Risk and protective factors for mental health in elite athletes: a scoping review

Abstract

The purpose of this scoping review was to provide an overview of studies concerning the mental health (MH) of elite athletes and to provide a methodological, conceptual, and applied overview of factors affecting elite athletes’ MH. A total of nine reviews and 43 empirical studies were evaluated and are reported in three sections: sample characteristics, research design, and factors affecting elite athletes’ MH. These factors were grouped into risk and protective factors and divided into a personal and sport-environmental domain. The studies used quantitative (84%), qualitative (11%), and mixed-method (5%) designs and examined a wide range of sports in different countries. Our review shows that researchers have predominantly examined the prevalence of athletes’ mental ill-health (e.g. depression) and the related factors compromising MH. Potential protective factors such as the feeling of autonomy, positive relations in sport and private life, and adequate recovery were highlighted in the qualitative studies. The discussion appraises the findings through a critical lens, focuses on the current state of the research area and the MH definition, limitations, suggested practical implications (e.g. to provide MH literacy to both athletes and coaches), and future research directions (e.g. examining environments that foster elite athletes’ MH).

Full text

Factors affecting the mental health of elite athletes
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Risk and Protective Factors for Mental Health in Elite Athletes: A Scoping Review
A. Kuettela*, C. H. Larsena
a Department of Sports Science and Clinical Biomechanics, University of Southern Denmark,
Denmark
Andreas Kuettel (*corresponding author) ORCID: 0000-0003-0235-3590
Assistant Professor, Ph.D.
Department of Sports Science and Clinical Biomechanics
University of Southern Denmark
Campusvej 55, DK-5230 Odense M, Denmark
Phone: +45-50-172897 https://www.linkedin.com/in/andreas-küttel-b7682356
E-mail: akuttel@health.sdu.dk or andreaskuettel@bluewin.ch
Carsten Hvid Larsen
Associate Professor, Ph.D. ORCID: 0000-0003-3309-9417
Department of Sports Science and Clinical Biomechanics
University of Southern Denmark
Campusvej 55, DK-5230 Odense M, Denmark
E-mail: chlarsen@health.sdu.dk https://www.linkedin.com/in/carstenhvidlarsen/

Factors affecting the mental health of elite athletes
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Abstract 1
The purpose of this scoping review was to provide an overview of studies concerning the 2
mental health (MH) of elite athletes and to provide a methodological, conceptual, and 3
applied overview of factors affecting elite athletes’ MH. A total of nine reviews and 43 4
empirical studies were evaluated and are reported in three sections: sample characteristics, 5
research design, and factors affecting elite athletes’ MH. These factors were grouped into 6
risk and protective factors and divided into a personal and sport-environmental domain. 7
The studies used quantitative (84%), qualitative (11%), and mixed-method (5%) designs 8
and examined a wide range of sports in different countries. Our review shows that 9
researchers have predominantly examined the prevalence of athletes’ mental ill-health 10
(e.g., depression) and the related factors compromising MH. Potential protective factors 11
such as the feeling of autonomy, positive relations in sport and private life, and adequate 12
recovery were highlighted in the qualitative studies. The discussion appraises the findings 13
through a critical lens, focuses on the current state of the research area and the MH 14
definition, limitations, suggested practical implications (e.g., to provide MH literacy to 15
both athletes and coaches), and future research directions (e.g., examining environments 16
that foster elite athletes’ MH). 17
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Key words: elite athletes; mental health; scoping review; sport; well-being 19
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Factors affecting the mental health of elite athletes
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Introduction 1
Research on mental health (MH) in elite sport has grown rapidly in recent years (Rice et 2
al., 2016). Several studies have demonstrated significant levels of mental ill-health among athlete 3
populations and therefore present cause for concern (Foskett & Longstaff, 2018; Schaal et al., 4
2011). Competitive international sport has led to increased pressure on elite athletes and the high 5
demands and extensive training loads present potential threats to athletes’ MH (Rice et al., 6
2016). The prevalence of diagnosable psychiatric disorders in athletes ranges from 4% to 68% 7
(Elbe & Jensen, 2016) and the variation in reported prevalence is a subject of ongoing debate 8
(Gorczynski, Coyle, & Gibson, 2017). Furthermore, the peak competitive years for elite athletes 9
tend to overlap with the peak age for the risk of onset of mental disorders (Gulliver et al., 2012). 10
The elite sports context consists of a unique range of stressors (Arnold & Fletcher, 2012). 11
These include competitive (e.g., performance expectation), organizational (e.g., travel), and 12
personal (e.g., family issues) stressors, that potentially increase the risk of mental illness in 13
athletes (Rice et al., 2016). Just as physical training must be balanced with adequate recovery, so 14
too must psychological demands be balanced with strategies to support MH. Since MH is a core 15
component of any culture of excellence (Schinke, Stambulova, Si, & Moore, 2017), many elite 16
sports organizations have recently increased their focus on athletes’ MH and aim to provide 17
solutions through their sports environments (Henriksen et al., 2019). 18
In order to conduct an overview of the current state of knowledge on athletes’ MH, we 19
have identified nine review-type papers through a systematic search (see Figure 1) and appraised 20
them in terms of aims, type of review, number and characteristics of studies included, as well as 21
major contributions (see a brief summary in Table 1). Reviews can be characterized by their 22
methods used (i.e., search, appraisal, synthesis, and analysis) and can, according to Grant and 23

Factors affecting the mental health of elite athletes
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Booth (2014), be divided into 14 different types, each with its strengths and weaknesses. For 1
example, systematic reviews aim for exhaustive comprehensive searching, apply a well-defined 2
query, and are useful to answer clearly defined questions. In scoping reviews, similar searching 3
methods are used for answering much broader questions (e.g., “what is known about this 4
concept?”) but no formal quality assessment of the included studies is conducted. Meta-analyses 5
use statistical techniques to provide a more precise effect of results obtained in different studies. 6
Narrative reviews are aimed at identifying and summarizing what has been previously published 7
for the purpose of reinterpretation or discussion. In contrast to systematic/scoping reviews and 8
meta-analyses, the methods used in narrative reviews to select the articles may not be described 9
explicitly and thus, reproducing narrative reviews is hardly possible. 10
The first reviews concerning MH in athletes (Bär & Markser, 2013; Glick et al., 2012; 11
Reardon & Factor, 2010) originated from the field of sport psychiatry and discussed the 12
diagnosis and treatment of athletes suffering from mental disorders. Rice and colleagues (2016) 13
assessed the prevalence of mental illnesses in athletes and structured the studies according to 14
different topics (e.g., anxiety, eating disorder, substance abuse), whereas Gorczynski, Coyle, and 15
Gibson (2017) compared mild or more severe depression between high-performance and non-16
athletes in their meta-analysis. Breslin, Shannon, Haughey, Donnelly, and Leavey (2017) 17
explored the effectiveness of MH awareness programs, while Moesch et al. (2018) discussed the 18
accessibility and quality of services in related to athletes’ MH in different European countries. 19
The recent position stands on MH from the International Society of Sport Psychology (ISSP; 20
Schinke et al., 2017) and the European Federation of Sport Psychology (FEPSAC; Moesch et al., 21
2018) represent the increasing attention directed at MH in sport. 22

Factors affecting the mental health of elite athletes
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Looking at the nine review papers (Table 1) from a developmental perspective, it can be 1
observed that the discourse of MH in elite athletes originated from a rather negative 2
conceptualization of athletes’ MH, hence from a clinical and treatment perspective. The ISSP 3
and FEPSAC position stands represent a discourse stemming from positive psychology that 4
directs the attention towards a proactive MH approach. Although risk and protective factors were 5
mentioned in some of the above-mentioned reviews, to date, no review has systematically 6
focused on factors affecting the MH of elite athletes. Still, knowledge about protective and risk 7
factors concerning athletes’ MH can be crucial for researchers and practitioners, and this is 8
where the current scoping review will contribute to the existing knowledge. 9
- Please insert Table 1 around here - 10
Conceptual Framework 11
The World Health Organization (WHO, 2014) defines mental health as ‘a state of well-12
being in which every individual realizes his or her own potential, can cope with the normal 13
stresses of life, can work productively and fruitfully, and is able to make a contribution to her or 14
his community’ (p.2). Hence, current views on MH and well-being have shifted the focus from a 15
negative conceptualization of MH as the absence of mental illness to definitions that encompass 16
positive aspects and include the level of functioning of individuals (Tennant et al., 2007; Uphill, 17
Sly, & Swain, 2016). In this review, we refer to Keyes’ (2002; 2007) two-continuum model in 18
which MH is characterized as a complete state. Keyes (2002) suggested that mental illness and 19
mental health are two distinct but related dimensions existing on two separate continua. The first 20
continuum relates to the absence and presence of mental illness, whilst the second relates to the 21
absence or presence of mental health. Accordingly, athletes could simultaneously have both 22
positive mental health and experience mental illness (e.g., Michael Phelps, who, despite his 23

Factors affecting the mental health of elite athletes
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diagnosed depression experienced flourishing in certain periods of his long successful career). 1
Alternatively, athletes could be free from mental illness but may be languishing. Thus, MH 2
provides a conceptual space that encapsulates the broad spectrum of both distressing and 3
flourishing experiences but recognizes that the strategies designed to reduce distressing 4
symptoms may not necessarily be the same as those designed to enhance flourishing (Uphill et 5
al., 2016). 6
Looking beyond this conceptualization, MH is contextualized and refers to the alignment 7
between the individual and the context (Henriksen et al., 2019). An elite athlete may thrive in 8
one sports environment or area of life and struggle in another, pointing to the importance of the 9
person-environment fit (Larsen, Alfermann, Henriksen, & Christensen, 2013). Environments 10
shape the motives of athletes, which contributes to self-reflection and the intrapersonal lived 11
experience of MH (Henriksen et al., 2019). Therefore, an appropriate and cognizant sporting 12
environment may support athletes’ well-being and could as such be understood as a protective 13
factor underpinning MH (Lebrun, MacNamara, Rodgers, & Collins, 2018). On the other hand, a 14
dysfunctional environment could be a risk factor that leads to the athlete having mental issues. 15
However, understanding whether a factor works as a risk or a protective factor for athletes’ MH 16
is key for practitioners to shape environments that can help to nourish athletes’ well-being. 17
Keeping in mind our shared identity as sports psychology researchers with a strong applied 18
perspective in elite sport, the critical component of this review was mainly applied to 19
psychological research literature. We chose to apply the propositions of the ISSP MH consensus 20
statement (Henriksen et al., 2019) as a critical lens to spur on the further development of the MH 21
discourse in elite sport. The purpose of this scoping review was threefold: (a) to provide an 22
overview of research concerning the MH of elite athletes from 1998 to 2018, (b) to identify and 23

Factors affecting the mental health of elite athletes
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categorize factors affecting elite athletes’ MH, and (c) to critically appraise the papers and detect 1
research gaps and future challenges. 2
Methodology 3
Methodologically, this review is informed by the Preferred Reporting Items for Systematic 4
Review and Meta-Analyses (PRISMA-ScR; Tricco et al., 2018, see Appendix 1) and the 5
recommendations of Grant and Booth (2009) on how to present results of a scoping review in 6
tabular, graphical, and narrative forms. 7
Search Strategy and Identifying Relevant Papers 8
The search strategy was initiated in May 2018 and initially included the use of the five 9
electronic databases: SPORTDiscus, SCOPUS, PsychINFO, EMBASE, and PubMed. The 10
rationale for using these databases relates to their prominent usage in other systematic reviews 11
(Park, Lavallee, & Tod, 2013; Rice et al., 2016; Sheridan, Coffee, & Lavallee, 2014) using a 12
similar protocol. Database searches included the following search terms (e.g., for SCOPUS): 13
((TITLE-ABS-KEY (sport*)) AND (TITLE-ABS-KEY (athlete*))) AND ((TITLE-ABS-KEY 14
(mental AND illness OR mental AND disorder OR psych* AND problem OR depression OR 15
anxiety OR stress)) OR (TITLE-ABS-KEY (mental AND health OR mental AND well-being OR 16
mental AND wellbeing OR flourishing))). We decided to include the more overarching term 17
“well-being” instead of adding the 13 sub-concepts (e.g., life satisfaction, personal growth, and 18
social integration) of Keyes’ (2005) dimensions reflecting mental health and flourishing. Similar 19
search strategies have been applied in other reviews concerning athletes’ MH (e.g., Breslin et al., 20
2017; Rice et al., 2016). Additional citations were subsequently gathered through reading the 21
reference lists of the articles and reviews already obtained. 22

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Inclusion criteria for the current investigation were as follows. Studies had to: (a) be 1
related to athletes’ MH or mental illness; (b) have a population comprising high-performance, 2
elite, or professional athletes (based on the definition of Swann, Moran, & Piggott, 2015), (c) be 3
published between 1998 and 2018, and (d) be written in English. Exclusion criteria were (a) grey 4
literature was not included, (b) participants not described as elite athletes (college athletes were 5
excluded because their athletic level can vary from local to national level), (c) commentaries 6
about athletes’ MH, and (d) full-text not available. We excluded publications that focused 7
exclusively on burnout, general stress, or concussions/overtraining that were not specifically 8
related to athletes’ MH. Not including grey literature provides a possible bias, however, 9
unpublished studies rarely impact the results and conclusions of reviews (Hartling et al., 2017). 10
The authors met on several occasions between May and July 2018 to review the initial 11
records identified (n = 3155). After excluding articles based on titles that were not relevant, 12
potential articles were taken to the abstract screening (n = 186) for further examination in 13
accordance with the above-described inclusion and exclusion criteria. The same procedure was 14
applied in the step from abstract to full-text screening (n = 64). Figure 1 provides an overview of 15
the study selection process. The selection and analysis of the studies were monitored throughout 16
the review process. Protocols for inclusion/exclusion and details about the search of the 17
databases can be obtained from the first author upon request. 18
- Please insert Figure 1 around here - 19
Bibliographic Coding and Appraisal of the Included Papers 20
As part of the analysis process, each of the 43 empirical studies was assigned a 21
bibliography number (in alphabetical order, see Table 2) and a similar systematic review 22
protocol as used by Park et al. (2012) and Stambulova and Wylleman (2018) was applied to this 23

Factors affecting the mental health of elite athletes
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analysis. After coding, the articles were re-read and appraised for (a) major foci of the study; (b) 1
participants (i.e., number, mean age or age range, gender ratio) and contexts (i.e., sports, level, 2
country); (c) methodology (i.e., type of study, design, methods/instruments used); (d) correlates 3
and associations related to elite athletes’ MH; and (e) main findings and major contribution to 4
the literature. A formal paper-by-paper critical quality assessment was not undertaken. Such 5
appraisals are not a necessary feature of scoping reviews (Grant & Booth, 2009; Tricco et al., 6
2018), and such practices can distract readers from the purpose of a scoping review. We did, 7
however, analyze the papers in a way that allowed us to make critical observations about the 8
research as a body of work, in keeping with the goals of a scoping review. The result of this 9
work is presented in Table 2, which represents an analysis of the included articles and which was 10
also used to summarize the protective and risk factors. 11
- Please insert Table 2 around here - 12
Data Analysis to Identify Protective and Risk Factors for Athletes’ Mental Health 13
Several steps were taken to identify and categorize the factors related to athletes’ MH. 14
Firstly, we selected and classified the correlates (quantitative studies) and associations 15
(qualitative studies) related to mental health (e.g., mental well-being, flourishing) and mental ill-16
health (e.g., depression, anxiety, eating disorder) from each of the studies. Many studies 17
examined multiple correlates, and in these cases, the correlates were listed separately. Secondly, 18
we examined the direction of association of the correlates and associations and classified them as 19
either protective or risk factors for athletes’ MH. Thirdly, based on the previous literature (e.g., 20
Rice et al., 2016; Sarkar & Fletcher, 2014) and our own understanding of MH factors, we 21
grouped the correlates/associations into overarching factor themes. For example, poor 22
performance, deselection, pressure from sponsors, and weight control were grouped into sport-23

Factors affecting the mental health of elite athletes
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specific stressors, whereas self-discipline, mature defense mechanism, self-encouragement, self-1
reflection, and setting meaningful goals were grouped into protective behavior. Fourthly, we 2
divided the overarching protective and risk factor themes into a personal domain and a sport-3
environmental domain and situated the overarching factors accordingly (Table 4). Throughout 4
the analysis process, the authors had regular meetings to discuss the process of identifying 5
correlates and categorizing the protective and risk factors. Furthermore, the participants of the 6
ISSP Think Tank on MH in the fall of 2018 (Henriksen et al., 2019) provided an expert 7
evaluation of the factor themes. Finally, in order to provide a visual overview and to facilitate the 8
understanding of the factors related to elite athletes’ MH, we created a conceptual map (Figure 2) 9
displaying the protective and risk factor themes and the number of correlates/associations within 10
the personal domain and the sport-environmental domain, respectively. 11
Results 12
Quantitative Mapping 13
Research designs and instruments. Table 3 represents the study design and sample 14
characteristics across the 43 empirical studies. Researchers have used quantitative characteristics 15
(36), qualitative characteristics (5) or a combination of both (2) to examine the MH of elite 16
athletes. Four studies employed a longitudinal approach while cross-sectional methods were 17
employed in the remaining 39 studies. Well over four-fifths of the studies (36) collected data via 18
questionnaires and the rest (7) collected data via interviews. The questionnaires can be divided 19
into 11 categories: (a) stress and recovery (30); (b) general health and mood state (25); (c) sleep, 20
eating, alcohol and smoking behavior (23); (d) depressive symptoms (21); (e) help-seeking 21
attitude and behavior (13); (f) career and need satisfaction (9); (g) anxiety (8); (h) well-being (6); 22
(i) resilience and coping (6); (j) social relationships and environment (5); and (k) perfectionism 23

Factors affecting the mental health of elite athletes
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(5). The most frequently applied instrument was the General Health Questionnaire (GHQ-12; 1
Goldberg & Williams, 1988) used in 14 studies, the Center for Epidemiologic Studies 2
Depression Scale (CES-D; Radloff, 1977) used in ten studies, the Distress Screener (4DSQ; 3
Braam et al., 2009) used in nine studies, and the Recovery-Stress Questionnaire (RESTQ; 4
Kellmann & Kallus, 2001) used in six studies. 5
- Please insert Table 3 around here - 6
Sample characteristics. The total number of participants was 11,475 of which 2,857 7
were female athletes and 7,539 were male athletes. Four studies (in total 1,079 athletes) did not 8
specify gender. More than half of the studies (24) had mixed-gender samples, 12 studies had 9
purely male samples, and three studies were conducted with female athletes only. Sample sizes 10
ranged between eight and 2,067. Eight studies had fewer than 50 participants and six studies 11
were conducted with a sample size of between 51 and 100. Eighteen studies had between 101 12
and 300 participants, and 11 studies examined population sizes greater than 300. According to 13
our inclusion criteria, the studies contained in this review reflected elite-level athletes (23) and 14
professional athletes (15), or a combination of both (5). Their athletic status was either active 15
(34), retired (2), or a combination of active and retired athletes (7). The samples contained team-16
sport athletes (16), athletes participating in individual sports (5), or a combination of both (22). 17
In three studies, the athletes were aged under 16, in 28 studies, the athletes were aged between 16 18
and 26 and in six studies, the athletes were 27 or older. Four studies included athletes with a 19
wide age range, while two studies did not report the age of the participants. As outlined in Table 20
3, the majority of studies were conducted in Western countries (26 in Europe, seven in 21
Australia/Oceania, two in North America, two in Asia, and seven studies had samples from 22

Factors affecting the mental health of elite athletes
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different continents/contexts). The fact that 81% of the included studies have been published in 1
the last five years (2013-2018) emphasizes the increased interest in the issue of athletes’ MH. 2
Qualitative Mapping 3
Factors affecting elite athletes’ mental health. Across the studies, we identified 82 4
correlates related to elite athletes’ MH. These variables were grouped into 25 overarching themes 5
during the analysis (ten protective factor themes and 15 risk factor themes; see Table 4 and 6
Figure 2). Eighty-one percent of the studies (35) examined personal risk factors (e.g., injury, 7
ineffective coping, adverse life events), while 37% of the studies examined sport-environmental 8
risk factors (e.g., deselection, stigma around help-seeking). Thirteen studies examined personal 9
protective factors (e.g., acceptance, positive social relationships), while 25% of the studies 10
investigated sport-environmental protective factors (e.g., climate of trust, access to specific 11
support). Injury and overtraining concerning athletes’ MH were the most researched themes with 12
37% of the studies examining these issues in relation to depressive symptoms and mental ill-13
being. The majority of studies (27) examined exclusively risk factors, four studies explored 14
protective factors only, while the remaining 12 studies investigated MH risk and protective 15
factors together. 16
Personal protective factors. Experiencing acceptance and expressing one’s real self in 17
therapeutic relationships (Doherty, Hannigan, & Campbell, 2016); appropriate behavior such as 18
self-discipline, mature defense mechanism, and self-encouragement (Gulliver, Griffiths, & 19
Christensen, 2012; Mousavi, Mousavi, & Yaghubi, 2017; Nixdorf, Frank, Hautzinger, & 20
Beckmann, 2013); self-reflection and setting meaningful and personally significant goals 21
(Mousavi et al., 2017); the use of available resources, and acceptance of multiple roles 22
(Lundqvist & Sandin, 2014) were the factors related to protective behavior investigated in five 23

Factors affecting the mental health of elite athletes
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studies. Sports confidence and sports success (Lundqvist & Sandin, 2014; Newman, Howells, & 1
Fletcher, 2016), self-esteem (Lundqvist & Raglin, 2015), situational control (Nixdorf et al., 2
2013), and employment status after retirement (Gouttebarge, Aoki, Verhagen, & Kerkhoffs, 3
2016) were found to be protective factors related to the feeling of competence. Positive social 4
relationships and support were investigated as general social support (Doherty et al, 2016; 5
Gulliver, Griffiths, & Christensen, 2012) and access to support (Coyle, Gorczynski, & Gibson, 6
2017) along with being married (Jones, 2016). General recovery (Frank, Nixdorf, & Beckmann, 7
2017; Nixdorf et al., 2013), sport as a way to de-stress (Newman et al., 2016), and willpower in 8
recovery (Doherty et al., 2016) were recovery factors promoting MH. Two studies (Lundqvist & 9
Sandin, 2014; Prinz, Dvorak, & Junge, 2016) highlighted athletes’ feeling of autonomy (e.g., 10
making own plans for the future) as a protective MH factor. Finally, two studies emphasized 11
basic needs satisfaction (Lundqvist & Raglin, 2015) and sports career satisfaction (Foskett & 12
Longstaff, 2017) as factors fostering elite athletes’ MH. 13
Sport-environmental protective factors. Six studies highlighted the positive effect on the 14
athletes’ support staff of mental health literacy, including awareness (Breslin, Shannon, 15
Haughey, Donnelly, & Leavey; 2017; Gulliver, Griffiths, & Christensen, 2012; Gulliver 16
Griffiths, Christensen, Mackinnon, et al., 2012; Newman et al., 2016), access to specialist 17
support (Coyle et al., 2017), and a positive therapeutic relationship Doherty et al., (2016) on 18
athletes’ MH well-being. Furthermore, a trusting and mastery-orientated climate (Lundqvist & 19
Raglin, 2015) such as confidentiality and trust in coach (Gulliver, Griffiths, & Christensen, 2012; 20
Lundqvist & Sandin, 2014), and encouragement of others towards help-seeking (Gulliver, 21
Griffiths, Christensen, Mackinnon et al., 2012) were found to be beneficial factors. Successful 22
retirement adjustment (i.e., retirement status compared to active elite athletes (Jones, 2016; Prinz 23

Factors affecting the mental health of elite athletes
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et al., 2016), or amount of working hours/employment status were related to a better MH status 1
in athletes. Two studies investigated sports friendships (Lundqvist & Sandin, 2014), and social 2
support from the sports context (Gulliver, Griffiths, & Christensen, 2012) and found these factors 3
to be positively related to athletes’ MH. 4
- Please insert Table 4 around here - 5
Personal risk factors. Factors related to injury and overtraining in relation to MH were 6
investigated as (severe or multiple) injuries (Appaneal, Levine, Perna, & Roh, 2009; Belz, 7
Heidari, Levenig, Hasenbring, Kellmann, & Kleinert, 2018; Biggin, Burns, & Uphill, 2017; 8
Celebi et al. 2015; Gouttebarge, Jonkers, et al., 2017a, 2017b; Gouttebarge, Tol, & Kerkhoffs, 9
2016; Gulliver, Griffiths, & Christensen, 2012; Gulliver, Griffiths, Mackinnon, Batterham, & 10
Stanimirovic, 2015; Jones, 2016; Junge & Feddermann-Demont, 2016; Kilic et al., 2017; 11
Newman et al., 2016; Prinz et al., 2016), concussion (Du Preez et al., 2017), or surgery 12
(Gouttebarge, Jonkers et al., 2017b; Kilic et al., 2017) and reflected the largest theme in terms of 13
the number of studies included in this review. Risk behavior and ineffective coping were 14
investigated in ten studies including perfectionist concerns (Jensen, Ivarsson, Fallby, Dankers, & 15
Elbe, 2018; Lundqvist & Raglin, 2015; Newman et al., 2016), negative coping strategies 16
(Mousavi et al., 2017; Nixdorf et al., 2013; Nixdorf et al., 2016), anxiety (Coyle et al., 2017; 17
Newman et al., 2016), fear of failure (Biggin et al., 2017), difficulties in expressing emotions 18
(Gulliver, Griffiths, & Christensen, 2012), and inaccurate self-understanding (Doherty et al., 19
2016). Adverse life events (e.g., death of a family member, separation) in the past six months 20
were investigated in nine studies (Gouttebarge, Aoki, et al., 2017; Gouttebarge, Frings-Dresen, & 21
Sluiter; 2015; Gouttebarge, Tol, & Kerkhoffs, 2016; Gouttebarge, Jonkers, et al., 2017a, 2017b; 22
Kilic et al., 2017; Newman et al., 2016; Prinz et al., 2016; Schuring, Kerkhoffs, Gray, & 23

Factors affecting the mental health of elite athletes
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Gouttebarge; 2017) and were found to have a strong negative impact on athletes’ MH status. 1
Several studies (Appaneal, et al., 2009; Beable, Fulcher, Lee, & Hamilton, 2017; Foskett & 2
Longstaff, 2017; Gerber, Holsboer-Trachsler, Puhse, & Brand, 2011; Hammond, Gialloreto, 3
Kubas, & Davis, 2013; Junge & Feddermann-Demont, 2016; Schaal et al., 2011) found female 4
athletes to be at a higher risk of depressive symptoms, most often in aesthetic or leanness sports 5
and frequently in combination with the female athlete triad syndrome. Poor general health 6
(Junge & Prinz, 2018), such as chronical back pain (Belz et al., 2018), permanent disability 7
(Jones, 2016; Newman et al., 2016), or osteoarthritis (Schuring, Aoki, et al., 2017) was found to 8
be related to athletes’ mental ill-health. Maladaptive personality traits and identity such as 9
extreme athletic identity or hypermasculinity (Doherty et al., 2016; Jones, 2016), perfectionism 10
(Jensen et al., 2018; Lundqvist & Raglin, 2015; Newman et al., 2016), and social phobia (Jensen 11
et al., 2018) were the factors found in five studies. Four studies (Gouttebarge, Tol, et al., 2016; 12
Gouttebarge, Jonkers, et al., 2017a; Gouttebarge, Hopley, et al., 2017; Schuring, Kerkhoffs, et 13
al., 2017) found positive relationships between sports career dissatisfaction and depressive 14
symptoms and need dissatisfaction (Lundqvist & Raglin, 2015) was found to be among the 15
strongest threats to athletes’ mental well-being. Chronic life stress was shown to be an influential 16
factor for burnout and/or depressive symptoms (Beable et al., 2017; Frank et al., 2017; Gerber et 17
al., 2011; Nixdorf et al., 2013). Negative relationship spillover (Jowett & Cramer, 2009) and lack 18
of social support (Gouttebarge, Jonkers, et al., 2017b; Prinz et al., 2016) were potential risk 19
indicators for languishing. Low quality of sleep (Biggins, Cahalan, Comyns, Purtill, & 20
O’Sullivan, 2018; Gerber et al., 2011) and poor eating habits (Coyle et al., 2017) were further 21
risk factors compromising athletes’ MH. 22

Factors affecting the mental health of elite athletes
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Sport-environmental risk factors. Many different sport-specific stressors were found to 1
have a negative effect on athletes’ MH, such as sporting pressure or competitive anxiety (Biggin 2
et al., 2017; Coyle et al., 2017; Doherty et al., 2016; Jensen et al., 2018), fear of failure or injury 3
(Biggin et al., 2017; Coyle et al., 2017; Hammond et al., 2013; Newman et al., 2016) , player’s 4
position (e.g., forward) (Junge & Feddermann-Demont, 2016; Prinz et al., 2016), deselection 5
Blakelock, Chen, & Prescott (2016), weight control (Gulliver, Griffiths, & Christensen 2012), 6
low match experience (Junge & Prinz, 2018), or upcoming major sports event (Drew et al., 7
2018). Athletes participating in individual (Beable et al., 2017; Nixdorf et al., 2016), and 8
aesthetic sports (Schaal et al., 2011) where a certain (lean) body image (Coyle et al., 2017) is 9
necessary were shown to have a higher prevalence of depressive symptoms. Uncertainty about 10
retirement (Beable et al., 2017) and retired status compared to active status (Gouttebarge et al., 11
2015) were risk factors for athletes’ MH. Low support from teammates (Gouttebarge et al., 2015) 12
and a general lack of support from the sporting environment (Prinz et al., 2016) were further 13
sport-environmental risk factors. Finally, stigma towards help-seeking was investigated in two 14
studies (Biggin et al., 2017; Gulliver, Griffiths, & Christensen, 2012) and it was found that 15
stigma and other dynamics collude to create barriers to accessing support, which can have a 16
potential negative impact on athletes’ MH. 17
Discussion 18
The current scoping review aimed to provide a systematic overview of factors affecting 19
elite athletes’ MH. Specifically, the review gives a summary of sample characteristics, research 20
designs employed, and risk and protective factors related to athletes’ MH of studies conducted in 21
the last 20 year up to May 2018. In total, 82 correlates/associations related to the athletes’ MH 22
were identified, grouped, and categorized into personal protective factors (e.g., protective 23

Factors affecting the mental health of elite athletes
16
behavior, social support), personal risk factors (e.g., injury, adverse life events), sport-1
environmental protective factors (e.g., climate of trust, MH literacy), and sport-environmental 2
risk factors (e.g., pressure, deselection). In general, the focus was predominantly on risk factors 3
for mental ill-health, while protective factors fostering athletes’ MH were assessed in a minority 4
of the studies. Researchers have used mostly quantitative and, to a lesser extent, qualitative 5
methods across both genders and various types of sports. The results demonstrate that the study 6
area has grown rapidly over the past five years. In the following sections, some of the 7
propositions of the MH consensus statement (Henriksen et al., 2019) will be used as a critical 8
lens to discuss and appraise the findings of the current review. 9
- Please insert Figure 2 around here - 10
Mental Health in the Elite Sport Context Should be Better Defined 11
The current review showed that athletes’ MH has until now mainly been investigated 12
through symptoms of general psychological distress or the prevalence of symptoms of a common 13
mental disorder (CMD; e.g., depression, anxiety, and eating disorder). As such, MH was 14
indirectly conceptualized as the absence of such symptoms. Assuming that all athletes who do 15
not show or report clinical disorders are mentally healthy is too simplistic (Henriksen et al., 16
2019, Schinke et al., 2017) and does not align with the MH conceptualization of Keyes (2002) in 17
which MH is understood as a dynamic and complete state where athletes may experience low 18
levels of mental well-being at one end of the continuum and mental wealth, or flourishing, at the 19
other end. The WHO (2014) definition of mental health adopts a positive psychology perspective 20
and represents a more holistic view of MH, emphasizing well-being as a core construct. Only a 21
few studies (e.g., Jowett & Cramer, 2009; Lundqvist & Raglin, 2015; Lundqvist & Sandin, 2014) 22
applied a sound theoretical model and/or framework to investigate athletes’ MH. Studies that 23

Factors affecting the mental health of elite athletes
17
investigated both sides of the MH continuum (i.e., languishing and flourishing; Keyes, 2002) 1
were rare and mostly qualitative (e.g., Coyle et al., 2017; Doherty et al., 2016; Gulliver et al., 2
2012; Jones, 2016; Lundqvist & Raglin, 2015; Nixdorf et al., 2013). However, as Lundqvist 3
(2011) argued, sport psychology studies have either failed to define well-being or used a variety 4
of labels to describe the construct. Therefore, Lundqvist proposed a more integrated model of 5
well-being that includes all three underlying dimensions (i.e., subjective, psychological, and 6
mental) of well-being. Henriksen et al. (2019) advised researchers to develop a clear definition of 7
mental health in sport. Consequently, based on the MH definitions of Keyes (2002, 2007) and the 8
WHO (2014), the outcomes of the current review, and our experience working with athletes in 9
various high-performance contexts, we propose the following definition of MH in relation to 10
elite sport as: 11
Mental health is a dynamic state of well-being in which athletes can realize their 12
potential, see a purpose and meaning in sport and life, experience trusting personal 13
relationships, cope with common life stressors and the specific stressors in elite 14
sport, and are able to act autonomously according to their values. 15
Research on Mental Health in Sport Should Broaden the Scope of Assessment 16
In terms of research design, our review findings indicate that the majority of the empirical 17
studies were of a quantitative nature and applied a cross-sectional epidemiological study design 18
(e.g., Beable et al., 2017; Gouttebarge et al., 2015, 2018; Killic et al., 2017) using self-reported 19
screening instruments such as the GHQ-12 and the CES-D. These instruments are borrowed from 20
clinical research and are not adapted to the elite sports context and the athletic population. Only 21
two studies (Gerber et al., 2011; Klinkowski et al., 2008) used a control group to compare 22
athletes to a comparable peer group in terms of their MH status. Although only 11% of the 43 23

Factors affecting the mental health of elite athletes
18
empirical studies included in this review applied a qualitative research design, these studies 1
added more than half of the correlates making up the personal and sport-environmental 2
protective factors. The qualitative studies emphasize the real and often idiosyncratic experiences 3
that athletes go through during their careers that might influence their well-being in the long run. 4
Similarly, a recent qualitative study by Lebrun and colleagues (2018) demonstrated that the 5
subjective experience of clinical depression can be triggered by sporting and non-sporting 6
reasons, and that the athletes’ sporting performance does not necessarily need to be directly 7
related to their subjective MH state. 8
The review showed that 37% of the included studies investigated the relationship between 9
injury or overtraining and athletes’ MH. Overtraining and burnout can be difficult to distinguish 10
from depression, as some of the symptoms such as fatigue, insomnia, appetite change, weight 11
loss, lack of motivation, and concentration difficulties overlap with each other (Gustafsson, 12
DeFreese, & Madigan, 2017; Reardon & Factor, 2010). However, burnout comes along with “a 13
withdrawal from sport noted by a reduced sense of accomplishment, devaluation/resentment of 14
sport, and physical/psychological exhaustion” (Raedeke, Lunney, & Venables, 2002, p. 181), 15
while athletes with depressive symptoms are still able to perform and can maintain active 16
competitors over a long period (e.g., Michael Phelps). 17
Gouttebarge and colleagues applied a prospective cohort study design in several of their 18
studies (e.g., Gouttebarge et al., 2015, 2018) in order to determine the prevalence of CMD 19
among rugby, cricket, and soccer players and discovered that it is quite common for athletes to 20
suffer from depression, anxiety, sleep disturbance, or adverse alcohol use simultaneously. Many 21
quantitative studies investigated athletes’ MH in commercial professional sports such as rugby 22
and football, where the risk of (multiple) injuries and concussion is relatively high (Gouttebarge 23

Factors affecting the mental health of elite athletes
19
et al., 2015, 2016). No qualitative inquiry has yet been made into professional team sports in 1
order to investigate not only risk factors such as adverse life events and injuries for CMD, but 2
also to explore factors fostering athletes’ MH. While recognizing the importance of monitoring 3
athletes’ MH before and after an important event such as the Olympics, only one study 4
(Hammond et al., 2013) examined athletes’ MH in relation to preparing for a major sports event 5
and found that after the competition, the number of athletes that met diagnostic criteria for 6
depression was halved compared to pre-competition. The study of Hammond et al. (2013) was 7
also the only one to use both semi-structured interviews based on the DSM-IV criteria (American 8
Psychiatric Association, 1994) and a quantitative scale in order to establish the presence of a 9
major depressive episode. 10
Sample characteristics. Although only studies that investigated the elite athlete 11
population in relation to MH were included, the samples reviewed in this study were rather 12
heterogeneous in terms of age, sporting level, type of sports, and level of professionalization, 13
which made it difficult to compare findings across studies. This is a common difficulty in sports 14
research because elite athletes can be further categorized into world-class elite, successful elite, 15
competitive elite, and sub-elite (Swann et al., 2015). Studies often fail to describe their samples 16
accordingly or athletes from different elite categories are included in the same sample (Belz et 17
al., 2013; Biggin et al., 2017; Kilic et al., 2017). In their review, Rice et al. (2016) further 18
criticized the quality of the empirical quantitative studies related to MH and elite athletes and 19
highlighted that participants’ self-selection may reduce the representativeness of the findings. In 20
terms of genders studied, less than one-third of the athletes included in this review were female 21
athletes. Some authors emphasize that professional male athletes are at an increased risk of 22
mental ill-health due to their attitude of being mentally tough and their inability to seek 23

Factors affecting the mental health of elite athletes
20
professional help (Souter et al., 2018). On the other hand, female athletes, especially in aesthetic 1
and endurance sports (e.g., Beable et al., 2017; Nixdorf et al., 2016; Schaal et al., 2011), are 2
exposed to specific stressors that promote the female athlete triad syndrome which potentially 3
leads to other CMDs (Reardon & Factor, 2010). 4
The results showed that there has been an increased research focus on athletes’ MH in 5
Europe and Australia/Oceania, while there has been less (Asia) or no (Africa, South America) 6
investigation of the MH of elite athletes published in the English language. As highlighted by 7
Park et al. (2012), investigating cultural diversity could help in testing the generality and validity 8
of existing knowledge and theories and have practical implications, such as providing suitable 9
and appropriate support in applied work. Since MH support programs and referral practices are 10
culturally infused and differ substantially even within high-income European countries (Moesch 11
et al., 2018), it can only be speculated how mental health symptoms/disorders of elite athletes are 12
managed in low- and middle-income countries, given the lack of research in these contexts. 13
Mental Health as a Major Resource for the Whole Athletic and Post-Athletic Career 14
Athletes with high levels of well-being have a better chance of performing well, 15
particularly over the long term (Henriksen et al., 2019). At the same time, sound mental health is 16
not a prerequisite for performance, since (some) athletes achieve world-class performances 17
despite mental health issues and clinically diagnosable disorders (e.g., Michael Phelps, Andre 18
Agassi, Lindsey Vonn). Within the holistic lifespan perspective, an athlete is conceptualized as a 19
whole person that develops on the athletic, psychological, psychosocial, academic-vocational, 20
and financial level throughout a life career (Wylleman, De Knop, & Reints, 2011). Career 21
transitions are critical phases in an athlete’s career (Stambulova et al., 2009) and especially the 22
junior to senior transition (e.g., Pummel & Lavallee, 2019, Wylleman, Rosier, & De Knop, 2015) 23

Factors affecting the mental health of elite athletes
21
and the transition out of elite sport (Kuettel, Boyle, & Schmid, 2017, Park et al., 2013) are 1
phases where athletes (often) perceive a misbalance between their resources and the transition 2
demands. Between 15-20% of athletes are expected to face serious adjustment difficulties 3
including a feeling of loss, identity crises, and distress following athletic retirement (Park et al., 4
2013). Gouttebarge and colleagues (2015, 2016) investigated MH among current and former 5
football players and showed that up to 39% of the retired athletes faced MH problems including 6
adverse nutritional and drinking behavior. Employment status and working hours were identified 7
as protective factors for retired footballers (Gouttebarge, Aoki et al., 2016) indicating – as 8
suggested in the WHO mental health definition – the importance that athletes can work 9
productively and fruitfully, contribute to their community, and find meaning also in life after 10
their elite sports career. 11
Dual career is experienced by athletes during the periods when they combine sport with 12
education or work (Stambulova & Wylleman, 2019). Even though the student-athletes in Gerber 13
et al. (2011) study reported less stress, better sleep, and fewer depressive and anxiety symptoms 14
than non-athletes, it can be assumed that student-athletes, despite experiencing a unique range of 15
stressors (e.g., managing pressure from different stakeholders, role strain) do not differ with 16
respect to reporting depressive symptoms (e.g., Gorczynski et al., 2017; Rice et al., 2016). 17
However, Stambulova and Wylleman (2019) highlighted that research on well-being concerning 18
dual career has just begun (e.g., Sorkkila, Ryba, Aunola, Selänne, & Salmela-Aro, 2017). Further 19
research is needed in order to understand how risk and protective factors affect athletes’ MH in 20
relation to their dual career pathways and challenges during within-career transitions. 21
Mental Health as a Core Component of a Culture of Excellence – The Influence of the 22
Environment on Athletes’ Mental Health 23

Factors affecting the mental health of elite athletes
22
Within the holistic ecological perspective (Henriksen, Stambulova, & Roessler, 2010), the 1
context of athletes’ career development is conceptualized as containing athletic and non-athletic 2
domains within the micro-, meso-, and macro-levels, including athletes’ close environments and 3
factors related to the national culture and sport system. Only three studies (Gulliver et al., 2012; 4
Lundqvist & Raglin, 2015; Lundqvist & Sandin, 2014) investigated the potential influence of the 5
sporting environment on athletes’ MH. However, several studies (e.g., Coyle et al., 2017; Schaal 6
et al., 2011) have shown that athletes from certain sports (i.e., weight-sensitive or aesthetic 7
sports) are more prone to develop MH issues, including anxiety and depressive symptoms. These 8
findings are, however, of descriptive nature and tell us little about the influence of the 9
contextualized cultural characteristics (e.g., coaching behavior and style, embedded values) 10
within the respective elite sports environments. Therefore, an increased focus should be placed 11
on the environment in which the athlete develops, since a trusting and mastery-orientated climate 12
̶ as opposed to ‘winning at all costs’ ̶ has been shown to be beneficial for the MH of young 13
elite athletes (e.g., Ivarsson et al., 2015; Verner-Filion & Vallerand, 2018). Figure 2 shows that 14
competence, autonomy, and relatedness as the three basic human needs (Deci & Ryan, 2000) 15
appear to be essential for facilitating optimal functioning, growth and integration, as well as for 16
the constructive social development and personal well-being of athletes (e.g., Coyle et al., 2017; 17
Doherty et al., 2016; Gulliver et al., 2012; Lundqvist & Sandin, 2014). Case studies conducted in 18
Scandinavian cultural contexts have identified some shared features that facilitate athletes’ 19
development and transitions (e.g., Henriksen et al., 2010). For example, development of 20
psychosocial skills, supportive relationships, proximal role models, integrated efforts of the 21
athletes’ close and wider environment, and focus on long-term development are characteristics 22
that are especially relevant for young athletes to make successful transitions while maintaining 23

Factors affecting the mental health of elite athletes
23
high levels of MH and well-being. Thus, coaches and support staff need to make sure that they 1
provide an environment where these features are integrated. Further research is needed to 2
provide a better link between sport-environmental factors and athletes’ MH in different elite 3
sports sub-cultures all over the world. 4
There is a general view that athletes who do seek help for psychological problems may be 5
seen as weak by other athletes and coaches (Souter, Lewis, & Serrant, 2018), and stigma has 6
been highlighted as a barrier to help-seeking in athletes (Biggin et al., 2017; Gulliver et al., 7
2012). However, as Moreland, Coxe, and Yang (2017) emphasized, many stakeholders (i.e., 8
athlete, coach, teammate, administrators) and their attributes (i.e., attitudes, opinion, behavior) 9
can either work as facilitators of and/or barriers to using MH services. The busy athletic 10
schedule can be another barrier why athletes are reluctant to use mental health services (Gulliver 11
et al., 2012). Help-seeking attitudes within the elite sport are starting to shift (Souter et al., 2018) 12
and some National Sport Governing Bodies have recently established a new Mental Health 13
Strategy (e.g., UK Sport) consisting of four key pillars, namely education, provision, 14
communication, and assurance. Although there is evidence of some support for the effectiveness 15
of MH awareness programs (Breslin et al., 2017; Sebbens, Hassmén, Crisp, & Wensley, 2016), 16
further studies are needed in order to review the quality and effectiveness of such programs and 17
initiatives. 18
Limitations 19
This review has several limitations. First, it includes only English language, peer-reviewed 20
studies. The exclusion of non-English studies might influence sample characteristics (e.g., 21
location of study) and lead to the omission of potential correlates that may be culturally 22
significant. Nevertheless, some evidence suggests that restricting the search to studies published 23

Factors affecting the mental health of elite athletes
24
in English language may not always be enough to influence systematic review findings 1
(Morisson et al., 2012). As previously described, the inclusion criteria ‘elite athlete’ is not 2
without problems. By excluding studies that investigated collegiate or student-athletes, we risked 3
omitting a significant body of research on athletes’ MH conducted in Northern America. Second, 4
our attempt to structure the MH factors into a personal and sport-environmental domain was not 5
without challenges as many factors are interrelated, therefore, the border between the two 6
domains might be more fluid than displayed in the conceptual map in Figure 2. Third, the 7
division of factors was influenced by our subjective understanding of risk and protective factors 8
based on our common background as sport psychology practitioners working with athletes in 9
different sports and at different career stages. Fourth, we extracted the correlates/associations 10
from the included studies without evaluating them in terms of their impact on mental health or 11
mental ill-health. Hence, factors placed further away from the center in Figure 2 do not 12
necessarily have stronger effects on athletes’ MH. Fifth, by not including the vast literature 13
concerning athletic burnout, we might have missed some important specific factors related to 14
recovery and stress (-reduction) which possibly affect athletes’ mental health. Finally, a general 15
weakness of scoping reviews concerns the lack of quality assessment of the included studies. 16
Practical Implications for Support Providers 17
Based on the review findings, several practical implications for MH support providers, 18
sports psychologists, and researchers emerged from this review. Coaches, parents, and peers 19
have been shown to positively influence a range of factors impacting athletes’ development in 20
sport (Sheridan et al., 2014) and as such contribute to athletes’ MH (e.g., Coyle et al., 2017; 21
Doherty et al., 2016; Gulliver et al., 2012; Lundqvist & Sandin, 2014). Coaches can foster 22
positive psychosocial development and well-being by helping athletes to learn to respond to the 23

Factors affecting the mental health of elite athletes
25
variety of sport-specific stressors in a healthy way (Turnnidge & Côté, 2016). However, the 1
athletes’ entourage might not always provide the appropriate type of support (e.g., exertion of 2
extra pressure, lack of understanding) and this can have a potentially negative effect on athletes’ 3
MH (e.g., Gouttebarge et al., 2015; Prinz et al., 2016). Therefore, coaches and practitioners 4
should be empowered with relevant information so that they recognize the importance of 5
creating an environment that supports MH, helping athletes sustain mentally healthy in elite 6
sport. Accordingly, the conceptual map (Figure 2) could be used as an orientation tool for 7
practitioners in order to enhance athletes’ MH. 8
Future Directions 9
Several research directions can be identified with regard to research design. Most of the 10
quantitative studies included have used instruments that have not been modified for the athletic 11
population. In the future, instruments should be adapted to the sports context (e.g., Baron et al., 12
2013; Foster & Chow, 2019) and researchers investigating athletes’ MH should use similar 13
instruments so that it is possible to compare MH between different athletic populations. The 14
majority of researchers have used retrospective data collection methods, which may have 15
negatively affected the data recall process. Employing a prospective longitudinal design to study 16
how an athlete’s MH state evolves during their sports career would allow researchers to examine 17
dynamic interpersonal processes and changes over time, for example during critical career 18
transitions (Kuettel et al., 2017; Pummell & Lavallee, 2019). Therefore, more prospective 19
longitudinal studies, as well as qualitative studies, are required in the study area across cultures. 20
Given that a positive personality is an important part of resilience (Sarkar & Fletcher, 21
2014), further research needs to be conducted on how personality (cf. Coulter, Mallett, Singer, & 22
Gucciardi, 2016) influences athletes’ MH and well-being inside and outside the elite sport 23

Factors affecting the mental health of elite athletes
26
domain. Since athletes’ individual behavior can be either protective (Doherty, et al., 2016; 1
Gulliver et al., 2012; Lundqvist & Sandin, 2014) or maladaptive (Coyle et al., 2017; Jensen et 2
al., 2018; Lundqvist & Raglin, 2015; Nixdorf et al., 2013) for their MH, appropriate coping 3
strategies and developing resilience can help athletes to deal with stressors both in the personal 4
domain and the sport-environmental domain (Lebrun et al., 2018). As such, strategies that are 5
directed towards promoting mental health (rather than reducing the incidence of mental illness) 6
should be developed by the athletes and supported by their personal and sporting environment. 7
The conceptual map displayed in Figure 2 could in future studies be utilized as a conceptual 8
framework to investigate athletes’ MH. However, more research is needed in order to improve 9
the understanding how dispositional (e.g., genetics, family history), triggering (e.g., critical life 10
events), psychological (e.g., personality, intelligence), social (e.g., poverty, milieu), and 11
protective and risk factors interplay in relation to athletes’ well-being and mental illness. 12
Conclusions 13
Elite athletes are exposed to a unique range of risk factors (e.g., injury and overtraining, 14
constant pressures to perform) that may potentially increase their vulnerability to mental ill-15
health. On the other hand, both personal (e.g., acceptance of multiple roles, appropriate recovery) 16
and sport-environmental protective factors (e.g., reducing stigma, climate of trust) can contribute 17
to athletes’ MH and well-being. Unlike previous reviews, the present scoping review has focused 18
specifically on factors affecting athletes’ MH providing a visual overview of factors that 19
potentially foster or hamper elite athletes’ MH. The findings indicate that the majority of the 20
empirical studies are of quantitative nature and apply a cross-sectional epidemiological study 21
design using self-reported screening instruments. No qualitative inquiry has yet been made into 22
professional team sports in order to investigate risk factors such as adverse life events and 23

Factors affecting the mental health of elite athletes
27
injuries for CMD, but also to explore factors fostering athletes’ MH. There is also a lack of 1
studies that investigate the potential influence of the sporting environment on athletes’ MH. 2
Our scoping review provides original contributions to the body of research by providing an 3
overview and a structure of the factors related to athletes’ MH in a conceptual map. By 4
thematically synthesizing findings from quantitative and qualitative studies, our review advanced 5
knowledge by reducing the complexity of factors that potentially affect athletes’ MH. To have a 6
better understanding of risk and protective factors in relation to athletes’ MH is not only 7
important for practitioners working with athletes, but also for researchers that intend to 8
investigate athletes’ MH in future studies. Insofar, the current review has contributed to the 9
evolving discourse about MH in elite sport and intends to spur discussion on how we can better 10
understand, measure, and support athletes’ MH. 11
Acknowledgments 12
This review was partly supported by a grant from Team Denmark. We are grateful to Anne 13
Faber Hansen for her help with the literature search. We thank Professor Natalia 14
Stambulova and Anna Daudert for their constructive inputs during the process and their 15
feedback on earlier drafts of this manuscript. 16
17
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Factors affecting the mental health of elite athletes
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Note: Studies/reviews that are underlined were included in the present scoping review. 42

Records identified
via databases
(n = 3155)
SPORTDiscus (n = 619)
SCOPUS (n = 259)
PsycINFO (n = 713)
EMBASE (n = 1187)
PubMed (n = 377)
Article before year
1998 removed (n = 373)
Records identified
from 1998-May 2018
(n = 2782)
Records after
removing duplicates
(n = 2410)
Duplicates removed
(n = 372)
After irrelevant titles
removed (n = 2224)
Abstracts screened
(n = 186)
Identification
Screening
Eligibility
Inclusion
Full-text articles
screened for eligibility
(n = 64)
Removed after reading
abstract (n = 112)
Empirical articles included in
the scoping review
(n = 43)
Removed because did not meet
inclusion criteria (n =18):
• Editorial/commentary/
research note (n = 6)
• Elite sport population not
met (n = 8)
• Not specifically about
mental health (n = 4)
Articles added after reading
references of obtained articles
(n = 6)
Full-text not available or
not in English (n = 10)
Figure 1. Study selection flow diagram
Review-type papers
included (n = 9)

Table 1
A Brief Summary of The Identified Review-Type Papers on Athletes’ Mental Health (MH) (in chronological order)
Reference
(year)
Aims/focus of the review
Type of review/ Amount of the
included studies/ Databases
Contribution and main findings
Reardon &
Factor
(2010)
To discuss the current state of knowledge
of psychiatric diagnoses in athletes.
This systematic review included 103
papers through a MEDLINE search
with a focus on disorders and illness.
The article reviews the athletic population in terms of mood, anxiety, eating, attention-
deficit, addictive, and other disorders and provides clinical treatment methods for
athletes suffering from such disorders.
Glick et al.
(2012)
To provide professional and ethical
quandaries that arise in treating elite
athletes with psychiatric issues.
Type of review not specified.
MEDLINE search for articles
discussing psychiatric
diagnosis/treatment of athletes.
Active and retired athletes have psychiatric problems and disorders like non-athletes
and require diagnosis and treatment to function in their sport and in the rest of their
lives. Practical implications for psychiatrists working with elite athletes are provided.
Bär &
Markser
(2013)
To discuss the issue of the sports
specificity of selected mental diseases in
elite athletes.
Thematic narrative review of 57
articles on sport-specific mental
disorders (search not reported).
The prevalence of psychiatric conditions among elite athletes is still under debate.
Further research on psycho-social factors is needed to better understand the sports
specificity of the etiology of mental disorders in high-performance athletes.
Rice et al.
(2016)
To appraise MH and wellbeing of elite-
level athletes, including the incidence
and/or nature of mental ill-health and
substance use.
Narrative systematic review of 60
articles identified through systematic
search in PubMed, EMBASE,
SPORTDiscus, PsycINFO, Cochrane
and Google Scholar.
Elite athletes experience a unique range of stressors (injury, overtraining and burnout;
intense public and media scrutiny; managing ongoing competitive pressures to
perform) that may potentially increase their vulnerability to mental ill-health. More
high-quality epidemiological and intervention studies are needed.
Breslin et al.
(2017)
To determine the effect of sport-specific
MH awareness programs and to review
the study quality and effectiveness of
programs.
Systematic review of ten studies
related to mental health awareness
programs for athletes/coaches. Search
in PsycINFO, MEDLINE, Scopus,
Cochrane, CINAHL, SPORTDiscus.
Some support was found for the effectiveness of the MH programs available. However,
a cautionary approach must be taken when determining an effective program, as
studies demonstrated a high risk of bias and showed a limited validity in the outcome
measures of mental health knowledge and referral efficacy.
Gorczynski,
Coyle, &
Gibson
(2017)
To assess the prevalence of mild or more
severe depressive symptoms between
high-performance athletes and non-
athletes.
Comparative meta-analysis of 5
studies. Systematic search in PubMed
PsycINFO, MEDLINE, CINAHL,
SPORTDiscus, and Google Scholar.
High-performance athletes and non-athletes do not differ with respect to reporting mild
or more severe depressive symptoms. The rates of mild or more severe depressive
symptoms ranged from 3.7% to 26.7% for high-performance athlete males and from
9.8% to 36.5% for high-performance athlete females.
Gucciardi,
Hanton, &
Fleming
(2017)
To evaluate theory and evidence
regarding the thesis that MH and mental
toughness are contradictory concepts in
elite sport.
Narrative review and critical
evaluation of authors’ own published
articles and additionally 15 papers
related to the two concepts.
Mental toughness may represent a positive indicator of MH, or facilitate its attainment,
rather than be at odds with. However, there is no research that has directly tested this
thesis in elite athletes. The notion that mental health and mental toughness are
contradictory concepts in elite sport may be too simplistic.
Schinke,
Stambulova,
Si, & Moore
(2017)
To consider what is known about MH to
spur dialogue, contribution to research
and services that support aspiring
athletes’ MH.
Position stand, narrative-type review
of international literature on athletes’
MH (details of search or number of
articles not specified).
Mental health of athletes is reviewed under elite athlete trends, athletes’ performance,
overtraining and injury, career development, culture and identity, and health-related
interventions. Ten postulates are provided to spur further discussions on how to make
athletes healthier and, thus, more resourceful for (and through) sport.
Moesch et
al.
(2018)
To enhance awareness of the MH topic
and to critically discuss optimal service
provision for athletes.
Position statement. Type of review
not specified. Models of service
provision of six European countries
are presented, 5 studies are compared.
Competencies, certification issues and professional boundaries of the involved service
providers differ considerably between different European countries. The models of
service provision presented should stimulate reflections on optimal support in one's
own country.

Table 2
Overview of the 43 Empirical Articles Included in the Systematic Literature Review on Mental Health (MH) of Elite Athletes (alphabetically)
Author(s)
(year)
Aims and major focus of the
study
N (female:male)
Age of athletes
Sport(s)
Location/Context
Research
design
MH protective
correlates/associations
MH risk
correlates/associations
Main findings/contributions
Appaneal,
Levine, Perna,
& Roh (2009)
Examine student elite
athletes’ postinjury
depression symptoms
164 (56:108)
M = 19.7
SD = 2.0
Various sports
USA
Quantitative
Cross-sectional
- Injury
- Female
Both athlete- and clinician-rated depression
symptoms decreased over time. Women,
regardless of injury status, exhibited greater
depression symptom severity than men in
clinical interviews.
Beable,
Fulcher, Lee,
& Hamilton
(2017)
Estimate the prevalence of
symptoms of depression and
daily life hassles in elite
athletes
187 (113:74)
Wide age range
Various sports
New Zealand
Quantitative
Cross-sectional
- Individual sport
- Retirement uncertainty
- Perceived life stress
- Female
- Age below 25
- Centralization
21% of elite athletes met the criteria for
moderate symptoms of depression with 8.6%
meeting the suggested criteria for a major
depressive episode. Athletes under 25 were
more likely to identify depressive symptoms.
Belz et al.
(2018)
Investigate stress and
depression in competitive
athletes with back pain
154 (79:75)
M = 18.81
SD = 5.05
Various sports
Germany
Quantitative
Cross-sectional
- Age under 18
- Back pain
Depending on the instrument 7.1% and 9.1%
of athletes were screened positive for
depression. Athletes <18 were lower in socio-
emotional stress and higher in psychological
wellbeing. Performance stress and risk for
depression were predictors of back pain
intensity and disability.
Biggin, Burns,
& Uphill
(2017)
Explore the elite athletes’ and
coaches’ perceptions of the
prevalence and types of
mental ill-health
19 (10:9)
M = 31.8
Various sports
UK
Mixed-method
Three-round
Delfi method
design
Athletes:
- Own pressure
- Fair of failure
- Injury
There are differences between coaches and
athletes in terms of beliefs about the
prevalence of mental-health issues. A general
consensus that stigma and other dynamics
collude to create barriers to access support.
Biggins,
Cahalan,
Comyns,
Purtill, &
O’Sullivan
(2018)
Assess the sleep profiles of
elite Gaelic athletes and to
compare wellbeing in relation
to sleep profiles
69 (0:69)
M = 22.7
SD = 4.1
Gaelic football and
hurling
Ireland
Quantitative
prospective
observational
study
- Poor sleep
Poor sleepers had significantly increased
subjective health complaints, increased stress,
and increased confusion-bewilderment.
Blakelock,
Chen, &
Prescott
(2016)
Establish the prevalence of
clinical levels of
psychological distress in elite
adolescent male soccer
players following deselection
91 (0:91)
M = 16.31
SD = 1.10
Soccer
UK
Quantitative
Cross-sectional
- Deselection
Results of this study suggested that deselected
players experienced significantly higher
levels of distress than retained players at post-
selection time points and that deselection was
the antecedent of such distress.
Breslin,
Shannon,
Haughey,
Determine whether providing
an MH awareness program to
100 (41:59)
M = 20.78
SD = 2.91
Quantitative
Cross-sectional
- MH awareness
program
MH awareness program improved athletes’
knowledge of MH and their likeliness to
engage and offer support to someone with an

Donnelly, &
Leavey (2017)
athletes increased knowledge
of MH
Various sports
UK and Ireland
MH problem, but the program did not
improve their knowledge of depression and
schizophrenia.
Celebi et al.
(2015)
Evaluate early phase
depression and anxiety in the
professional athletes who
underwent anterior cruciate
ligament reconstruction
38 (3:35)
M = 26.84
SD = 8.03
Various sports
Turkey
Quantitative
Cross-sectional
- ACL injury had no
impact on depression and
anxiety in the recovery
process
Levels of depression and anxiety were below
the diagnostic cutoff values. This is a rather
unexpected finding since ACL injury is a very
serious injury that threatens professional
sports careers.
Coyle,
Gorczynski, &
Gibson (2017)
Explore what MH (behaviors)
mean for a group of young
elite athletes as conditioned
by their peculiar social
context as elite athletes
8 (6:2)
Range:
14y-24y
Diving
UK
Qualitative
Thematic
analysis
- Access to support
(family, peers, and
coaches)
- Access to specialist
support (psychologist)
- Body image and eating
habits
- Stress and anxiety, fear
of injury
- Pressure through
expectations and
sponsoring
Participants demonstrated relatively limited
knowledge of MH. However, participants
identified a range of risks to MH which are
broadly aligned with risk factors
acknowledged within existing literature
including.
Doherty,
Hannigan, &
Campbell
(2016)
Explore how current/former
elite male athletes experience
depression during their
sporting careers
8 (0:8)
M = 40.38
SD = 14.7
Various sports
Ireland
Qualitative
Skype and face-
to-face
interviews
- Channeling sporting
will toward recovery
- Experiencing
acceptance
- Positive therapeutic
relationship
- Support from
significant others
-Developing hope
- Extreme athletic identity
-Sporting pressure
- Extrinsic motivations
- External locus of
evaluation
- Perceived precipitating
factors for depression
Masculine values, commitment to excellence,
and high levels of athletic identity which were
embraced by the athletes and reinforced by
the elite sporting environment played a role in
the development and maintenance of their
depression.
Drew et al.
(2018)
Establish the prevalence of
illness symptoms, poor sleep
quality, poor MH symptoms,
and stress-recovery state in an
Olympic cohort
132 (85:47)
M = 25.05
SD = 4.0
Various sports
Australia
Quantitative
Cross-sectional
- Upcoming major sports
event (e.g., preparation
towards the Olympics)
The point prevalence of poor MH was 14%–
17% and poor sleep quality was 49%. High
point prevalence of athlete self-reported
illnesses in the preparations (3 months prior)
was found in this cohort.
Du Preez et al.
(2017)
Investigate the prevalence of
mental illness in elite rugby
athletes and exploring
potential risk factors to
mental illness
404 (0:404)
Age>18
Rugby
Australia
Quantitative
Cross-sectional
- Concussion
Prevalence of depression was 12.6% pre-
season and 10% in-season. Prevalence of
generalized anxiety disorder was 14.6% and
10% for these 2 periods. Overall, 68.6% of
players had a hazardous level of alcohol use
pre-season and 62.8% in-season.
Foskett &
Longstaff
(2017)
Investigate the prevalence of
signs of anxiety and
depression and distress among
UK elite
143 (61:81)
M = 24.0
SD = 8.6
Various sports
UK
Quantitative
Cross-sectional
- Career satisfaction
- Female for distress but
not on anxiety/depression
47.8% of the athletes showed signs of
anxiety/depression and 26.8% signs of
distress. 17.3% of male athletes and 39.3% of
female athletes reporting signs of distress.
Frank,
Nixdorf, &
Beckmann
(2017)
Explore how depression and
burnout are related in athletes
194 (?:?)
M =15.08
SD = 1.95
Various sports
Germany
Quantitative
Cross-sectional
- Recovery
- Chronic stress
Burnout and depression can cause each other
to some degree and no particular direction can
be specifically supported by the current study.

Gerber,
Holsboer-
Trachsler,
Puhse, &
Brand (2011)
Examine whether
participation in elite sport
interacts with stress in
decreasing or increasing
symptoms of depression
434 (278:156)
M = 17.2
SD = 1.4
Various sports
Switzerland
Quantitative
Cross-sectional
- Female
- Stress
- Low quality of sleep
A substantial association of perceived stress
with depressive and anxious symptoms was
found. Poor sleepers are at increased risk of
depression and anxiety. Elite athletes reported
less stress, better sleep and decreased
depressive and anxious symptoms than the
control group from a conventional class.
Gouttebarge,
Aoki,
Verhagen, &
Kerkhoffs
(2016)
Explore the relationship of
level of education,
employment status with
symptoms of CMD among
current and retired
professional footballers
607 (0:607)
M = 26.8
SD = 4.4
Soccer
International
Quantitative
Cross-sectional
- Employment status for
retired players
- Number of working
hours for retired players
Prevalence of CMD ranged from 4% for
smoking to 38% for anxiety/depression and
58% for adverse nutritional behavior among
current professional footballers. Among
current footballers, level of education was not
associated with symptoms of CMD.
Gouttebarge,
Frings-
Dresen, &
Sluiter (2015)
Determine the prevalence of
MH problems and
psychosocial difficulties in
current and former
professional footballers
253 (0:253)
M = 27
SD = 5
Soccer
International
Quantitative
Cross-sectional
- Adverse life events
- Low social support from
teammates
- Retirement
The prevalence of MH problems was up to
26% and 39% in current and former
professional football players, respectively;
anxiety/depression was the most commonly
reported condition.
Gouttebarge,
Hopley, et al.
(2017)
Determine the prevalence of
symptoms of CMD among
professional rugby players
across countries
333 (0:333)
M = 26
SD = 4
Rugby
International
Quantitative
Cross-sectional
Prevalence (4-week) of symptoms of CMD
ranged from 13 % for sleep disturbance to 30
% for anxiety/depression, while around 17 %
of the participants reported comorbidity of
two simultaneous symptoms of CMD.
Gouttebarge,
Hopley, et al.
(2018)
Determine the incidence of
symptoms of CMD among
male professional rugby
players and to explore their
association with potential
stressors
595 (0: 595)
M = 26
SD = 4
Rugby
International
Quantitative
Observational
prospective
cohort study
- Adverse life events
The incidence of symptoms of CMD among
professional rugby players ranged from 11%
for distress and eating disorders to 28% for
anxiety/depression, while around 13% of the
participants reported two simultaneous
symptoms of CMD over the follow-up period.
Gouttebarge,
Jonkers, et al.
(2017a)
Establish the 12-month
incidence of symptoms of
CMD among Dutch elite
athletes and to explore their
potential association with
stressors
203 (?:?)
M = 27
SD = 7
Various sports
Netherland
Quantitative
Observational
prospective
cohort study
- Career dissatisfaction
- Injury
- Critical life events
The 12-month incidence was 32% for
distress, 57% for anxiety/depression, 19% for
sleeping disturbance, 6% for adverse alcohol
use, and 17% for eating disorders. In follow-
up, 17% of the participants reported two
(17%) or three (19%) symptoms of CMD.
Gouttebarge,
Jonkers, et al.
(2017b)
Determine the prevalence of
CMD among current and
former Dutch elite athletes,
and to explore potential risk
indicators
485 (?:?)
M = 27.3
SD = 7.1
Various sports
Holland
Quantitative
Cross-sectional
- Severe injuries
- Surgery
- Adverse life events
- Career dissatisfaction
- Low social support
Prevalence (4-week) 6% for adverse alcohol
use to 45% for anxiety/depression among
current elite athletes, and from 18% for
distress to 29% for anxiety/depression among
former elite athletes.

Gouttebarge,
Tol, &
Kerkhoffs
(2016)
Determine the prevalence,
comorbidity and 6-month
incidence of symptoms of
CMD among elite Gaelic
athletes
204 (0:204)
M = 25
SD = 4
Hurling and football
Ireland
Quantitative
Observational
prospective
cohort study
- Severe injury
- Sports career
dissatisfaction
- critical life events
Prevalence of symptoms of CMD among elite
Gaelic athletes ranged from 23% for adverse
alcohol use to 48% for anxiety/depression;
comorbidity of two or more symptoms of
CMD reached 48% among the sample.
Gulliver,
Griffiths, &
Christensen
(2012)
Determine what young elite
athletes perceive as the
barriers and facilitators to
help-seeking for common MH
problems
15 (6:9)
M = 19.3
One individual and
one team sport
Australia
Qualitative
Focus group
interviews
Self-initiated
written exercise
- Athlete appropriate
behavior
- Social support
- MH literacy
- Service accessibility
- Encouragement of
others
- Confidentiality
- Poor performance
- Injuries
- Weight control
- Stigma towards help-
seeking
- Difficulty to express
emotions
Stigma was the most important barrier to
seeking help. Other barriers were a lack of
MH literacy and negative past experiences of
help-seeking. Facilitators to help-seeking
were encouragement from others, relationship
with a provider, and positive attitudes of
others.
Gulliver
Griffiths,
Christensen,
Mackinnon, et
al. (2012)
Test the feasibility and
efficacy of three Internet-
based interventions designed
to increase MH help-seeking
attitudes and behavior
59 (43:16)
M = 25.42
SD = 5.64
Various sports
Australia
Quantitative
Randomized
controlled trial
- MH literacy
None of the interventions yielded a significant
increase in help-seeking attitudes or behavior
relative to control. MH literacy intervention
increased athletes’ knowledge of CMD and
showed evidence of reducing depression and
anxiety stigma.
Gulliver,
Griffiths,
Mackinnon,
Batterham, &
Stanimirovic
(2015)
Investigate Australian elite
athletes’ symptoms of general
psychological distress and
CMD
224 (118:106)
M = 24.91
SD = 6.00
Various sports
Australia
Quantitative
Cross-sectional
- Injury
46.4% of athletes had at least one MH
problem: Depression (27.2%), eating disorder
(22.8%), general psychological distress
(16.5%), social anxiety (14.7%), generalized
anxiety disorder (7.1%), and panic disorder
(4.5%). Injured athletes had higher levels of
depression and generalized anxiety disorder.
Hammond,
Gialloreto,
Kubas, &
Davis (2013)
Assess the prevalence of
diagnosed failure-based
depression and self-reported
symptoms of depression
within a sample of elite
swimmers
50 (22:28)
M = 20.5
Swimming
Canada
Mixed-method
Semi structure
interview and
survey
- Female
- High-performance level
- Performance failure
Before the competition, 68% of athletes met
the criteria for a major depressive episode.
After the competition, 34% of athletes met
diagnostic criteria and 26% self-reported mild
to moderate symptoms of depression.
Depression prevalence doubled among the
elite top 25% of athletes.
Jensen,
Ivarsson,
Fallby,
Dankers, &
Elbe (2018)
[27]
Investigate the relation of
perfectionism and anxiety to
depressive symptoms in
Danish and Swedish male
elite football players
323 (0:323)
M = 22.08
SD = 5.15
Soccer
Denmark & Sweden
Quantitative
Cross-sectional
- Competitive anxiety
- Perfectionist concerns
- Social phobia
16.7% of players reported de3pressive
symptoms. A relationship between depression
and perfectionistic concerns, competitive
anxiety, and social phobia was found.
Jones (2016)
Investigate how personal
characteristics and individual
athletic-related characteristics
correlate with MH attitudes in
elite football players
112 (0:112)
Wide age range
NFL football
USA
Quantitative
Cross-sectional
- Being married
- Being retired
- Hypermasculinity
- Injury that leads to
permanent disability
Although athletes have high levels of
hypermasculinity, other factors, including
marital status and sports level lessen the
effects of hypermasculinity and facilitate
positive perceptions of mental illness and
receptivity to help.

Jowett &
Cramer
(2009)
Explore how athletes’
perceptions of their
relationship impinge upon
their satisfaction with sport
and emotional well-being
87 (48:39)
M = 26.71
SD = 7.29
Various sports
UK
Quantitative
Cross-sectional
- Negative relationship
spillover
Relationship spillover was negatively related
to sports satisfaction and positively to
depressive symptoms.
Junge &
Feddermann-
Demont
(2016)
Evaluate the prevalence of
depression and anxiety in top-
level football players in
comparison to the general
population
471 (182:289)
M = 22.3
SD = 4.47
Soccer
Switzerland
Quantitative
Cross-sectional
- Female
- Injury
- Players position
- Levels of play
Swiss football players have the same
prevalence of depression as the general
population, while male U-21 players have a
higher prevalence of depression.
Junge & Prinz
(2018)
Evaluate the prevalence and
risk factors of depression and
anxiety symptoms in high-
level female football players
290 (290:0)
M = 21.5
SD = 4.2
Soccer
Germany
Quantitative
Cross-sectional
- Age (younger)
- Low match experience
- Playing in second league
- Poor general health
16.6% of the players expressed moderate and
14.1% severe symptoms of depression.
Second league female football players had a
higher prevalence of depression symptoms.
Kilic et al.
(2017)
Determine the prevalence of
symptoms of CMD among
soccer and handball players
and to explore the influence
of psychosocial stressors
1155 (249:906)
M = 25.5
SD = 4.7
Handball and soccer
Denmark
Quantitative
Cross-sectional
- Severe injuries
- Surgeries
- Critical life events
19% of soccer players expressed
anxiety/depression symptoms. In professional
handball, the highest prevalence (4 weeks) of
symptoms of CMDs was 26% and 16% for
anxiety/depression among current and retired
players, respectively.
Klinkowski,
Korte,
Pfeiffer,
Lehmkuhl, &
Salbach-
Andrae (2008)
Investigate psychopathology
and psychological distress in
female elite rhythmic
gymnasts compared to two
control groups
51 (51:0)
M = 15.2
SD = 1.8
Gymnastics
Germany
Quantitative
Cross-sectional
No psychological distress comparable to that
of anorectic patients was found. Gymnasts
suffered less from psychological distress than
the patient group and showed even fewer
symptoms than the high school student group.
Lundqvist &
Raglin (2015)
Investigated whether need
(dis)satisfaction, motivational
climate, perfectionism, and
self-esteem relate to athletes’
well-being and stress
103 (54:49)
M = 22.3
SD = 4.4
Orienteering
Sweden
Quantitative
Cross-sectional
- Need satisfaction
- Self-esteem
-Mastery-orientated
climate
- Need dissatisfaction
- Perfectionistic concerns
Distinct patterns of well-being and stress
levels could be identified with reasonable
accuracy (88 %) by five variables: Need
dissatisfaction, self-esteem, need satisfaction,
perfectionism, and mastery-oriented climate.
Lundqvist &
Sandin (2014)
Examine subjective,
psychological and social well-
being at a global and sport
contextual level among elite
orienteers
10 (6:4)
Median age: 20.4
Orienteering
Sweden
Qualitative
Semi-structured
interviews
- Sport confidence
- Realistic performance
standards
- Acceptance of
multiple roles
- Trust in coach
- Sport friendships
- Self-reflection and
autonomy
- Use available
resources
Subjective and psychological well-being
interacted, and important psychological
functioning among athletes included the
abilities to adopt value-driven behaviors, be
part of functional relationships, and to self-
regulate one’s autonomy.

- Meaningful and
personally significant
goals
Mousavi,
Mousavi, &
Yaghubi
(2017)
Determine the predictability
of psychological health and
sports success by defense
mechanisms
385 (100:285)
M = 23.25
SD = 3.11
Various sports
Iran
Quantitative
Cross-sectional
- Mature defense
mechanism
- Neurotic defense
mechanism
A significant positive relationship between
mature defense mechanisms with psycho-
logical wellbeing was revealed. Neurotic
defense mechanism was significantly
associated with psychological distress.
Newman,
Howells, &
Fletcher
(2016)
Explore the depressive
experiences of top-level
athletes
12 (3:9)
Age not specified
Various sports
UK
Qualitative
Thematic
analysis of
autobiographies
- Sport as a way to
escape from distress
- Sports success
- MH literacy
- Critical life event
- Multiple injuries
- Social anxiety
- Sporting failure
- Extreme demands
- Perfectionism
The findings display a two-way interaction,
with depression having implications for
performance, and performance having
implications for depression.
Nixdorf,
Frank, &
Beckmann
(2016)
Examined if athletes in
individual sports are more
prone to depressive symptoms
than athletes in team sports
199 (?:?)
M = 14.96
SD = 1.56
Various sports
Germany
Quantitative
Cross-sectional
- Individual sport
- Negative attribution
after failure
Athletes in individual sports showed higher
scores in depressive symptoms than athletes
in team sports. Attribution plays important
role in explaining the different vulnerability
to depression in team and individual sports.
Nixdorf,
Frank,
Hautzinger, &
Beckmann
(2013)
Provide insight into the
prevalence of depressive
symptoms among German
elite athletes and possible
associated factors
162 (58:104)
M = 23.41
SD = 7.04
Various sports
Germany
Quantitative
Cross-sectional
- Situation control
- Addressing oneself in
encouraging tones
- Recovery
- Chronic stress
- Negative coping
strategies
Prevalence of depression was 19%. General
stress and sport-specific stress show positive
correlations with depressive symptoms,
whereas general recovery and sport-specific
recovery show significant negative
correlations with depressive symptoms.
Prinz, Dvorak,
& Junge
(2016)
Evaluate depression
symptoms during and after
the career in former female
football players of the
German First League.
157 (157:0)
M = 33.0
SD = 6.25
Soccer
Germany, Europe
Quantitative
Cross-sectional
- Athletic retirement
- Future plans
- Playing position
- Conflicts with coach or
management;
- Injury
- Low performance
- Lack of support
- Separation/divorce
-Critical life event
32.3% of players had symptoms of major
depression, and 25.2% of mild or moderate
depression at least once during their football
career. The average depression score differed
significantly between playing positions and
levels of play.
Schaal et al.
(2011)
Identify the principal
psychological problems
encountered within French
high-level athletes, and the
prevalence based on sex and
the sport practiced
2067 (728:1339)
M = 18.5
SD = 4.9
Various sports
France, Europe
Quantitative
Cross-sectional
- Female
- Younger for prevalence
- Older age for depression
- Aesthetic and fine motor
skills sports
Overall, 20.2% of women had at least one
psychopathology, against 15.1% in men. This
female predominance applied to anxiety and
eating disorders, depression, sleep problems
and self-harming behaviors.
Schuring,
Aoki, et al.
(2017)
Establish the association
between osteoarthritis and
CMD in former elite athletes
602 (0:602)
M = 37.0
SD = 6
Various sports
International
Quantitative
Cross-sectional
- Osteoarthritis
Osteoarthritis might be a risk factor for
common mental disorders in athletes, as
significant association with symptoms of
distress, sleep disturbance, alcohol use, and
CMD in former elite athletes was found.

Schuring,
Kerkhoffs,
Gray, &
Gouttebarge
(2017)
Determine the prevalence of
CMD among current and
former professional cricketers
116 (10:106)
M = 27.36
SD = 5.6
Cricket
International
(Europe and Africa)
Quantitative
Observational
prospective
cohort study
with a follow-up
- Surgeries
- Adverse life events
- Career dissatisfaction
Prevalence of 38% for CMD symptoms
among current professional cricketers was
found. Significant associations between
career dissatisfaction and distress,
anxiety/depression and sleep disturbance,
between surgeries and distress, and between
adverse life events and anxiety/depression.
Note: CMD = Common mental disorder; MH = Mental health.

Table 3
Research Design and Sample Characteristics
Study
characteristics
Reference number
n
%
Study Design
Quantitative
Qualitative
Mixed-method
Cross-sectional
Longitudinal
1, 2, 3, 5, 6, 7, 8, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 36, 38, 39, 40,
41, 42, 43
9, 10, 23, 35, 37
4, 26
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
38, 39, 40, 41, 42
19, 20, 22, 43
36
5
2
39
4
84
11
5
91
9
Sample Size
≤10
11-50
51-100
101-300
>300
9, 10, 35
4, 8, 23, 26, 37
5, 6, 7, 24, 29, 33
1, 2, 3, 11, 13, 14, 17, 20, 22, 25, 27, 28, 31, 34, 38, 39, 40, 42
12, 15, 16, 18, 19, 21, 30, 32, 36, 41, 43
3
5
6
18
11
7
12
14
42
25
Gender
Female
Male
Combined
Not specified
31, 33, 40
5, 6, 10, 12, 16, 17, 18, 19, 22, 27, 28, 42
1, 2, 3, 4, 7, 8, 9, 11, 13, 15, 23, 24, 25, 26, 29, 30, 32, 34, 35, 36, 37, 39, 41, 43
14, 20, 21, 38
3
12
24
4
7
28
65
9
Competitive Level
Elite
Professional
Mixed
1, 2, 6, 7, 9, 10, 13, 14, 15, 20, 21, 23, 24, 25, 26, 29, 33, 34, 35, 36, 38, 39, 42
5, 8, 11, 12, 16, 17, 18, 19, 22, 27, 28, 30, 31, 40, 43
3, 4, 32, 37, 41
23
15
5
53
35
12
Type of Sport
Team
Individual
Combined
5, 6, 12, 16, 17, 18, 19, 20, 22, 27, 28, 30, 31, 32, 40, 42, 43
9, 26, 33, 34, 35
1, 2, 3, 4, 7, 8, 10, 11, 13, 14, 15, 20, 21, 23, 24, 25, 29, 36, 37, 38, 39, 41
16
5
22
37
12
51
Athletic Status
Active
Retired
Mixed
1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 18, 19, 20, 21, 23, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 41
40, 42
10, 16, 17, 21, 28, 37, 43
34
2
7
79
5
16

Age
<16
16-26
27-40
>40
Wide age range
Undefined
14, 33, 38
1, 3, 5, 6, 7, 8, 9, 11, 13, 15, 16, 18, 19, 22, 23, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 39, 41
4, 17, 20, 32, 40, 42
10
2, 22, 28, 43
12, 37
3
28
6
1
4
2
7
63
14
2
9
5
Location/Context
Europe
Australia/Oceania
North America
Asia
International
3, 4, 5, 6, 7, 9, 10, 13, 14, 15, 20, 21, 22, 27, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39, 40, 41
1, 2, 11, 12, 23, 24, 25
26, 28
8, 36
16, 17, 18, 19, 42, 43
26
7
2
2
6
60
16
5
5
14
Note. Percentages (%) in relation to the total number of included empirical articles (N = 43). Reference numbers: 1 = Appaneal, Levine, Perna, & Roh
(2009); 2 = Beable, Fulcher, Lee, & Hamilton (2017); 3 = Belz, Heidari, Levenig, Hasenbring, Kellmann, & Kleinert (2018); 4 = Biggin, Burns, &
Uphill (2017); 5 = Biggins, Cahalan, Comyns, Purtill, & O’Sullivan (2018); 6 = Blakelock, Chen, & Prescott (2016); 7 = Breslin, Shannon, Haughey,
Donnelly, & Leavey (2017); 8 = Celebi et al. (2015); 9 = Coyle, Gorczynski, & Gibson (2017); 10 = Doherty, Hannigan, & Campbell (2016); 11 =
Drew et al. (2018); 12 = Du Preez et al., (2017); 13 = Foskett & Longstaff (2017); 14 = Frank, Nixdorf, & Beckmann (2017); 15 = Gerber, Holsboer-
Trachsler, Puhse, & Brand (2011); 16 = Gouttebarge, Aoki, Verhagen, & Kerkhoffs (2016); 17 = Gouttebarge, Frings-Dresen, & Sluiter (2015); 18 =
Gouttebarge, Hopley, et al. (2017); 19 = Gouttebarge, Hopley, et al. (2018); 20 = Gouttebarge, Jonkers, et al. (2017a); 21 = Gouttebarge, Jonkers, et al.
(2017b); 22 = Gouttebarge, Tol, & Kerkhoffs (2016); 23 = Gulliver, Griffiths, & Christensen (2012); 24 = Gulliver Griffiths, Christensen, Mackinnon,
et al. (2012); 25 = Gulliver, Griffiths, Mackinnon, Batterham, & Stanimirovic (2015); 26 = Hammond, Gialloreto, Kubas, & Davis (2013); 27 = Jensen,
Ivarsson, Fallby, Dankers, & Elbe (2018); 28 = Jones (2016); 29 = Jowett & Cramer (2009); 30 = Junge & Feddermann-Demont (2016); 31 = Junge &
Prinz (2018); 32 = Kilic et al. (2017); 33 = Klinkowski, Korte, Pfeiffer, Lehmkuhl, & Salbach-Andrae (2008); 34 = Lundqvist & Raglin (2015); 35 =
Lundqvist & Sandin (2014); 36 = Mousavi, Mousavi, & Yaghubi (2017); 37 = Newman, Howells, & Fletcher (2016); 38 = Nixdorf, Frank, &
Beckmann (2016); 39 = Nixdorf, Frank, Hautzinger, & Beckmann (2013); 40 = Prinz, Dvorak, & Junge (2016); 41 = Schaal et al. (2011); 42 =
Schuring, Aoki, et al. (2017); 43 = Schuring, Kerkhoffs, Gray, & Gouttebarge (2017).

Table 4
Personal and Sport-Environmental Protective and Risk Factors Affecting Elite Athletes’ Mental Health
Factor themes
Reference number
n
%
Personal Protective Factors
13
30
Protective behavior
10, 23, 35, 36, 39
5
12
Feeling of competence
16, 34, 35, 37, 39
5
12
Positive social relationships and support
9, 10, 23, 28
4
9
Recovery
10, 14, 37, 39
4
9
Feeling of autonomy
35, 40
2
5
Basic needs and career satisfaction
13, 34
2
5
Sport-Environmental Protective Factors
11
25
Mental health literacy and support
7, 9, 10, 23, 24, 37
6
14
Trusting and mastery-orientated climate
23, 34, 35
3
7
Successful retirement adjustment
16, 28, 40
3
7
Positive sporting relationships and support
23, 35
2
5
Personal Risk Factors
35
81
Injury and overtraining
1, 3, 4, 8, 12, 20, 21, 22, 23, 25, 28, 30, 32, 37, 40, 43
16
37
Risk behavior and ineffective coping
4, 9, 10, 23, 27, 34, 36, 37, 38, 39
10
23
Adverse life events
17, 19, 20, 21, 22, 32, 37, 40, 43
9
21
Gender (female)
1, 2, 13, 15, 26, 30, 41
7
16
Poor general health
3, 28, 31, 37, 42
5
12
Maladaptive personality traits and identity
10, 27, 28, 34, 37
5
12
Basic needs and career dissatisfaction
20, 21, 22, 34, 43
5
12
Chronic life stress
2, 14, 15, 39
4
9
Poor eating/drinking/sleeping habits
5, 9, 15
3
7
Negative social relationships and low social support
21, 29, 40
3
7
Sport-Environmental Risk Factors
16
37
Sport-specific stressors
4, 6, 9, 10, 11, 23, 26, 27, 30, 31, 37, 40
12
28
Individual and aesthetic sports’ specific features
2, 9, 38, 41
4
9
Crisis-type retirement
2, 17
2
5
Lack of support from teammates and coaches
17, 40
2
5
Stigma towards help seeking
4, 23
2
5

Note. Percentages (%) in relation to the total number of included empirical articles (N = 43). Reference numbers: 1 = Appaneal, Levine, Perna, & Roh (2009);
2 = Beable, Fulcher, Lee, & Hamilton (2017); 3 = Belz, Heidari, Levenig, Hasenbring, Kellmann, & Kleinert (2018); 4 = Biggin, Burns, & Uphill (2017); 5 =
Biggins, Cahalan, Comyns, Purtill, & O’Sullivan (2018); 6 = Blakelock, Chen, & Prescott (2016); 7 = Breslin, Shannon, Haughey, Donnelly, & Leavey
(2017); 8 = Celebi et al. (2015); 9 = Coyle, Gorczynski, & Gibson (2017); 10 = Doherty, Hannigan, & Campbell (2016); 11 = Drew et al. (2018); 12 = Du
Preez et al., (2017); 13 = Foskett & Longstaff (2017); 14 = Frank, Nixdorf, & Beckmann (2017); 15 = Gerber, Holsboer-Trachsler, Puhse, & Brand (2011);
16 = Gouttebarge, Aoki, Verhagen, & Kerkhoffs (2016); 17 = Gouttebarge, Frings-Dresen, & Sluiter (2015); 18 = Gouttebarge, Hopley, et al. (2017); 19 =
Gouttebarge, Hopley, et al. (2018); 20 = Gouttebarge, Jonkers, et al. (2017a); 21 = Gouttebarge, Jonkers, et al. (2017b); 22 = Gouttebarge, Tol, & Kerkhoffs
(2016); 23 = Gulliver, Griffiths, & Christensen (2012); 24 = Gulliver Griffiths, Christensen, Mackinnon, et al. (2012); 25 = Gulliver, Griffiths, Mackinnon,
Batterham, & Stanimirovic (2015); 26 = Hammond, Gialloreto, Kubas, & Davis (2013); 27 = Jensen, Ivarsson, Fallby, Dankers, & Elbe (2018); 28 = Jones
(2016); 29 = Jowett & Cramer (2009); 30 = Junge & Feddermann-Demont (2016); 31 = Junge & Prinz (2018); 32 = Kilic et al. (2017); 33 = Klinkowski,
Korte, Pfeiffer, Lehmkuhl, & Salbach-Andrae (2008); 34 = Lundqvist & Raglin (2015); 35 = Lundqvist & Sandin (2014); 36 = Mousavi, Mousavi, &
Yaghubi (2017); 37 = Newman, Howells, & Fletcher (2016); 38 = Nixdorf, Frank, & Beckmann (2016); 39 = Nixdorf, Frank, Hautzinger, & Beckmann
(2013); 40 = Prinz, Dvorak, & Junge (2016); 41 = Schaal et al. (2011); 42 = Schuring, Aoki, et al. (2017); 43 = Schuring, Kerkhoffs, Gray, & Gouttebarge
(2017).

MH literacy and
support (4)
Successful retirement
adjustment (2)
Trusting and
mastery-orientated
sport climate (3)
Lack of social
support from team-
mates and coaches (2)
Individual and
aesthetic sports
specific features (3)
Sport-specific
stressors (7)
Stigma towards
help-seeking (1)
Crisis-type
retirement (2)
Positive sporting
relationships and
support (2)
Feeling of
autonomy (2)
Feeling of
competence (5)
Protective
behavior (9)
Recovery
(3)
Basic needs and
career satisfaction (2)
Risk behavior and
ineffective coping (8)
Maladaptive personality
traits and identity (4)
Adverse
life events (1)
Female
athlete triad (1)
Injury and
Overtraining (5)
Basic needs and
career
dissatisfaction (2)
Poor general
health (5)
Poor eating/
sleeping/ drinking
habits (2)
Chronic life
Stress (1)
Positive social
relationships
and support (3)
Negative social
relationships and low
social support (3)
Athletes’
mental health
Risk factors
Sport-environmental domain
Protective factors
Personal domain
Figure 2. Conceptual map of protective and risk factors related to elite athletes’ mental health. Numbers in brackets indicate numbers of correlates/associations.

Factors affecting the mental health of elite athletes
1
Appendix 1: PRISMA-ScR Checklist (Tricco et al., 2018)
Section
Item
PRISMA-ScR Checklist Item
Reported
on page#
Title
1
The review title includes “scoping review”
Abstract
2
Based on the journal’s guidelines, an unstructured
abstract of 200 words is provided. It includes
objectives, amount of studies included, charting
methods, results, and conclusions
1
Introduction
Rationale
3
Although there are existing reviews on mental
health in elite sport, no review has systematically
screened article for protective and risk factors
4
Objectives
4
The purpose of this scoping review was threefold:
(a) to provide an overview of research concerning
the MH of elite athletes from 1998 to 2018, (b) to
identify and categorize factors affecting elite
athletes’ MH, and (c) to critically appraise the
papers and detect research gaps and future
challenges
5-6
Methods
Protocol and registration
5
The study has not been officially registered
n/a
Eligibility criteria
6
Only English peer-reviewed articles were included
in the present review. Due to that elite athletes’
mental health is and emerging topic, we limited the
search for the last 20 years
6-7
Information sources
7
The search strategy was applied in the following
databases: SPORTDiscus, SCOPUS, PsychINFO,
EMBASE, and PubMed
6
Search
8
Key terms of the database search are explained and
an example for one database (SCOPUS) is
provided
6
Selection of sources of
evidence
9
Inclusion and exclusion criteria are described in
detail
6-7
Data charting process
10
The two authors met on several occasions to
review the initial records identified and applied the
inclusion and exclusion criteria independently.
The qualitative data analysis process to identify
protective and risk factors is described
7
7
Data items
11
We looked for associations (qualitative studies)
and correlates (quantitative studies) in the included
articles related to athletes’ MH.
8
Table 2
Critical appraisal
12
Not conducted in a systematic way
n/a
Summary measures
13
Not applicable for scoping reviews
n/a
Synthesis of results
14
Results are presented both in tabular, graphical,
and narrative forms
9
Table 2-4
Figure 2
Risk of bias across
studies
15
Not applicable for scoping reviews
n/a
Additional analyses
16
Not applicable for scoping reviews
n/a
Results
Selection of sources of
evidence
17
We describe the selection process including the
different stages (identification, screening,
8

Factors affecting the mental health of elite athletes
2
eligibility, and inclusion). Figure 1 presents the
flow chart
Figure 1
Characteristics of sources
of evidence
18
Table 2 provides an overview of the 43 included
articles in relation to authors, year, aims, sample,
location, sport, research design, protective/risk
factors, and main findings
9-10
Table 2
Critical appraisal within
sources of evidence
19
Not conducted in a systematic way
n/a
Results of individual
sources of evidence
20
Table 2 gives an overview of all the correlates and
associations of the individual articles that relates to
the protective and risk factors (rational of the
study)
9-13
Table 2
Synthesis of results
21
Table 3 (Research Design and Sample
Characteristics) and Table 4 (Personal and Sport-
Environmental Protective and Risk Factors
Affecting Elite Athletes’ Mental Health)
summarize the findings in tabular forms.
Figure 2 (Conceptual map of protective and risk
factors related to elite athletes’ mental health)
provides a graphical overview of the results
9-13
Risk of bias across
studies
22
Not applicable for scoping reviews
n/a
Additional analyses
23
Not applicable for scoping reviews
n/a
Discussion
Summary of evidence
24
Summarizes the main results through the critical
lens of MH propositions (MH should be better
defined in sport; MH research should broaden the
scope of assessment; MH as a resource for an
athletic career; influence of the environment on
athletes’ MH), link to the review questions and
objectives, and consider the relevance researchers,
coaches, and practitioners.
13-21
Limitations
25
Discusses some limitations of the scoping review
process and the results.
21
Conclusions
26
Provides a general interpretation of the results with
respect to the review questions and objectives, as
well as potential implications and/or next steps.
23-24
Funding
27
Describes sources of funding for the included
sources of evidence, as well as sources of funding
for the scoping review.
24