Self-reported prevalence of sleep and mental health disorders in current and former athletes: a preliminary cross-sectional investigation of help-seeking behaviours

ABSTRACT

Objective

Athletes typically report a high prevalence of mental health issues and face significant barriers within the sporting environment that prevent help seeking. However, little is known about how retired or non-elite athletes engage with support; this lack of empirical data is even more pronounced in the case of sleep disorders.

Method

A cross-sectional online survey was completed by current and former athletes (n = 946; 495 women, 444 men, 7 non-binary) from all sports and competition levels in seven countries. Self-disclosed diagnosis history, help-seeking behaviours, and functional impact of sleep and mental health disorders were assessed.

Results

Comparable rates of mental health disorders were observed between current athletes (39.3%) and former athletes (42.0%), OR = 1.17, p = .337. Similarly, comparable rates of sleep disorders were observed between current athletes (25.4%) and former athletes (30.9%), OR = 1.20, p = .298. Men reported lower mental health disorder prevalence than women (32.9% vs. 46.3%), OR = 1.82, p < .001. Gender differences were also evident in the types of disorders reported. For sleep disorders, men were less likely to seek assistance from a psychologist than women (8.4% vs. 26.2%), p < .001,φ = 0.23. No differences in help seeking avenues for mental health were observed. Comorbid disorders had more frequent functional impairment than individual sleep or mental health disorders.

Conclusions

These self-reported prevalence estimates have significance for practitioners and clinicians. Data indicates the most prevalent types of disorders in these populations and where athletes typically engage in support. Evaluating the time course of these disorders can inform health promotion strategies and treatment methods for practitioners and support staff.

Key Points

What is already known about this topic:

1. Higher rates of mental health disorders are observed in athletes who are women–a potential consequence of men fearing breaches of masculinity.

2. Former athletes present similar prevalence of sleep and mental health problems to current athletes, despite the removal of sport specific demands and barriers to help seeking.

3. Despite increased attention to mental health in sports, various barriers prevent athletes from engaging in help-seeking behaviours.

What this topic adds:

1. This novel study explores the help seeking behaviours of both current and retired athletes. Comparable diagnosis rates between cohorts were observed, underscoring the need for continued support post-retirement.

2. Gender disparities in diagnosis rates, diagnosis types and help-seeking avenues were apparent, emphasising the need for further research in gender diverse samples.

3. Comorbid disorders appeared to have greater perceived functional impairment, highlighting the importance of early intervention and equal promotion of sleep and mental health education.

KEYWORDS:

• Sport
• retired athletes
• anxiety
• depression
• help seeking
• stigma

Introduction

Despite a surge in research and promotion of mental health in sport (Lundqvist & Andersson, 2021; Reardon et al., 2019), many athletes do not seek help for these issues (Neumann et al., 2023a). Lack of mental health literacy, fear of repercussions (i.e., deselection, negative evaluation) and confidentiality breaches, denial, and media scrutiny (Cosh et al., 2022; Gulliver et al., 2012; Lebrun et al., 2020; Purcell et al., 2019) contribute to lower help-seeking rates than non-athletes (Cosh et al., 2022). These barriers influence how and where athletes seek help; often athletes prefer external support or performance-focused sport psychology (Brown et al., 2023; Crawford et al., 2022; Neumann et al., 2023b). Although previous research has explored the barriers and help-seeking behaviours of current athletes, the help-seeking behaviours of retired athletes are unknown.

These barriers to help-seeking are not unique to sport; many present in context-specific ways within sport and prevent help-seeking for disordered sleep. Over half of athletes self-report that they do not seek help for sleep problems (Juliff et al., 2015). Combined with this, coaches and support staff seldomly promote good sleep practices within the training environment (Miles et al., 2019). Consequently, access to treatment may be limited. There is considerable overlap between sleep and mental health problems, where poor sleep can contribute to mental ill health and vice versa (Asplund & Chang, 2020; Benjamin et al., 2020). Equally, improvements in one domain of sleep or mental health may improve the other (Scott et al., 2021). Therefore, help-seeking and intervention may benefit both conditions or potentially prevent comorbidity. Ramsawh et al. (2009) found that comorbid poor sleep and anxiety are associated with greater functional impairment than anxiety alone, which emphasises the importance of early intervention.

Gender can also influence the way in which athletes engage in help-seeking behaviours. Men who are athletes are perceived as less capable than women in their ability to articulate their feelings and are less willing to seek support, as this perceived “weakness” misaligns with traditional views of masculinity (Agnew & Drummond, 2015; Gulliver et al., 2012; Moreland et al., 2018). Sexualised body perceptions, social media, financial hardship, interpersonal conflict, and discrimination can contribute to increased mental health symptomology in women athletes (Åkesdotter et al., 2020; Schaal et al., 2011; Walton et al., 2021). Previous literature has highlighted how gender can influence help seeking behaviours within the context of sport; however, this knowledge is based solely on research involving currently competing athletes.

Current and retired athletes present comparable prevalence rates of sleep and mental health problems (Gouttebarge et al., 2019; Montero et al., 2022), but the way in which athletes engage in help-seeking after retirement is greatly unexplored. Currently, much of the research on mental health and sleep within sport has focused on elite athlete populations (Montero et al., 2022). The foreclosure of sport can be psychologically distressing (Brewer & Petitpas, 2017), especially because access to resources and support are often limited following retirement (Jewett et al., 2018). Conceptually, known factors that impact athlete health would be less pertinent following retirement (e.g., performance expectations, coaching pressures, injury, travel, training, competition timing; Arnold & Fletcher, 2012; Gupta et al., 2017); however, the reduction of sport-specific barriers to help-seeking (e.g., deselection) may encourage help-seeking. Moreover, while sports specific demands may more greatly impair sleep and mental health for currently competing athletes to a greater extent than former athletes, barriers to help seeking may inhibit current athletes from seeking necessary support and receiving a formal diagnosis. So, while existing prevalence estimates between current and former athletes appear comparable, perceived barriers to help seeking within sport may reflect disproportionate self-reported diagnosis rates between current and retired athletes.

Therefore, we aimed to compare diagnosis rates and help-seeking avenues of current and former athletes by investigating self-disclosed reports of clinical sleep and mental health disorder diagnoses. This knowledge can assist practitioners and clinicians to better understand how athletes engage in help seeking behaviours, helping to optimise support. We hypothesise that: i) retired athletes will have a higher self-reported prevalence of sleep and mental health disorders than current athletes; ii) women will have a higher self-reported prevalence of sleep and mental health disorders than men; iii) those with comorbid sleep and mental health diagnoses will experience greater functional impairment.

Method

Participants

Nine hundred and forty-six adults (women = 495, men = 444, non-binary/other = 7) completed an anonymous online Qualtrics survey as part of a broader study aimed at assessing sleep and mental health using screening tools. For this manuscript, data related to help-seeking will be presented. Local, state, and national sporting organisations from all sports in seven countries were approached via email to advertise the study on the researcher’s behalf. Recruitment was achieved using convenience sampling by sporting organisations sharing a brief description of the study and recruitment flyer including a QR code for the online survey. Information was shared via a combination of social media posts, email correspondence, newsletters, and word of mouth. Participation was open to competing or retired athletes (i.e., stopped playing competitive sport <20 years ago), from all genders, sports, and competition levels (amateur to professional). Currently, there is no consensus approach to defining an athlete’s level of competition in sport research (McAuley et al., 2022). Although there are more sophisticated ways to define “eliteness” in sport research (e.g., Swann et al., 2015), we aimed to refer to a broader demographic than elite level athletes while prioritising survey succinctness. Thus, participants selected from the following four options: amateur level (i.e., receive no form of income/sponsorship, play competitively), sports institute or university/college level (i.e., involved in sports development program, e.g., Australian Institute of Sport (AIS), National Collegiate Athletic Association (NCAA)), semi-professional (i.e., earn income/sponsorship from sport, but hold other employment), professional (i.e., full time paid athlete). Due to non-homogeneous distribution among gender, participants who reported being non-binary/other gendered (n = 7) were excluded from data analysis. Demographic data of participants are presented in Table 1.

Table 1. Participant characteristics.

Characteristic	n	%
Age (years)
18-29	462	49.2
30-39	139	14.8
40-49	113	12.0
50-59	98	10.4
60 and over	127	13.5
Gender
Men	444	47.3
Women	495	52.7
Sport
Alpine	1	0.1
American football/gridiron	12	1.3
Archery/shooting	29	3.1
Athletics	52	5.5
Australian rules football	66	7.0
Baseball/softball	10	1.1
Basketball	15	1.6
Bowling	19	2.0
Cheerleading/dance	6	0.6
Climbing/mountaineering/orienteering	13	1.4
Combat sports (e.g., boxing, karate, judo, etc.)	23	2.4
Cricket	42	4.5
Croquet	2	0.2
Cycling	22	2.3
Equestrian	30	3.2
Football/soccer	42	4.5
Flying disc	2	0.2
Gaelic football	2	0.2
Golf	4	0.4
Gymnastics/trampoline	26	2.8
Hockey/ice hockey	14	1.5
Lacrosse	2	0.2
Motorsport	2	0.2
Netball	32	3.4
Paddle sports (e.g., canoeing, paddleboarding, rowing)	20	2.1
Racquet sports (e.g., badminton, tennis, squash)	35	3.7
Rugby	95	10.1
Sailing	2	0.2
Skating	12	1.3
Skipping	9	1.0
Surf life saving/surfing	8	0.9
Swimming/diving	65	6.9
Touch football/rugby	7	0.7
Triathlon	25	2.7
Volleyball	16	1.7
Water polo	11	1.2
Weightlifting/CrossFit	166	17.7
Playing Status
Currently competing	732	78.0
Retired	207	22.0
Level of Competition
Amateur	642	68.4
Sports Institute or university/college	127	13.5
Semi-professional	128	13.6
Professional	42	4.5
Country of Residence
Australia	602	64.1
Canada	9	1.0
Ireland	31	3.3
New Zealand	48	5.1
South Africa	6	0.6
United Kingdom	39	4.2
United States of America	204	21.7

Materials and procedure

Participants voluntarily accessed the survey information via an anonymous link or QR code and progressed by manually providing informed consent. After providing demographic information, participants were asked about lifetime diagnoses of sleep and mental health disorders. Participants responded to the following question: “Have you ever been diagnosed with a sleep/mental health disorder?” If they selected “Yes”, they were prompted to answer more questions. Participants were asked: “Have you ever been diagnosed with the following?” and “Who did you see for your sleep/mental health problem?” by selecting “Yes” or “No” from a list of options (see Tables 2 and 3). These questions were based on common sleep and mental health disorders and commonly accessed support resources identified from a literature search in athletic populations. Lastly, participants were asked “Did/does your sleep/mental health problem impair your: i) sports training, ii) sports performance, iii) general lifestyle?” and selected either “Yes” or “No”.

Table 2. Sleep and mental health disorder diagnoses and who athletes sought help from by playing status.

Disorder Type		Current Athlete (n=732)	Retired Athlete (n=207)
Sleep Disorders Sleep Disorder Diagnosis
Bruxism	χ^2(1,938)=2.08,p=.149,φ=0.05	86(11.7%)	17(8.2%)
Frequent urination (i.e., Nocturia)	χ^2(1,938)=0.35,p=.557,φ=0.02	35(4.8%)	12(5.8%)
Insomnia	χ^2(1,938)=3.75,p=.053,φ=0.06	54(7.4%)	24(11.6%)
Narcolepsy	χ^2(1,938)=2.00,p=.158,φ=0.05	7(1.0%)	0(0%)
Obstructive sleep apnoea (OSA)	χ^2(1,938)=6.94,p=.008,φ=0.09	35(4.8%)	20(9.7%)
Restless leg syndrome	χ^2(1,938)=0.11,p=.736,φ=0.01	28(3.8%)	9(4.3%)
Rapid eye movement (REM) behaviour disorder	χ^2(1,938)=9.81,p=.002,φ=0.10*	1(0.1%)	4(1.9%)
Vivid dreams/nightmares	χ^2(1,938)=3.43,p=.064,φ=0.06	41(5.6%)	19(9.2%)
Who athletes sought help from for their sleep disorder^†
General practitioner/family doctor	χ^2(1,280)=0.05,p=.825,φ=0.01	116(62.4%)	42(65.6%)
Psychologist	χ^2(1,280)=1.11,p=.292,φ=0.06	37(19.9%)	16(25.0%)
Self-sought	χ^2(1,280)=2.96,p=.085,φ=0.10	48(25.8%)	10(15.6%)
Sleep physician	χ^2(1,280)=0.04,p=.838,φ=0.01	42(22.6%)	14(21.9%)
Sports physician/team doctor	χ^2(1,280)=2.85,p=.091,φ=0.10	7(3.8%)	6(9.4%)
Other (e.g., physiotherapist)	χ^2(1,280)=3.56,p=.059,φ=0.11	36(19.4%)	6(9.4%)
Mental Health Disorders Diagnosis type
Anxiety	χ^2(1,939)=1.12,p=.291,φ=0.03	216(29.5%)	69(33.3%)
Bipolar affective disorder	χ^2(1,939)=0.10,p=.756,φ=0.01	9(1.2%)	2(1.0%)
Depression	χ^2(1,939)=1.26,p=.263,φ=0.04	194(26.5%)	63(30.4%)
Dissociative and dissociation disorder	χ^2(1,939)=0.10,p=.912,φ=0.00	4(0.5%)	1(0.5%)
Eating disorder	χ^2(1,939)=0.18,p=.672,φ=0.01	37(5.1%)	12(5.8%)
Obsessive compulsive disorder (OCD)	χ^2(1,939)=0.01,p=.906,φ=0.00	26(3.6%)	7(3.4%)
Paranoia	χ^2(1,939)=0.85,p=.356,φ=0.03	3(0.4%)	0(0%)
Personality disorder	χ^2(1,939)=0.45,p=.503,φ=0.02	4(0.5%)	2(1.0%)
Post-traumatic stress disorder (PTSD)	χ^2(1,939)=0.05,p=.828,φ=0.01	43(5.9%)	13(6.3%)
Psychosis	χ^2(1,939)=1.83,p=.177,φ=0.04	2(0.3%)	2(1.0%)
Schizophrenia	χ^2(1,939)=0.57,p=.452,φ=0.03	2(0.3%)	0(0%)
Who athletes sought help from for their mental health disorder^†
Counsellor	χ^2(1,374)=0.09,p=.761,φ=0.02	68(23.6%)	22(25.3%)
General practitioner/family doctor	χ^2(1,374)=0.06,p=.804,φ=0.01	133(46.2%)	39(44.8%)
Psychologist	χ^2(1,374)=1.60,p=.206,φ=0.07	180(62.5%)	48(55.2%)
Self-sought	χ^2(1,374)=0.87,p=.352,φ=0.05	47(16.3%)	18(20.7%)
Sports physician/team doctor	χ^2(1,374)=1.46,p=.227,φ=0.06	16(5.6%)	8(9.2%)
Other (e.g., naturopath)	χ^2(1,374)=0.21,p=.647,φ=0.02	19(6.6%)	7(8.0%)

Note. Data is n (%). Bold* denotes statistical significance at the p<.05 level. ^† % is from group with diagnosed sleep or mental health disorders, respectively. A sleep disorder was reported by n=186 current athletes and n=64 retired athletes. A mental health disorder was reported by n=288 current athletes and n=87 retired athletes.

Table 3. Sleep and mental health disorder diagnoses and who athletes sought help from by gender.

Disorder Type		Men (n=444)	Women (n=495)
Sleep Disorders
Diagnosis type
Bruxism	χ^2(2,938)=4.07,p=.044,φ=0.07*	39(8.8%)	64(12.9%)
Frequent urination (i.e., Nocturia)	χ^2(2,938)=5.47,p=.019,φ=0.08*	30(6.8%)	17(3.4%)
Insomnia	χ^2(2,938)=0.45,p=.501,φ=0.02	34(7.7%)	44(8.9%)
Narcolepsy	χ^2(2,938)=0.05,p=.816,φ=0.01	3(0.7%)	4(0.8%)
Obstructive sleep apnoea (OSA)	χ^2(2,938)=19.90,p<.001,φ=0.15*	42(9.5%)	13(2.6%)
Restless leg syndrome	χ^2(2,938)=1.40,p=.236,φ=0.04	21(4.7%)	16(3.2%)
Rapid eye movement (REM) behaviour disorder	χ^2(2,938)=0.11,p=.745,φ=0.01	2(0.5%)	3(0.6%)
Vivid dreams/nightmares	χ^2(2,938)=6.23,p=.013,φ=0.08*	19(4.3%)	41(8.3%)
Who athletes sought help from for their sleep disorder^†
General practitioner/family doctor	χ^2(2,280)=2.05,p=.153,φ=0.15	68(56.7%)	90(69.2%)
Psychologist	χ^2(2,280)=15.02,p<.001,φ=0.23*	11(9.2%)	39(30.0%)
Self-sought	χ^2(2,280)=2.30,p=.129,φ=0.09	22(18.3%)	36(27.7%)
Sleep physician	χ^2(2,280)=12.48,p<.001,φ=0.21*	38(31.7%)	18(13.8%)
Sports physician/team doctor	χ^2(2,280)=0.00,p=.963,φ=0.00	6(5.0%)	7(5.4%)
Other (e.g., physiotherapist)	χ^2(2,280)=2.43,p=.119,φ=0.09	15(12.5%)	27(20.8%)
Mental Health Disorders Diagnosis type
Anxiety	χ^2(2,939)=20.39,p<.001,φ=0.15*	103(23.2%)	182(36.8%)
Bipolar affective disorder	χ^2(2, 939)=1.19,p=.275,φ=0.04	7(1.6%)	4(0.8%)
Depression	χ^2(2,939)=5.18,p=.023,φ=0.07*	106(23.9%)	151(30.5%)
Dissociative and dissociation disorder	χ^2(2,939)=0.11,p=.744,φ=0.11	2(0.5%)	3(0.6%)
Eating disorder	χ^2(2,939)=25.46,p<.001,φ=0.17*	6(1.4%)	43(8.7%)
Obsessive compulsive disorder (OCD)	χ^2(2,939)=2.67,p=.102,φ=0.05	11(2.5%)	22(4.4%)
Paranoia	χ^2(2,939)=0.45,p=.501,φ=0.02	2(0.5%)	1(0.2%)
Personality disorder	χ^2(2,939)=0.02,p=.894,φ=0.00	3(0.7%)	3(0.6%)
Post-traumatic stress disorder (PTSD)	χ^2(2,939)=1.53,p=.216,φ=0.04	22(5.0%)	34(6.9%)
Psychosis	χ^2(2,938)=1.24,p=.266,φ=0.04	3(0.7%)	1(0.2%)
Schizophrenia	χ^2(2,938)=2.23,p=.135,φ=0.05	2(0.5%)	0(0%)
Who athletes sought help from for their mental health disorder^†
Counsellor	χ^2(2,381)=1.05,p=.305,φ=0.05	31(21.2%)	59(25.8%)
General practitioner/family doctor	χ^2(2,381)=2.31,p=.129,φ=0.08	60(41.1%)	112(48.9%)
Psychologist	χ^2(2,381)=3.03,p=.082,φ=0.09	81(55.5%)	147(64.2%)
Self-sought	χ^2(2,381)=2.26,p=.133,φ=0.08	20(13.7%)	45(19.7%)
Sports physician/team doctor	χ^2(2,381)=2.12,p=.145,φ=0.08	6(4.1%)	18(7.9%)
Other (e.g., naturopath)	χ^2(2,381)=0.80,p=.370,φ=0.05	8(5.5%)	18(7.9%)

Note. Data is n (%). Bold* denotes statistical significance at the p<.05 level. ^† % is from group with diagnosed sleep or mental health disorder, respectively. A sleep disorder was reported by n=120 men and n=130 women. A mental health disorder was reported by n=146 men and n=229 women.

Statistical analysis

IBM SPSS Statistics V28 (IBM Corp; Armonk, NY) was used for statistical analysis. Independent variables were playing status (current, retired), gender (women, men), comorbidity (comorbid sleep and mental health disorders, sleep/mental health disorders only). Dependent variables were diagnosis status, diagnosis type, who athletes sought help from, and impairment in training, performance, or general lifestyle. Independent samples t-test and chi-squared tests were used to compare participant demographics. Logistic regressions were used to compare diagnosis rates between playing status and gender while accounting for age (years) in each model. A series of chi-squared tests were used to compare differences in diagnosis types, who athletes sought help from and to assess whether the comorbidity of self-reported diagnoses exacerbated functional impairment. An alpha level of .05, with 95% confidence intervals was used for all statistical tests. Effect size cut-off scores of > 0 (no/very weak), >0.05 (weak), >0.10 (moderate), >0.15 (strong), >0.25 (very strong) were used for Phi (φ) (Akoglu, 2018).

Results

An independent samples t-test revealed that former athletes (38.6 ± 14.0 years) were older than current athletes (34.1 ± 15.0 years), t(93) =-3.84, p < .001. A chi-squared analysis revealed that there were a greater proportion of current athletes were women (54.6%) compared to former athletes who were women (45.9%), χ²(1,939) = 4.96,p = .026,φ = 0.07.

Logistic regressions were performed to ascertain the effects of playing status and gender on the self-reported lifetime prevalence of sleep and mental health disorders, while accounting for age. For mental health disorder prevalence, the model was statistically significant, χ²(3) = 18.82, p < .001. Gender was associated with the prevalence of mental health disorders (OR = 1.82[95% CI:1.37,2.41], p < .001), whereby women were more likely to self-report lifetime diagnoses of mental health disorders (46.3%) than men (32.9%). However, neither age (OR = 1.00[95% CI:0.99,1.01], p = .638) nor playing status (OR = 1.17[95% CI:0.85,1.61], p = .337) were associated with the prevalence of mental health disorder diagnoses. Moreover, comparable prevalence of mental health disorders between current athletes (39.3%) and former athletes (42.0%) were observed.

For sleep disorder prevalence, the model was statistically significant, χ²(3) = 31.83, p < .001. Older respondents were more likely to self-report lifetime diagnoses of sleep disorders than younger respondents (OR = 1.03[95% CI:1.02,1.04], p < .001). However, neither gender (OR = 1.32[95% CI:0.96,1.82], p=.085) nor playing status (OR = 1.20[95% CI:0.85,1.70], p = .298) were associated with the prevalence of sleep disorder diagnoses. Moreover, comparable prevalence of sleep disorders between women (26.3%) and men (27.0%), and current athletes (25.4%) and former athletes (30.9%) were observed.

In general, playing status was not associated with the types of sleep or mental health disorder diagnoses or who respondents sought help from (Table 2). However, a higher prevalence of rapid eye movement (REM) behaviour disorder was observed in retired athletes (1.9%) compared to current athletes (0.1%). Men reported lower rates of bruxism (8.8% vs. 12.9%) and nightmares/vivid dreams (4.3% vs. 8.3%) but reported higher rates of OSA (9.5% vs. 2.6%) and frequent urination at night (6.8% vs. 3.4%) than women, respectively (Table 3). When seeking help for their sleep disorder, men were more likely to seek help from a sleep physician (29.0% vs. 12.1%), but less likely to seek help from a psychologist (8.4% vs. 26.2%) than women, respectively. Men reported lower rates of anxiety (23.2% vs. 36.8%), depression (23.9% vs. 30.5%), and eating disorders (1.4% vs. 8.7%) than women, respectively; however, there were no differences in the help seeking resources accessed for their mental health disorders.

As seen in Table 4, those with comorbid sleep and mental health disorders (n = 160, 17.0%) reported more often that their mental health disorder impacted their sports training, sports performance, and general lifestyle than those with either a sleep disorder or mental health disorder only.

Table 4. Functional impairment of sleep and mental health disorder for those with comorbid sleep and mental health disorders compared to sleep and mental health disorders only.

Functional impairment of sleep disorder	Comorbid disorders		Sleep disorder only
	n	%	n	%
Sports training (χ^2(1,280)=8.50,p=.004,φ=0.17)*	87	56.9	50	39.4
Sports performance (χ^2(1,279)=15.24,p<.001,φ=0.23)*	93	60.8	47	37.3
General lifestyle (χ^2(1,280)=9.36,p=.002,φ=0.18)*	121	78.6	78	61.9
Functional impairment of mental health disorder	Comorbid disorders		Mental health disorder only
	n	%	n	%
Sports training (χ^2(1,374)=4.87,p=.027,φ=0.12)*	102	63.7	112	52.3
Sports performance (χ^2(1,374)=8.48,p=.004,φ=0.15)*	107	66.9	111	51.9
General lifestyle (χ^2(1,374)=3.92,p=.048,φ=0.10)*	145	90.6	178	83.6

Data is n (%). Bold* denotes statistical significance at the p < .05 level.

Discussion

This study aimed to compare diagnosis rates and explore help-seeking behaviours of current and former athletes by investigating self-disclosed reports of clinical sleep and mental health disorder diagnoses. Results from the large-scale online survey partially support the hypotheses. Specifically, aligning with our hypotheses, women had a higher prevalence of mental health disorders than men. Comorbid sleep and mental health disorders were associated with greater functional impairment than sleep or mental health disorders alone. However, contrary to our hypothesis, playing status was not associated with the prevalence of mental health disorders, and both playing status and gender were not associated with the prevalence of sleep disorders. These findings provide preliminary evidence on the prevalence of sleep and mental health disorders in current and former athletes and offers insight into their help seeking behaviours. These results help to inform support staff and clinicians on the types of sleep and mental health disorders that may be prevalent in different athletic populations and highlights how athletes engage in support.

Despite the removal of sport-specific barriers after retirement, current and retired athletes presented comparable diagnosis rates of sleep and mental health disorders. Findings reflect research in elite athletic populations by Oevreboe et al. (2023), where 44.3% were diagnosed with a mental disorder and 24.5% were diagnosed with sleep problems from diagnostic interviews. In the present study, the lifetime prevalence of mental health disorders in current athletes was 39.3% and sleep disorders were 25.4%. Åkesdotter et al. (2020) observed a slightly higher lifetime prevalence of mental health problems in elite athletes (51.7%). Although most of the present sample are current or former amateur athletes, prevalence estimates align with wider research in elite athletic populations using screening tools to screen for symptomology of sleep and mental health issues (Gouttebarge et al., 2019). Although elite athletes are considered to face greater stressors related to mental health and greater demands on their sleep due to travel and performance (Arnold & Fletcher, 2012; Gupta et al., 2017), level of competition may be positively correlated with level of funding, and consequently, the level of support and resources available to athletes. Furthermore, while elite athletes may be more at risk for these issues, they may be better equipped to prevent and treat sleep and mental health issues. Results highlight that retired athletes are not exempt from these issues and report low help-seeking behaviours after exiting sport. Although there may be an increased desire to engage in help seeking behaviours following retirement, these efforts may be futile if support is inaccessible. Therefore, sporting organisations should endeavour to continue to offer support following retirement – irrespective of their level of competition.

Older respondents were more likely to self-report lifetime diagnoses of sleep disorders than younger respondents, which may suggest that attitudes towards help seeking improve with age. In the general population, older adults are suggested to be more likely to seek help than younger adults (Mackenzie et al., 2006). We must also consider that higher diagnosis rates in older adults may be the result of increased opportunities for help seeking. While former athletes tended to be older, playing status was not independently associated with the prevalence of sleep or mental health disorders. This may suggest that age shows a greater association with reporting a lifetime sleep or mental health diagnosis than playing status. Furthermore, there were no differences between current and former athletes in diagnosis type and help seeking avenues, which reinforces this notion. Despite a higher prevalence of Rapid Eye Movement (REM) behaviour disorder being observed in retired athletes (1.9%) compared to current athletes (0.1%), this is not considered to be a substantive finding given the rates observed in the cohort.

Women reported a higher prevalence of mental health disorders than men, reflecting previous literature (Schaal et al., 2011; Walton et al., 2021). Moreover, in the present study, women reported higher rates of anxiety, depression, and eating disorders than men. This finding supports research by Schaal et al. (2011) who observed that the lifetime prevalence of these three disorders was significantly greater in women compared to men (8.1% vs. 0.5%, respectively). Wolanin et al. (2016) found that collegiate women athletes presented 1.84 times greater risk than men for clinically relevant depressive symptoms, which is comparable to the prevalence observed in the current study. Bratland-Sanda and Sundgot-Borgen (2013) also observed disproportionately higher rates of eating disorders and anxiety in women athletes than men athletes. Yet, men remain over-represented in sport research (Walton et al., 2022). Results also reinforce previous sport research suggesting that men are less likely to seek support than women and are less able to articulate issues that arise (Gulliver et al., 2012; Moreland et al., 2018). Therefore, men who are athletes may not necessarily be at a lower risk of sleep and mental health problems than women; rather, they may be less likely to receive a diagnosis due to a lower inclination to seek help. Men reported lower rates of bruxism and nightmares/vivid dreams but reported higher rates of OSA and frequent urination than women. These results align with the broader literature on gender differences in the prevalence of sleep disorders (Bonsignore et al., 2019; Kinalski et al., 2019; Schredl & Reinhard, 2011); however, the prevalence of nocturia is thought to increase at a constant rate with age, but almost twice as rapidly in men (Tikkinen et al., 2006). Not only was gender associated with the types of disorders reported, but gender influenced how athletes engaged in support.

Findings indicated that athletes preferred to seek help outside of the sporting environment (e.g., psychologists, general practitioners) rather than within their sporting environment, potentially for fear of confidentiality breaches (Crawford et al., 2022; Gulliver et al., 2015; Roberts et al., 2016). Differences in help seeking behaviours for sleep disorders were more apparent between genders. Men who are athletes were more likely to seek help from a sleep physician but less likely to seek help from a psychologist than women. Rooney et al. (2021) found that individual women who are athletes were more likely to engage with sport psychologists than men who are athletes. This finding suggests that stigma surrounding help-seeking within sport remains a significant barrier for athletes engaging in necessary support and may be perceived to a greater extent by men athletes than women athletes. We acknowledge that help seeking avenues also depend on availability of resources, which are influenced by several factors, such as level of competition and socioeconomic status of the athlete. Greater emphasis should be placed on destigmatising help-seeking in sport through better education to develop mental health literacy as a preventative measure to address gender disparity in diagnosis rates and help-seeking behaviours.

Comorbid diagnoses appeared to exacerbate functional impairment, supporting literature on the bi-directional relationship between sleep and mental health (Asplund & Chang, 2020) and the impact of their comorbidity (Ramsawh et al., 2009). The consequences of comorbid diagnoses on athletic performance and health highlight the importance of early intervention. Disordered sleep also appeared to be associated with less functional impairment than mental ill-health, even for those who reported comorbid disorders. Moreover, in general, for those with comorbid sleep and mental health disorders, the impact of their sleep problem was less than the impact of their mental health problem. Recent emphasis on mental health promotion (Lundqvist & Andersson, 2021; Reardon et al., 2019) has led to greater awareness, and may explain the disproportionately higher prevalence of mental ill-health than disorder sleep observed due to increased levels of reporting. We argue that equal attention for sleep is needed. Moreover, Gwyther et al. (2022) highlight that interventions focused on sleep hygiene, assisted sleep, and sleep extension can improve sleep, performance, and mood outcomes. We anticipate that greater emphasis on sleep education for athletes can improve knowledge and promote help-seeking.

Some limitations of this research design should be acknowledged. Obtaining data on lifetime diagnoses limits our conclusions about the association between playing status, gender, and help-seeking. Future longitudinal research should identify the incidence and persistence of help-seeking. Measuring the time course of these disorders in relation to the stage of an athlete’s career would extend these findings, to pinpoint when athletes are likely to actively engage in support. Moreover, this knowledge would aid in optimising help seeking promotion initiatives.

The diversity of participants within the sample increases representation of under researched athletic populations, but low saturation within groups limits the extent to which comparisons can be made between sports. To mitigate interpretational errors, participants who identified as having non-binary gender identity were removed due to non-homogeneous sample sizes. Although the number of individuals who were non-binary recruited (0.7%) is not dissimilar to the proportion of individuals who are non-binary that make up the Australian population (0.17%; Australian Bureau of Statistics, 2022) and sport and exercise psychology research participants (0.2%; Walton et al., 2022); all seven of these individuals reported a diagnosis of at least one type of mental health disorder. Further research into this demographic is warranted as they may have greater risk for mental health disorders and may be subjected to greater barriers which prevent help seeking.

Previous attempts to develop methods to define levels of athletic participation by eliteness or training level (e.g., De Pauw et al., 2013; Swann et al., 2015) were considered impractical for this study. After careful consideration, an a priori decision was made to employ a novel approach to classify the level of competition. We recognise that the chosen method may be a limitation of the research with potential misalignment between representational and financial level of competition (i.e., elite vs. professional). Further work is required in developing categorisation methods for competition level in large-scale quantitative surveys.

Despite the prevalence of sleep and mental health disorders across playing status being comparable, results suggest gender disparity in mental ill-health within sport. Diagnosis types and help-seeking avenues also differed by gender, which has implications for those who support athletes (i.e., coaches and support staff, practitioners, clinicians, etc.). Understanding groups who may be at greatest risk for sleep and mental health and whether they engage with support is invaluable in optimising health within sport. Furthermore, these findings have implications for athletes of all competition levels – from grassroots competitions up to elite competitors. This research highlights that there are gender differences in the rates of mental health problems, the types of sleep and mental health disorders, and where athletes seek help. Further investigation of the timing of diagnoses and help seeking behaviours within gender diverse samples is warranted. Information about help-seeking patterns and symptom presentation can be used to tailor education to subgroups of athletes for optimal health outcomes at all stages of their careers.

Author statement

Ashley Montero: Conceptualisation, Methodology, Software, Writing- Original draft preparation, Data curation, Visualisation, Investigation. John Baranoff: Supervision, Validation, Writing-Reviewing and Editing. Robert Adams: Supervision, Validation, Writing- Reviewing and Editing. Murray Drummond: Supervision, Validation, Writing- Reviewing and Editing.

Acknowledgements

The authors would like to acknowledge the participants for volunteering their time to take part in this study. The authors would also like to acknowledge the sporting organisations who promoted and advertised this research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The Flinders University Human Research Ethics Committee approved the project (Project Number 4276). The data that support the findings of this study are available from the corresponding author, AM, upon reasonable request.