamond horizontally, the microchiplets stay in the sockets.

“Because we can control the fabrication of both the diamond and the CMOS chip, we can make a complementary pattern. In this way, we can transfer thousands of diamond chiplets into their corresponding sockets all at the same time,” Li says.

The researchers demonstrated a 500-micron by 500-micron area transfer for an array with 1,024 diamond nanoantennas, but they could use larger diamond arrays and a larger CMOS chip to further scale up the system. In fact, they found that with more qubits, tuning the frequencies actually requires less voltage for this architecture.

“In this case, if you have more qubits, our architecture will work even better,” Li says.

The team tested many nanostructures before they determined the ideal microchiplet array for the lock-and-release process. However, making quantum microchiplets is no easy task, and the process took years to perfect.

“We have iterated and developed the recipe to fabricate these diamond nanostructures in MIT cleanroom, but it is a very complicated process. It took 19 steps of nanofabrication to get the diamond quantum microchiplets, and the steps were not straightforward,” he adds.

Alongside their QSoC, the researchers developed an approach to characterize the system and measure its performance on a large scale. To do this, they built a custom cryo-optical metrology setup.

Using this technique, they demonstrated an entire chip with over 4,000 qubits that could be tuned to the same frequency while maintaining their spin and optical properties. They also built a digital twin simulation that connects the experiment with digitized modeling, which helps them understand the root causes of the observed phenomenon and determine how to efficiently implement the architecture.

In the future, the researchers could boost the performance of their system by refining the materials they used to make qubits or developing more precise control processes. They could also apply this architecture to other solid-state quantum systems.

This work was supported by the MITRE Corporation Quantum Moonshot Program, the U.S. National Science Foundation, the U.S. Army Research Office, the Center for Quantum Networks, and the European Union’s Horizon 2020 Research and Innovation Program.

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SYSTEMATIC REVIEW article

Analyzing the impact of team-building interventions on team cohesion in sports teams: a meta-analysis study.

• Department of Physical Education, Yonsei University, Seoul, Republic of Korea

Introduction: Participation in team sports requires collaboration among multiple individuals over an extended period. Success in the game relies on more than just individual excellence; it necessitates effective teamwork. Team-building interventions have been shown to enhance team functioning, particularly in fostering cohesion among sports teams. This study aims to identify crucial factors in team-building interventions that contribute to improved team cohesion in sports teams.

Methods: A comprehensive meta-analysis of 15 articles was conducted to identify the crucial factors in team-building interventions that contribute to improved team cohesion in sports teams. The analysis focused on the age of participants, level of performance, and duration of interventions.

Results: The results of the analysis revealed that the positive impact of team-building was found to be most pronounced when the participants were between 15 and 20 years old, performed at collegiate teams, and engaged in interventions lasting more than 2 weeks. Among the four types of cohesion in sports teams, individual attraction to the group task (ATG-T) emerged as the aspect most influenced by team-building interventions.

Discussion: These findings provide valuable insights into the factors influencing the success of team-building interventions in enhancing team cohesion within sports teams.

1 Introduction

Psychological interventions in sports have proven effective in enhancing athletes’ skill development, team cohesion, and team performance. Among these interventions, team-building has emerged as a prominent strategy for promoting effective collaboration among team members, thereby strengthening cohesion and team performance in sports teams. This approach has been employed to optimize the functionality of sports teams, resulting in improved team performance.

This study aims to explore the impact of team-building interventions on cohesion within sports teams. While numerous investigations have reported favorable effects of team-building on team cohesion ( Cogan and Petrie, 1995 ; Prapavessis et al., 1996 ; Stevens and Bloom, 2003 ; Senécal et al., 2008 ; Kim and Kim, 2012 ; Durdubas and Koruc, 2023 ; Tassi et al., 2023 ), it remains challenging to assert that team-building interventions yield effective results. Some studies, such as those by Bloom and Stevens (2002) , Kilty (2000) , Kwon (2022) , Prapavessis et al. (1996) , and Rainey and Schweickert (1988) , did not report positive developments in group cohesion.

Moreover, improvements in cohesion achieved through team-building interventions were sometimes transient, with studies indicating that cohesion levels were not sustained throughout the season ( Cogan and Petrie, 1995 ; Stevens and Bloom, 2003 ). Drawing definitive findings about the effectiveness of team-building in sports is complicated by the diversity of methods and designs employed in these interventions, which yield unexpected results and necessitate an integrated examination of previous studies.

In the meta-analysis conducted by Carron et al. (2002) , the impact of team-building on four subgroups of cohesion – GI-T (group integration–task), GI-S (group integration–social), ATG-T (individual attractions to the group-task), and ATG-S (individual attractions to the group-social) – was examined, with reported effect sizes of 0.471, 0.349, 0.676, and 0.463. Martin et al. (2009) conducted a meta-analysis on team-building interventions within sports teams, reporting an effect size of 0.427. Their analysis revealed that team-building interventions had the most substantial impact on cognitions ( g  = 0.799), with goal setting as the exclusive method coming in second ( g  = 0.714). The effect sizes of task and social cohesion were 0.263 and 0.214.

While team-building is known to have a positive effect on team cohesion, in actual application, its implementation time is limited. Therefore, to ensure that the cohesion effect is evident in sports teams, understanding the factors that should be considered in team-building interventions is crucial. This study seeks to determine which moderator variables such as gender, age, athletes’ level, group size, and intervention duration, enhance the effect and which factors do not need to be considered.

2 Methodology

This methodology conforms to the relevant guidelines of the Preferred Reporting Items of Systematic Reviews and Meta-Analyses (PRISMA) Statement and ensures that the necessary scientific information is provided in the field ( Page et al., 2021 ).

2.1 Study selection and inclusion criteria

For this meta-analysis, literature selection focused on research studies examining the effectiveness of team-building interventions in interactive sport teams. The selection process followed rigorous and systematic procedures, incorporating keyword searches in computerized databases and employing a snowball sampling approach.

The computer-based search covered various databases, including PsychINFO, PsycARTICLES, SPORT Discus, and Google Scholar. This comprehensive search strategy involved using a range of keywords, such as “team-building in sport,” “team-building intervention in sport,” “team-building and cohesion,” and various combinations.

Two independent reviewers extracted the following data from each article: study design, total number of participants, gender, age, intervention duration, and athletes’ skill level. The accuracy of the extracted or calculated data was verified by comparing the data collection forms of the two investigators.

2.2 Dependent variables: cohesion

Team-building in sports teams can yield various outcomes, including enhanced cohesion. Carron and Spink (1993) developed a conceptual framework for team-building interventions in sports teams, designating group cohesiveness as the primary result of this process. Within this framework, four subgroups of cohesion, specifically GI-T, GI-S, ATG-T, and ATG-S, serve as dependent variables when assessing the impact of team-building interventions ( Eys and Kim, 2017 ).

2.3 Moderating factors

2.3.1 gender.

Within the studies under review, two distinct demographic cohorts were examined. Specifically, 5 studies with 38 cases focused on male participants, while 10 studies with 14 cases centered around female participants.

2.3.2 Age of participants

The participants’ ages were divided into three groups: under the age of 15, 15–20 years old, and over 20. Specifically, two studies with eight cases focused on participants under 15, while nine studies with 29 cases targeted the 15–20 age group, and four studies with 15 cases focused on participants over the age of 20.

2.3.3 Sample size

The sample size was categorized into three groups: under 20 participants, 20–30 participants, and over 30 participants. More specifically, five studies with 17 cases were centered on under 20 participants, while another five studies with 18 cases were aimed at the 20–30 participants group. Additionally, five studies with 17 cases were focused on participants comprising over 30 participants.

2.3.4 Skill level

The analysis covered a range of team proficiency levels. High school and collegiate teams were each represented in five studies with 16 effect sizes, whereas professional club teams were featured in five studies with 20 effect sizes.

2.3.5 Length of intervention

This study also investigated the duration of a team-building intervention as a potential moderator for their effectiveness. The intervention durations were classified into three groups: less than 2 weeks, 2 to 20 weeks, and 20 weeks or more. There were 8 studies with 32 cases that fell within the 2 to 20 weeks category, while 6 studies with 18 cases had intervention lasting over 20 weeks. Additionally, one study with two cases had an intervention duration of less than 2 weeks.

2.4 Coding methodology

Following established norms for meta-analytic research, we meticulously designed our coding procedure to thoughtfully capture and quantify crucial study characteristics and outcomes. Our comprehensive coding approach involved systematically extracting 11 essential pieces of information from each study. This included details such as authorship, year of publication, study setting, study design type, sport type, duration of intervention, athletes’ skill level, gender of participants, number of participants in experimental and control groups, means and standard deviations of intervention effectiveness at pretest and posttest, as well as effect size or measures of effectiveness.

2.5 Effect size calculations

The computation of effect sizes was conducted using R-4.3.2 for Windows. 1 This program provides various options for calculating effect sizes, and we chose Hedges g ( Hedges and Olkin, 2014 ), an effect size adjusted to consider differences in sample size and sample variance. In interpreting the magnitude of effect sizes, we followed Cohen’s (1988) guidelines. Specifically, a Hedges g of 0.80 was considered a large effect size, 0.50 signified a medium effect size, and 0.20 indicated a small effect size.

3.1 Study selection

Following a database search, a total of 1,928 documents were initially identified, with 35 documents found through snowballing methods. After removing duplicates, 1,752 articles remained. Subsequently, 525 articles were excluded based on title screening. Application of the inclusion criteria led to the exclusion of an additional 664 articles. This left us with 121 articles that underwent full-text screening, focusing on articles potentially relevant to the impact of team-building interventions on cohesion in sports teams. To ensure methodological rigor, studies lacking the necessary statistical information for calculating effect sizes were excluded from the meta-analysis. Following these criteria, a total of 15 studies, comprising 52 cases, were considered eligible for inclusion in the meta-analysis (refer to Figure 1 for details).

Figure 1 . Flowchart of the systematic review process according to the PRISMA protocol declarations.

3.2 Assessment of risk of bias

To assess the risk of bias in the included articles, we used the Cochrane Risk of Bias Tool ( Higgins and Altman, 2008 ). This tool assesses each article based on a checklist comprising five items: randomization process, deviation from the intended intervention, missing outcome data, measurement of the outcome, and selection of the reported result. We then categorized each article’s overall bias risk as low risk (indicating low risk across all items), some concerns, and high risk (indicating high risk of bias in at least one domain). Low risk indicates better methodological quality, while high risk suggests a high risk of bias.

Figure 2 provides a visual representation of risk of bias evaluations for each domain of the Cochrane Risk of Bias tool. Out of all included articles, 1 article (6.7%) had a low overall risk of bias, while 14 articles (93.3%) exhibited a high overall risk of bias. However, except for the randomization process domain, the other four checklist items showed low risk across all 15 articles.

Figure 2 . Assessment of risk of bias in the included studies.

The high prevalence of ‘high’ risk is attributed to the inherent challenges in randomly selecting teams, particularly in studies involving interactive sports teams. This difficulty arises from the complexities associated with randomly assigning teams in research focused on sports team dynamics.

3.3 Overall analysis

3.3.1 overall effect size.

The meta-analysis results, drawn from 52 individual cases extracted from 15 papers, are presented in Table 1 . The table covers both the overall analysis and outcomes related to five moderating variables influencing cohesion. Additionally, Figure 3 illustrates a forest plot depicting effect sizes for the 52 individual cases. The overall analysis of these cases showed a significant moderate effect size (ES = 0.65, 95% CI = [0.40; 0.91]) of team-building intervention on cohesion. Additionally, the I 2 heterogeneity statistic indicated a significant level of heterogeneity at 96.9%.

Table 1 . Effect sizes of dependent variables.

Figure 3 . Forest plot of meta-analysis for team building intervention on cohesion in sports teams. The individual effect sizes are identified as Hedges g with lower and upper limits of 95% CIs.

3.3.2 Publication bias

To assess the potential presence of publication bias in our meta-analysis of team-building intervention on cohesion, we utilized a funnel plot for visual examination, as illustrated in Figure 4 . In an ideal scenario without publication bias, data points (depicted as solid circles) from individual case studies would exhibit a symmetrical distribution. Any deviation from this symmetry suggests the potential presence of publication bias. As seen in Figure 4 , the distribution of effect sizes is slightly left–skewed.

Figure 4 . Funnel plot of standard error by Hedges g.

Applying the trim-and-fill method by Duval and Tweedie (2000) reveals that 15 missing studies on the right side are required to achieve symmetry in the funnel plot. The required 15 additional cases are shown on the right as hollow circles in Figure 4 .

We also assessed publication bias using Rosenthal’s (1979) fail-safe N (N fs ) concept. When N fs exceeds 5 k + 10, where k represents the number of included case studies, it is unlikely to substantially impact the average effect size. In our specific study, with k equal to 52, the meta-analysis results remain stable as long as the N fs exceeds 270. Our N fs value is 2,570, well above the 270 threshold, emphasizing the robustness of the meta-analysis. In simpler terms, even if more than 2,570 studies with zero effect size were introduced, the overall results would remain largely unaltered.

According to the trim-and-fill method by Duval and Tweedie (2000) , an adjusted effect size of 1.00 (95% CI = [0.75; 1.25]), larger than the calculated effect size of 0.65, is presented.

3.4 Type of cohesion measure

Table 1 presents 52 effect sizes calculated for four cohesion types (GI-T, GI-S, ATG-T, and ATG-S). Notably, task cohesion exhibited a larger effect size than social cohesion. ATG-T showed a significant large effect size (ES = 1.06, 95% CI = [0.17; 1.95]). The other three cohesion types, GI-T (ES = 0.56, 95% CI = [0.23; 0.89]), ATG-S (ES = 0.56, 95% CI = [0.22; 0.91]), and GI-S (ES = 0.52, 95% CI = [0.01; 1.02]), showed a moderate effect size. According to meta-ANOVA, the differences between the four cohesion types were not statistically significant ( F (3, 48) = 1.312, p  > 0.05).

3.5 Moderator variables

This study examined the effectiveness of team-building concerning five different moderators. These moderators encompassed the effectiveness of team-building on cohesion across gender, age, sample size, intervention duration, and athletes’ skill level. Notably, the only significant moderator identified was athletes’ skill level. No statistically significant differences were observed within the other four moderators (refer to Table 2 for details).

Table 2 . Moderator effects.

3.5.1 Gender

As indicated in Table 2 , there is a slightly larger effect size for male athlete teams (ES = 0.66, 95% CI = [0.32; 1.00]) compared to female athlete teams (ES = 0.63, 95% CI = [0.43; 0.84]). However, the difference is not statistically significant ( p  > 0.05).

We categorized the ages of the participants into three groups. In the 15–20 years old category, we observed a significant large effect size (ES = 0.88, 95% CI = [0.60; 1.15]), while those under the age of 15 showed a significant moderate effect size (ES = 0.48, 95% CI = [0.17; 0.78]). However, the effect size (ES = 0.25, 95% CI = [−0.45; 0.96]) for those over the age of 20 was not statistically significant. The meta-ANOVA analysis indicated that the difference between these three categories was not statistically significant ( p  > 0.05). Consequently, age was not identified as a significant moderator in this study.

3.5.3 Sample size

The sample size was divided into three groups. In the category with 20–30 participants, we observed a significant large effect size (ES = 0.85, 95% CI = [0.49; 1.21]). Additionally, the category with under 20 participants showed a significant moderate effect size (ES = 0.64, 95% CI = [0.49; 1.21]). However, the effect size (ES = 0.50, 95% CI = [−0.03; 1.31]) for those over 30 participants was not statistically significant. The meta-ANOVA analysis indicated that the difference between these three categories was not statistically significant ( p  > 0.05). Consequently, the sample size was not identified as a significant moderator in this study.

3.5.4 Length of intervention

The team-building interventions in our study varied in duration, ranging from 1 day to the entire sports season. As shown in Table 2 , a significant moderate effect size (ES = 0.69, 95% CI = [0.31; 1.06]) was observed for interventions lasting between 2 and 20 weeks. Additionally, a significant moderate effect size was evident for interventions extending for 20 weeks or longer (ES = 0.62, 95% CI = [0.43; 0.82]). However, the effect size (ES = 0.31, 95% CI = [−0.97; 1.60]) for intervention durations less than 2 weeks was not statistically significant. The meta-ANOVA analysis indicated that the difference between these three categories was not statistically significant ( p  > 0.05). Consequently, the length of intervention was not identified as a significant moderator in this study.

3.5.5 Skill level of the athletes

As outlined in Table 2 , we observed a significant large effect size (ES = 1.13, 95% CI = [0.53; 1.72]) in the category of collegiate teams, while we identified a significant moderate effect size (ES = 0.77, 95% CI = [0.59; 0.95]) in the category of high school teams. However, the effect size (ES = 0.40, 95% CI = [−0.02; 0.83]) was not statistically significant for professional teams.

According to the meta ANOVA and post-hoc test results, significant differences ( p  < 0.05) in the effectiveness of team-building on cohesion were found between collegiate teams and professional teams. Consequently, athletes’ skill level can act as a moderator in the effectiveness of team-building intervention on cohesion.

3.6 Meta-regression analysis

We conducted meta-regression analyses to explore the association between three independent variables (age, sample size, and duration in weeks) and the effect size. The results of meta-regression analysis showed that the effect size tend to decrease with mean age, although this association did not reach statistical significance ( p > 0.05) (refer to Figure 5 and Table 3 ). Furthermore, the relationships between sample size and effect sizes, as well as the relationship between duration in weeks and effect sizes, did not show statistical significance ( p > 0.05) (refer to Table 3 ).

Figure 5 . Meta-regression analysis of the relationship between Hedges’ g and the mean age of participants.

Table 3 . Univariate meta-regression analysis.

4 Discussion

The main goal of this meta-analysis is to assess the impact of team-building interventions on cohesion, a critical element in sports teams that plays a pivotal role in task execution and fostering social interactions ( Carron and Spink, 1993 ; Carron et al., 1997 ). If team-building interventions focused on fostering cohesion can establish a sense of unity among team members, they have the potential to serve as catalysts for enhancing overall team performance.

Our study’s key finding is that team-building activities indeed improve cohesion in sports teams. Among various measures of cohesion, we found that team-building interventions were most successful in enhancing ATG-T, followed by GI-T, GI-S, and ATG-S. Some team-building activities focus on social aspects, like team camping trips ( Cogan and Petrie, 1995 ), ropes and challenge courses (e.g., Meyer, 2000 ), and informal social gatherings (e.g., Yukelson, 1997 ). These activities are likely to enhance social cohesion within the team. On the other hand, other team-building activities concentrated on team goals and tasks, such as team goal-setting (e.g., Senécal et al., 2008 ; Kim et al., 2017 ; Durdubas and Koruc, 2023 ), tasks relevant to team performance (e.g., Leo et al., 2021 ), clarifying roles (e.g., Tassi et al., 2023 ), and adhering to team norms (e.g., Prapavessis et al., 1996 ). These activities are expected to primarily improve task cohesion within the team. Notably, our analysis revealed a stronger impact of team-building activities on task cohesion compared to social cohesion due to the predominant focus on tasks and objectives rather than social interactions in the studies examined.

Another aim of our study is to explore how various moderator variables affect the improvement of cohesion through team-building intervention. Several findings are associated with the influence of moderators. To begin with, we explored gender as a potential moderator. The findings indicate that team-building interventions are equally effective for teams composed solely of females as well as those with only males. In our meta-analysis, using gender served as a potential moderator, the results of the meta t -test showed no significant difference ( p  > 0.05) in the effectiveness of team-building interventions applied to both men’s and women’s teams. This aligns with the results reported by Martin et al. (2009) .

In this study, the second potential modulator under scrutiny was the age of participants. We categorized subjects of individual study into three age groups, and then the effect size was calculated with age as a moderate variable. We found that the age category of 15–20 exhibited a large effect size, while the category under 15 years old showed a significant moderate effect size. However, there was no significant effect size observed for the category of those aged over 20 years. Consequently, we can conclude that team-building is most effective for sports teams with members between 15 and 20 years old, while it does not show effectiveness for sports teams with members aged over 20.

In our analysis, the third potential modulator we explored was sample size. We classified the sample size of each study into three groups, and then the effect size was calculated with sample size as a moderate variable. In the group with 20–30 participants, a significant large effect size was observed, while the category with under 20 participants showed a significant moderate effect size. However, there was no significant effect size observed for the category of those with over 30 participants. As a result, we can conclude that team-building is most effective for sports teams ranging from 20 to 30 members, while it does not show effectiveness for sports groups with over 30 members.

In our analysis, the fourth potential modulator we explored was athletes’ skill level, which turned out to be the only significant moderator in this study. Team-building interventions were most effective for collegiate teams, followed by high school teams, while the effectiveness in professional teams did not reach statistical significance ( p  > 0.05). This discrepancy may be explained by a potential ceiling effect, given that professional athletes typically possess a strong understanding of cohesion. Consequently, while professional teams do benefit from team-building interventions, the extent of improvement may be comparatively modest due to their already robust cohesion and training. The meta-ANOVA indicated that the differences between the three groups were statistically significant ( p  < 0.05), and the post-hoc test revealed that the effect size of the collegiate team was larger than that of the professional club team. Thus, it can be concluded that team-building is most effective for collegiate sports teams, while it does not show effectiveness for professional club teams.

Moving on to the fifth potential modulator, we explored intervention duration. The articles in this meta-analysis encompassed team-building interventions with durations ranging from a single day to an entire sports season. Notably, interventions lasting less than 2 weeks did not yield noticeable improvements in cohesion and were not statistically significant, aligning with the findings of Martin et al. (2009) . Conversely, Shipherd et al. (2014) conducted a single-day team-building intervention with a collegiate rugby team and observed a significant increase in team cohesion. These disparities in intervention duration underscore the need for meta-analytic investigations to gain a comprehensive understanding of the optimal duration required for team-building interventions to enhance cohesion in future studies.

Although numerous studies have demonstrated the positive effects of team-building interventions on cohesion, there are instances, as seen in some studies ( Prapavessis et al., 1996 ; Kwon, 2022 ), where significant improvements were not observed. The intervention period might have impacted why there wasn’t a significant change in group cohesion after the team-building program was implemented. Kwon (2022) and Prapavessis et al. (1996) conducted a team-building intervention over 8 weeks but did not find a clear improvement in group cohesion. This suggests that the intervention duration might have been too short to see significant differences in these studies. Group cohesion improves gradually through changing members’ perceptions and resolving conflicts that arise during interactions. Therefore, steady progress over a long enough time is important. However, conducting long-term team-building interventions can be challenging due to various environmental factors.

5 Conclusion

In conclusion, this study provides several key insights into the impact of team-building intervention on cohesion within sports teams. Firstly, team-building activities predominantly enhance task cohesion rather than social cohesion within sports teams. Different approaches to team-building, focusing on either social interactions or team goals and tasks, result in corresponding improvements in cohesion. Thus, social cohesion benefits from team-building activities emphasizing social interaction, while task cohesion improves when activities concentrate on team objectives.

Secondly, team-building interventions are most effective for individuals aged 15–20 and within collegiate sports teams. Conversely, the expected positive effects may not be noticeable when subjects are over 20 years old and belong to professional league teams.

Thirdly, interventions lasting longer than 2 weeks are crucial for enhancing team cohesion. Conversely, the expected positive effects may not be noticeable if the intervention period is less than 2 weeks. Based on our findings, an intervention period of at least 2 weeks is necessary to see the effects of a team-building intervention on group cohesion in sports teams. However, it is not necessarily the case that a longer intervention period will result in a greater intervention effect. Additionally, the time delay of the intervention was not investigated in this study. Therefore, the association between the team-building intervention period and group cohesion remains unclear. Further research is needed to determine the optimal intervention period that significantly affects group cohesion. It is also important to consider the time delay of intervention. Furthermore, there is possibility that a group cohesion may be influenced by multiple processes rather than just team-building alone. Therefore, claiming that team-building alone enhances group cohesion may not be reasonable. Therefore, decision-makers in sports teams should carefully consider the duration and realistic expectations of team-building interventions. In any case, to have an effective team-building intervention, it is necessary to implement the intervention for a long enough period. To address this, leaders should ensure interventions are implemented over a sufficient period to yield meaningful results.

In summary, team-building interventions can significantly enhance cohesion within sports teams, particularly when tailored to specific team dynamics and implemented over a sufficient duration.

Nevertheless, it’s important to note the limitations of this meta-analysis. First and foremost, the study focused exclusively on interactive sports, suggesting the need for future research to explore and compare the effectiveness of team-building interventions in both interactive and coactive sports settings. Secondly, the review concentrated solely on immediate post-intervention effects, emphasizing the necessity for longitudinal studies to gain a more profound understanding of the lasting benefits of team-building interventions for sports teams over an extended period.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the author, without undue reservation.

Author contributions

SK: Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: group cohesion, group-based intervention, interactive sports, meta-analysis, teambuilding

Citation: Kwon SH (2024) Analyzing the impact of team-building interventions on team cohesion in sports teams: a meta-analysis study. Front. Psychol . 15:1353944. doi: 10.3389/fpsyg.2024.1353944

Received: 11 December 2023; Accepted: 04 March 2024; Published: 15 March 2024.

Reviewed by:

Copyright © 2024 Kwon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sang Hyun Kwon, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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