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The relationship between diet, sleep, screen time, stress
coping strategies with psychological strain and athlete
burnout in Chinese competitive swimmers: a cross-sectional
study
Zejun Yan
Shanghai Nanhui Middle School
Yezhou Guo
Shanghai University of Sport
Lei Wang
Shanghai University of Sport
Research Article
Keywords: athlete burnout, stress coping, sleep quality, adolescent athletes, psychological intervention
Posted Date: May 12th, 2025
DOI: https://doi.org/10.21203/rs.3.rs-6496771/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
Additional Declarations: No competing interests reported.
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Abstract
Background
Athlete burnout signicantly affects both athlete well-being and performance, potentially inuenced by dietary patterns,
sleep quality, screen time, and stress-coping strategies. However, the mechanistic interplay among these factors remains
unclear. This study utilized a cross-sectional design to examine the relationships between daily health behaviors (including
diet, sleep, and screen time), stress coping strategies, perceived stress and athlete burnout among Chinese competitive
swimmers.
Methods
A comprehensive questionnaire was developed, encompassing demographic information, eating behavior (BEDA), sleeping
behavior (ASSQ), screen time, stress coping strategies (CSCA), perceived psychological strain (APSQ), and athlete burnout
(ABQ). This questionnaire was administered online and distributed to participating athletes through a snowball sampling
method during the 2024 Shanghai Youth Swimming Competition to enhance the sample size.
Results
Data from 1,071 swimmers (477 females, 44.5%) revealed through Lasso regression analysis that perceived psychological
strain emerged as the strongest predictor of athlete burnout (β = 5.07), followed by age (β = 2.19) and athlete level (β =
3.76). Sleep disturbances (ASSQ) demonstrated a weaker yet signicant contribution to ABQ (β = 0.92). A temporal
inection point in age-related burnout trajectories was identied at 19 years.
Conclusion
The ndings underscore the central role of psychological strain management in preventing athlete burnout, while
highlighting the necessity to tailor psychological intervention strategies according to athletes' age and competitive level.
Introduction
Competitive swimmers face substantial pressures from both competition and training. Mental health challenges,
particularly anxiety and depression, have emerged as critical factors inuencing athletic performance and career longevity
[1]. In response to these concerns, the International Olympic Committee and national sports administrations have
implemented comprehensive mental health intervention strategies. These institutional measures, which include
psychological counseling, social support systems, and behavioral interventions, aim to optimize stress management [2].
Notably, athletes' self-developed daily stress management mechanisms may exert mitigating and protective effects against
perceived external pressures. Current research indicates signicant correlations between athletes' lifestyle patterns—such
as sleep deprivation, dietary irregularities, and social media engagement—and their psychological regulation capacities,
particularly in relation to stress-coping strategies and the development of athlete burnout [3]. However, within the cohort of
Chinese competitive swimmers, the precise mechanisms through which these factors interact remain underexplored.
Sleep serves as the neurobiological foundation for athletes' psychophysiological recovery [4], with its quality directly
regulating cortisol secretion rhythms through modulation of the hypothalamic-pituitary-adrenal (HPA) axis, thereby
inuencing mental health outcomes [5]. One investigation found that approximately 62% of elite athletes experience clinical
sleep disorders [6]. Factors such as pre-competition anxiety [7] and competing across time zones [8] are likely primary
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contributors to these sleep disturbances. In addition, sleep deprivation of less than six hours signicantly increases the risk
of athlete burnout [9].
Diet plays a fundamental role in maintaining the physical function of athletes. Effective dietary strategies can maximize
adaptive responses to fatigue, enhance muscle function, and increase exercise tolerance [10]. However, signicant dietary
behavior issues are prevalent among competitive athletes, including macronutrient intake that exceeds recommended
dietary allowance (RDA) standards [11], deciencies in breakfast consumption, insucient intake of fruits and vegetables,
and a preference for high-sugar diets [12]. Additionally, issues such as alcohol abuse [13] and extreme dieting behaviors
[14] are also common. Furthermore, caffeine abuse, along with factors such as medication-assisted sleep, can further
impair autonomic regulation in athletes [15].
Screen time has also emerged as one of the most important factors affecting athletic performance with the rise of social
media and mobile electronics. Studies have found that athletes 3–5 h of recreational screen time per day [16], and 70% use
multiple mobile devices within 1 h before bedtime [17]. Frequent social media use directly contributes to the onset of
burnout, with negative social comparisons associated with decreased achievement and exercise devaluation [18]. It is
evident that everyday health behaviours may be both an externalising issue and one of the triggers that exacerbate
psychological problems in athletes, and the interactive effects of these behaviours with stress and burnout need to be
explored in depth.
Stress is dened as the cognitive and behavioral patterns adopted by an individual when perceiving that internal or external
demands exceed their available resources [19]. Athletes' competitive stress arises from a dynamic imbalance between
environmental demands and individual capabilities, and their coping strategies, serving as a core mechanism of
psychological adjustment, directly inuence mental health trajectories. Nicholls et al. categorized athletes' stress coping
strategies into three types [20]: rst, problem-centered coping, which includes goal setting and time management [21];
second, emotion-centered coping, involving seeking social support or employing relaxation techniques; and third,
avoidance-centered coping strategies, such as denial of the problem, distraction, or wishful thinking. The ndings of this
study indicate that avoidance coping is positively correlated with exercise burnout, while problem-focused coping is either
negatively correlated or shows no correlation with exercise burnout, and emotion-focused coping is negatively correlated
with exercise burnout [22]. In terms of mechanisms of action, coping strategies primarily prevent burnout by addressing
stressors. Furthermore, positive coping strategies enhance subjective well-being [23] and mitigate the development of
burnout by reducing anxiety symptoms and psychological distress, which are further alleviated.
Athletes' perceived psychological strain is inuenced by the distinctive characteristics of competitive sports, which are
marked by high levels of competitiveness, substantial responsibility, and elevated expectations [24]. Moderate
psychological strain can enhance athletic performance [25]; however, chronic high-intensity stress has detrimental effects
on both physical and mental health [26]. The perceived psychological strain in athletes encompasses three primary
dimensions: diculties in self-regulation, performance anxiety, and external coping pressures. Previous studies have
demonstrated that perceived stress is signicantly associated with athlete injuries and well-being [27], and serves as a
precursor to burnout [28]. When athletes experience low-to-moderate perceived stress, they often strive to maintain high
professional performance. However, as coping strategies fail, individuals develop symptoms of anxiety and depression,
ultimately progressing to the exhaustion phase of stress resource depletion, which culminates in burnout [29]. The strong
correlation between the Athlete Psychological Strain and the Athlete Burnout aligns with Smith’s Cognitive-Affective Stress
Model [30]. Nevertheless, the interplay between athletes’ perceived stress and burnout across diverse cultural contexts
requires further empirical validation.
Athlete burnout, a syndrome unique to competitive sports, is characterized by three primary dimensions: (1) emotional and
physical exhaustion, which refers to the perceived depletion of psychological and physical resources due to training and
competition; (2) a reduced sense of accomplishment, which involves negative self-evaluation of athletic abilities; and (3)
sport devaluation, characterized by a cynical detachment from sports participation. This syndrome can ultimately lead to
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withdrawal from athletic activities [31]. Initially termed "sport-related mental fatigue," athlete burnout is now recognized as a
multidimensional psychopathological construct. In terms of etiology, Isoard-Gautheur et al. identied overtraining as a key
precipitating factor [32]. The Cognitive-Affective Stress Model posits that burnout emerges from dynamic interactions
between stress appraisal and affective responses, rather than from the direct effects of external stressors [33]. The three
dimensions of the Athlete Burnout Questionnaire (ABQ)—exhaustion, reduced accomplishment, and devaluation—align with
the progressive stages of burnout development: resource depletion, self-denigration, and behavioral disengagement. This
reects a sequential escalation from physiological strain to psychological withdrawal. Furthermore, athlete burnout has
been found to be associated with social support, perfectionism, mental toughness, and extrinsic motivation, which are
signicant predictors of burnout [34]. Notably, the COVID-19 pandemic has exacerbated burnout risks through both acute
psychological trauma and chronic latent effects [35].
Research on athlete burnout can signicantly contribute to the prevention and early intervention of mental health issues,
thereby mitigating their potential exacerbation. China's unique training system, characterized by centralized training and
living arrangements under the 'Chinese Whole Nation System,' may engender distinct patterns in the relationship between
daily lifestyle and burnout compared to Western contexts [36]. It is noteworthy that the prevalent issue of excessive screen
time among East Asian populations, which has been further exacerbated post-COVID-19, may create multiple pathways that
inuence the mechanisms of athlete burnout [37]. Furthermore, China's athlete ranking system is intrinsically linked to
training modalities, resulting in marked disparities in social support and training/competition intensity across different tiers
of athletes.
Building upon these considerations, this study aims to examine the relationships between daily health behaviors, stress-
coping strategies, perceived stress, and burnout among Chinese competitive swimmers. We hypothesize that: (1)
signicant gender differences exist in selected indicators of health behaviors, coping strategies, perceived stress, and
burnout; (2) certain indicators vary across athlete ranking tiers; (3) sleep quality, stress-coping strategies, and perceived
stress are strongly correlated with burnout levels; and (4) specic daily health behaviors, coping strategies, and measures
of stress perception may signicantly predict the manifestation of burnout.
Methods
Participants
The target population for this research study comprised Chinese competitive swimmers registered as serving athletes with
a Chinese sports administration unit. The research was conducted through informal channels, specically utilizing
questionnaires distributed during the 2024 Shanghai Youth Swimming Competition, along with a snowball sampling
approach to enhance participant recruitment. Participating athletes and their coaches were encouraged to share the
questionnaire link with other athletes who met the inclusion criteria. The exclusion criteria for the data included: (1) age
below 8 years or above 30 years; (2) non-serving athletes; (3) identical responses across all options; and (4) insucient
time taken to complete the questionnaire.
This study, approved by the Ethics Review Committee of Shanghai University of Sport (Approval No.: 102772022RT113),
required all participants or their legal guardians to provide online informed consent prior to completing the questionnaire.
The collected data were anonymized to ensure condentiality. The data collection period spanned from June to August
2024, during which data from 1,071 valid samples (477 females, 44.5%) were ultimately conrmed. Detailed information of
the athlete subjects are presented in Additional le 1, which shows that 319 (29.8%) were Third-Class athletes, 296 (27.6%)
were Second-Class athletes, 321 (30.0%) were First-Class athletes, 120 (11.2%) were National Master and women, and 15
(1.4%) were International Master athletes. The primary swimming events included: freestyle 508 (47.4%), backstroke 150
(14.0%), breaststroke 269 (25.1%), buttery 116 (10.8%), and individual medley 28 (2.6%).
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Measurements
In this study, we utilized the web-based questionnaire tool ‘Questionnaire Star’ (WJX.cn) to distribute an online
questionnaire, which comprised several key components. Firstly, it collected demographic information from the survey
respondents, including gender, age, sport level, and sport specialty, among other details. Additionally, we selected dietary
scales, sleep screening scales, screen time questions, psychological strain perception scales, athlete stress coping scales,
and athlete burnout scales that are appropriate for the athlete population. Detailed information regarding the questionnaire
is presented in Additional le 2. The athlete grades were referenced from the ‘Chinese Swimmer Technical Grade Standard’
[38]. Furthermore, all scales lacking Chinese versions were translated to ensure cross-cultural adaptation.
Brief Eating Disorder in Athletes Questionnaire
The Brief Eating Disorder in Athletes Questionnaire (BEDA) was developed by the International Olympic Committee (IOC)
Medical Commission Working Group. This 9-item scale evaluates eating disorders in athletes through statements such as,
"I feel very guilty after overeating," "I am obsessed with the desire to become thinner," and "I think I have too big an appetite."
Respondents rate their experiences on a 6-point Likert scale (Always, Mostly, Often, Sometimes, Rarely, and Never), with
scores ranging from 0 to 3 for each item. A total score of 4 or higher indicates the need for clinical observation [39]. The
scale demonstrated good reliability, and its internal consistency was also satisfactory when analyzed with the data from
this study(α = 0.626).
Athlete Sleep Screening Questionnaire
The Athlete Sleep Screening Questionnaire (ASSQ) was developed by Samuels et al. to facilitate the rapid screening of
athletes for sleep-related issues. This questionnaire comprises nine items, including questions such as: 'How many hours
of sleep have you actually had at night during the recent period?' 'Are you satised or dissatised with the quality of your
sleep?' and 'How long does it typically take you to fall asleep each night during the recent period?' The scoring is based on
items 1, 3, 4, 5, and 6, yielding a total score that ranges from 0 to 17. Scores are categorized as follows: 0 to 4 indicates no
sleep disorder, 5 to 7 indicates mild sleep disorder, 8 to 10 indicates moderate sleep disorder, and 11 to 17 indicates severe
sleep disorder. The ASSQ has demonstrated good internal consistency and reliability, making it the most widely utilized
sleep screening tool for athlete populations [40]. In conjunction with the data from this study, the scale exhibited good
internal consistency (α = 0.751).
Screen Time
Screen time was referenced from previous studies that investigated participants' average daily screen time usage over the
past week, with options ranging from 0 to 7 hours, including increments of 0.5 hours. Scores were assigned accordingly: 0,
0.5, 1, 2, 3, 4, 5, 6, and 7 hours.
Coping Scale for Chinese Athletes
The Coping Scale for Chinese Athletes (CSCA), developed by Zhong Boguang et al. (2004), comprises 24 items, including
statements such as ‘solving problems step by step’, ‘actively utilizing mental skills to alleviate stress’, and ‘focusing on
essential tasks’. Each item is rated on a 5-point scale, yielding a total score range of 24 to 140 points. The scale is
categorized into four dimensions: Problem-Focused Coping (PC), Emotionally-Focused Coping (EC), Avoidance Coping (AC),
and Transcendence Coping (TC). The scale was validated to have high internal consistency (Cronbach α = 0.82; Cronbach α
= 0.68–0.87 for all dimensions) in a previous study [41] and has been included in the Sport Mental Health Assessment Tool
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(SMHAT-1) package [42]. The internal consistency of the scale was good (α = 0.898) when tted with the data from this
study.
Athlete Psychological strain Questionnaire
The Athlete Psychological strain Questionnaire (APSQ) was developed by the International Olympic Committee (IOC)
Working Group on Mental Health and comprises a total of 10 items designed to assess the psychological strain
experienced by athletes. Items include statements such as "I have diculty getting along with my teammates," "I struggle to
motivate myself to complete necessary tasks," and "I feel less motivated," among others. Each item is evaluated using a 5-
point Likert scale, where scores range from 1 to 5, resulting in a total score between 10 and 50. Scores are categorized as
follows: 15 or less indicates no stress, 15–16 indicates moderate stress, 17–19 indicates high stress, and 20 or above
indicates very high stress, with a general score of ≥ 17 warranting clinical observation. The APSQ is structured into three
dimensions: diculties with self-discipline (items 1–4), performance anxiety (items 5–8), and external coping (items 9–10).
Previous studies have validated the scale, demonstrating high internal consistency (Cronbach's α = 0.82; Cronbach's α =
0.68–0.87 for each dimension) [43]. In this study, the internal consistency of the scale was excellent (α = 0.898).
Athlete Burnout Questionnaire
The Athlete Burnout Questionnaire (ABQ) scale was developed by Raedeke and Smith (2001) and consists of 15 items
distributed across three dimensions: physical/emotional exhaustion (PEE), reduced sense of accomplishment (RSA), and
sports devaluation (SD). Higher scores on each dimension and the total score indicate greater levels of athlete burnout,
with the exception of items 1 and 14, which are reverse scored. A 5-point Likert scale was employed, ranging from 0,
indicating 'never,' to 4, indicating 'always' [44]. The validity of the scale has been demonstrated to be good in a sample of
athletes from China [45]. Additionally, the internal consistency of the ABQ scale was found to be strong (α = 0.902) when
analyzed with the data from this study.
Data analysis
Missing values in the questionnaire data were supplemented with either the mean value of the variable or the value of the
nearest observation [46]. The questionnaire data were exported in Excel format and subsequently imported into the R
package for statistical analysis. All raw data are provided in Additional le 3 and 4 for verication and further analysis. (1)
Descriptive statistics: The Kolmogorov-Smirnov test was employed to characterize the distribution of the data. The basic
characteristics of the athletes were initially described according to gender and sport level. Non-normally distributed data
were analyzed for between-group variability using the Mann-Whitney U test or the Kruskal-Wallis H test, with 95%
condence intervals (CIs) provided; (2) Correlation analysis: Spearman's correlation analysis was conducted, and to control
for the overall error rate, Holm's Bonferroni step-down correction was applied to maintain the type I error rate at 0.05. The
type I error rate was also preserved at 0.05 through Bootstrap correction. The accuracy and stability of the correlation
coecients were assessed using the Bootstrap method, which included the estimation of 95% condence intervals [47]; (3)
Regression analysis: Lasso regression was utilized to compress the coecients of redundant or irrelevant variables to zero
by incorporating an L1 regularization penalty term based on Ordinary Least Squares (OLS) through the loss function, thus
achieving automatic feature selection and avoiding traditional regression methods.
The Lasso regression process involves several key steps: (1) standardizing continuous variables and creating dummy
variables for categorical variables; (2) selecting the optimal regularization parameter through 10-fold cross-validation (CV),
denoted as λ (lambda); (3) applying the λ.min criterion to identify the nal model, which retains only non-zero coecients;
and (4) constructing a multiple linear regression model based on the results of the variable selection process. The analyses
were conducted using the '
stats
', '
pacman
', '
glmnet
', and '
caret
' packages. In this study, statistical signicance is indicated
by p-values less than 0.05, denoted by an asterisk (*), and p-values less than 0.01, denoted by two asterisks (**).
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Results
Descriptive statistics
The main prole of the athletes is presented in Table1. A total of 1,071 competitive swimmers from China were included in
the study, comprising 594 male athletes (55.5%) and 477 female athletes (44.5%). Among these participants, there were 15
International Master athletes (1.4%), 120 National Master (11.2%), 321 First-Class athletes (30.0%), 296 Second-Class
athletes (27.6%), and 319 Third-Class athletes (29.8%). Signicant differences were observed in height, weight, and BMI
between male and female athletes; male athletes were older and exhibited greater height, weight, and BMI compared to
their female counterparts. Additionally, signicant variations in these three indicators were noted across different athletic
classications, with height, weight, and BMI increasing with higher sports grades and age. However, no signicant
differences were found between International Master athletes and National Master regarding tness levels.
Table 1
Demographics of athlete samples
Category(N.) Age, y Training
year, y Height, cm Weight, kg BMI, kg/m2
Total(1071) 20(15,22) 2(1,2) 178.0(169.0,185.0) 70(58,80) 22.3(20.0,24.5)
Male(594) 15(12,20) 2(1,2) 176.0(158.0,182.0) 65(45,75) 20.8(18.2,23.2)
Female(477) 14(10,19) 2(1,2) 165.0(150.0,170.0) 53(39,60) 19.5(16.9,21.0)
Z -3.8** -0.7 -11.8** -10.5** -7.4**
Int. Master(15) 20(17,23) 3(2,3) 179.0(170.0,184.0) 67.0(60.0,80.0) 21.1(20.4,23.8)
Master(120) 19(16,21) 2(2,3) 180.0(174.0,185.0) 70.9(63.0,79.8) 21.9(20.6,23.6)
First Class(321) 19(15,21) 2(2,3) 175.0(170.0,181.0) 65.0(57.0,75.0) 21.2(19.8,23.1)
Second
Class(296) 14(13,20) 2(2,2) 168.8(161.0,176.8) 57.0(49.6,67.9) 19.8(17.9,22.0)
Third Class(319) 9(8,11) 1(1,1) 141.0(135.0,154.0) 34.0(28.0,44.0) 16.4(14.9,19.3)
H 528.9** 102.3** 555.8** 476.2** 285.3**
Note
: Int. Master(International Masters): Meet the corresponding performance standards in world or Asian level
competitions; Master: Meet the corresponding performance standards in the national games; First/Second Class: Meet
the corresponding performance standards in the National Collegiate League or U level competitions; Third Class: Meet
the corresponding performance standards in the county (district) level competitions; Mann-Whitney U test was used
between genders, with Z-values indicating between-group differences; Kruskal-Wallis H test was used between exercise
levels, with H-values indicating between-group differences; *p < 0.05, **p < 0.01.
Table2 presents the primary measures of athletes, revealing that BEDA exhibits a right-skewed distribution, which aligns
with the right-skewed prevalence of eating disorders among athletes as reported by Noll et al [48]. The ASSQ, measuring
sleep quality, also demonstrates a right-skewed distribution, consistent with ndings from Samuels' study. Conversely, the
CSCA shows a left-skewed distribution, indicating that the overall coping strategies of Chinese swimmers tend to favor
positive approaches; however, the presence of extremely low values suggests that some athletes exhibit coping extremes.
The mean value of the ABQ is approximately 0, while the maximum value reaches 3.30, highlighting the existence of
individuals experiencing high levels of burnout. Furthermore, the maximum value of the APSQ is 4.22, signicantly
exceeding the mean, which indicates that certain athletes perceive stress at extreme levels. The distribution of Screen Time
is nearly symmetrical (skewness = 0.28), although extreme values (1.99) are also present. Overall, the data distribution is
appropriate for conducting correlation analysis.
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Table 2
List of characteristic values of main indicators
Variable Mean S. D. Minimum Maximum Skewness Kurtosis
BEDA 0.06 1.04 -1.31 3.77 1.07 0.87
S.T. 0.30 0.97 -1.23 1.99 0.28 -1.04
ASSQ 0.22 1.01 -1.51 4.41 0.82 0.78
APSQ 0.16 1.00 -1.24 4.22 0.50 0.17
CSCA 0.01 0.94 -3.32 1.74 -0.23 0.69
ABQ 0.17 0.95 -1.89 3.30 0.30 -0.02
Note
: BEDA: Brief ED in Athletes Questionnaire;S.T: Screen TimeASSQ: Athlete Sleep QuestionnaireAPSQ: Athlete
Psychological strain QuestionnaireCSCA: Coping Scale for Chinese AthletesABQ: Athlete Burnout Questionnaire
Table3 presents the correlation matrix for the primary measures. According to Evans' (1996) criteria, the correlations were
interpreted, revealing several strong correlations. Notably, athlete age was signicantly and positively correlated with screen
time (r = 0.61, p < 0.001), suggesting that older athletes tend to spend more time using screens. Additionally, APSQ was
signicantly and positively correlated with ABQ (r = 0.56, p < 0.001), indicating that higher self-perceived stress among
athletes is associated with higher burnout scores. The correlations for both pairs of indicators were robust. Moderately
correlated metrics included age, which was signicantly and positively correlated with years of training (r = 0.50, p < 0.001),
and age was also signicantly and positively correlated with ASSQ scores (r = 0.34, p < 0.001), suggesting that symptoms of
sleep diculties become more pronounced as athletes age. Furthermore, screen time was positively and signicantly
correlated with the ASSQ (r = 0.35, p < 0.001), indicating a potential synergistic relationship between screen time and sleep
diculties. The ASSQ also showed a signicant positive correlation with the ABQ (r = 0.39, p < 0.001), suggesting a strong
association between sleep diculties and athlete burnout symptoms. Moderate to strong correlations between the APSQ
and both the ASSQ and ABQ indicate that these variables may serve as signicant predictors of athlete burnout. Overall, age
was signicantly correlated with several variables (screen time, years of training, ASSQ, ABQ), suggesting its potential role
as a confounding variable.
The nal indicator of weak correlation was a signicant negative correlation between APSQ and CSCA (r = -0.28, p < 0.001).
This nding suggests that athletes' perceptions of stress and their coping strategies are inversely related; specically,
higher stress perceptions are associated with less effective coping strategies. Additionally, signicant negative correlations
were observed between BEDA and CSCA (r = -0.11, p < 0.001), as well as between BEDA and ABQ (r = -0.09, p < 0.001).
These correlations, although signicant, are weak, indicating that higher stress coping strategies may be associated with an
increased risk of eating disorders. Similarly, more pronounced symptoms of athlete burnout correlate with heightened
symptoms of eating disorders. However, the generally weak and insignicant correlations between BEDA and other
variables suggest that eating disorder factors may operate independently of these variables, limiting the practical guidance
provided by these correlations due to their small coecients. Furthermore, CSCA exhibited signicant negative correlations
with ASSQ, APSQ, and ABQ (r = -0.24, r = -0.28, r = -0.22, p < 0.001), indicating that effective stress coping strategies may
have a protective effect on athletes' mental health, as coping abilities can buffer mental health symptoms.
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Table 3
correlations between all indicators
1.Age 2.Trainingyear 3.BEDA 4.ST 5.ASSQ 6.APSQ 7.CSCA
2. 0.50
(0.44,0.56)**
1
3. -0.02
(-0.09,0.06)
0.03
(-0.05,0.10)
1
4. 0.61
(0.56,0.65)
**
0.35
(0.29,0.41)**
0.07
(-0.01,0.14)
1
5. 0.34
(0.28,0.41)
**
0.18
(0.11,0.25)**
0.06
(-0.01,0.14)
0.35
(0.28,0.41)**
1
6. 0.23
(0.16,0.30)
**
0.09
(0.02,0.16)**
0.20
(0.14,0.28)**
0.21
(0.14,0.28)**
0.45
(0.39,0.51)**
1
7. -0.03
(-0.09,0.05)
0.04
(-0.03,0.11)
0.11
(0.05,0.19)**
-0.01
(-0.08,0.06)
-0.24
(-0.31,-0.17)**
-0.28
(-0.35,-0.21)**
1
8. 0.351
(0.29,0.41)
**
0.23
(0.15,0.30)**
0.09
(0.02,0.16)**
0.27
(0.20,0.34)**
0.39
(0.32,0.45)**
0.56
(0.49,0.61)**
-0.22
(-0.30,-0.14)**
Note: **p < 0.01, performed correction Bootstrap = 1,000, 95% condence interval in parentheses
Regression analysis
Lasso regression analysis was selected for this study due to the multidimensional characteristics of the data, which
included variables such as the athletes' age, years of training, health sleep disorders, and mental health scales. Previous
correlation analyses indicated a moderate to strong correlation among these variables, while certain factors, such as eating
disorders, had a weak contribution to ABQ. In this study, the ABQ served as the dependent variable, and independent
variables with signicant effects were initially screened from APSQ, BEDA, ASSQ, ST, and CSCA. Control variables, including
age, gender, and athlete grade, were included in the analysis; however, training year was excluded due to its high correlation
with age. The results of the initial regression analysis are presented in Table4.
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Table 4
Screening results of the rst Lasso regression
variable coef
1 age 2.08
2 athletegrade.L 1.95
3 athletegrade.C -0.17
4 APSQ 4.94
5 ASSQ 0.83
6 S.T. 0.28
7 CSCA -0.15
Note: R²=0.3417861, RMSE = 8.958359; Optimal regularization parameter: lambda (min) = 1.92
Lasso regression was employed to identify ve core predictors for the rst time: age, athlete grade, APSQ, ASSQ, ST, and
CSCA. Regularized path diagrams and cross-validated error diagrams were presented (see Figs.1 and 2). The preliminary
screening results indicated that for every 1-unit increase in APSQ, ABQ increased by 4.93 units, suggesting that APSQ is a
stronger driver of ABQ. Additionally, ABQ increased by 2.08 units for each 1-unit increase in athlete age and by 1.94 units for
each 1-unit increase in athlete grade. These two factors were identied as the next most signicant drivers. ASSQ sleep
problems increased by 0.83 units for every 1-unit increase in APSQ and by 0.27 units for every 1-unit increase in ST;
however, these two factors had a signicant but lesser impact than APSQ. Similarly, the effect of CSCA was notable, with
ABQ decreasing by 0.14 units for every 1-unit increase in CSCA, indicating a signicant but weaker inuence. The results of
the quadratic regression model based on the initial screening of the variables are presented in Table5.
Table 5
Coecients of Lasso regression second model
Variable Estimate Std. Error t value Pr(>|t|) 95% CI
1 (Intercept) 22.58 0.65 34.90 < .01*** [21.54, 23.38]
2 age 2.19 0.42 5.24 < .01*** [1.40, 2.98]
3 athletegrade.L 3.76 1.64 2.30 0.02* [2.10, 6.54]
4 athletegrade.Q -0.84 1.30 -0.65 0.52 /
5 athletegrade.C -0.13 0.84 -0.15 0.88 /
6 APSQ 5.07 0.38 13.49 < .01*** [4.12, 5.93]
7 ASSQ 0.92 0.39 2.37 0.02* [0.14, 1.77]
8 ST 0.40 0.42 0.97 0.33 [-0.53, 1.25]
9 CSCA -0.33 0.34 -0.96 0.34 [-1.12, 0.40]
Lasso regression was used for variable selection and the regularisation path is shown in Fig.1. The optimal penalty
parameters were determined by 10-fold cross-validation, and the cross-validation error results are shown in Fig.2. The
Lasso regression prediction trends are shown in Fig.3. The nal model results show that factors with signicant predictive
power include AGE, athletegrade.L, APSQ, and ASSQ, while ST and CSCA are no longer identied as signicant independent
variables. Specically, a one-unit increase in AGE corresponds to a 2.19-unit increase in ABQ. Similarly, a one-unit increase
in athletegrade.L (representing a linear trend in athlete rank) results in a 3.76-unit increase in ABQ. Furthermore, a one-unit
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increase in APSQ leads to a 5.07-unit increase in ABQ, and a one-unit increase in ASSQ results in a 0.92-unit increase in
ABQ. Notably, athletegrade.Q (quadratic trend), athletegrade.C (cubic trend), ST (screen time), and CSCA did not emerge as
signicant independent variables. The regression model demonstrated an R² of 0.343 and an adjusted R² of 0.336,
indicating that approximately 33.6% of the variance in ABQ is explained by the model (F(8, 743) = 48.56, p < 0.001),
suggesting the model is statistically signicant. A forest plot of the regression coecients, generated through Bootstrap
(1000 iterations), is presented in Fig.4. The perception of psychological strain emerged as a central predictor of ABQ,
highlighting the value of psychological interventions in the management of ABQ. The linear trend of athlete rank
(athletegrade.L) revealed that a higher athlete rank is associated with elevated ABQ levels, while increases in age and sleep
disorders were signicantly correlated with higher ABQ levels.
Building upon previous studies, a segmented regression analysis was performed on the signicant predictor 'age' using
Lasso regression to elucidate the relationship between age and ABQ scores. The results presented in Table6 indicate a
signicant inection point (p < 0.05) in the relationship between age and ABQ at a standardized age of 0.411, corresponding
to an actual age of 19.14 years (95% CI [0.06, 0.76]). The adjusted model revealed a 6.315-point increase in ABQ scores for
each 1-unit increase in age prior to the inection point (p < 0.001), which transitioned to a 1.708-point decrease following
the inection point (p = 0.02), resulting in a change in slope of -8.023 (p < 0.01) (refer to Fig.5). Additionally, the segmented
model was validated through ANOVA, demonstrating a signicant improvement over the linear model (F = 14.56, p < 0.01).
Table 6
Age and athlete burnout piecewise regression coecients
Variable Estimate Std. Error t value Pr(>|t|) 95% CI
Inection point parameters
Standardised inection points 0.41 0.18 - < 0.05*[0.06, 0.76]
Slope parameter
Pre-inection point slope 6.32 0.73 8.66 < 0.01*** -
Slope after inection point -1.71 0.73 -2.34 0.02*-
Slope Change -8.02 1.59 -5.05 < 0.01*** -
Model Comparison
Segmented Model vs Linear Model F = 14.56 - - < 0.01*** -
Model Fit
Adjusted R² 0.14 - - - -
Residual Standard Error 10.26 - - - -
Discussion
In this study, LASSO regression with Bootstrap stability validation identied four core predictors affecting ABQ: age, athlete
grade, APSQ, and ASSQ. Among these, APSQ emerged as the strongest positive predictor of ABQ (β = 5.07, 95% CI [4.12,
5.93]), conrming the strong correlation established by previous studies [49, 50]. Furthermore, the results indicated that the
relationship between perceived stress and burnout in athletes was stronger than the relationship between other types of
stress and burnout. The level of perceived exercise stress serves as a signicant moderator of athlete burnout, either
amplifying or attenuating the effects of other variables on burnout [51]. An imbalance between personal coping resources
and perceived exercise stress can lead to athlete burnout. Athletes are more susceptible to burnout when they are
chronically exposed to stressors such as high-intensity training, competition stress, inadequate recovery, and lack of social
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support. Perceived stress can directly trigger the onset of athlete burnout and indirectly exacerbate it by simultaneously
weakening psychological resources and coping abilities. In the present study, LASSO regression and Bootstrap validation
effectively eliminated the interference of multicollinearity, thereby reinforcing the robustness of the observed relationships.
Previous studies have indicated that high APSQ scores are closely associated with dysfunction of the hypothalamic-
pituitary-adrenal (HPA) axis and abnormal cortisol secretion rhythms, which predispose individuals to emotional exhaustion
[52]. In this analysis, after controlling for age and exercise level, the APSQ continued to demonstrate a pronounced main
effect in explaining the ABQ, underscoring its centrality in the multifactorial model. This nding supports the use of the
APSQ as a key indicator for screening athlete burnout and as a target for mental toughness training interventions [53].
The present study found a signicant positive correlation between increasing age and ABQ levels (β = 2.19). Further
regression analyses revealed an inection point at around age 19, after which the correlation weakened. This nding
partially supports Gustafsson et al.'s conclusion that developmental changes in ABQ are not linear[54], suggesting that
athletes may be less inclined to leave the sport despite experiencing fatigue, frustration, and negativity—indicated by stable
burnout values. This phenomenon, characterized by 'disincentives' and 'stuckness,' may explain athletes' reluctance to exit
the sport even in the face of negative outcomes, as well as the age inection effect observed in ABQ, where athletes cease
to 'give up' upon reaching a certain age. Additionally, the study found that high-level athletes exhibited a higher risk of
burnout (β = 3.76), corroborating Reche's ndings that these athletes train more frequently and endure signicantly higher
stress levels compared to lower-level athletes[55]. High-level athletes contend with greater event intensities and public
expectations [56], alongside the depletion of physical and mental resources resulting from years of specialized training [57].
Furthermore, they face increased complexities arising from the need to balance professional, academic, and personal life
issues during their development [58]. The results of this study elucidate the positive impact of sport rank on burnout,
indicating that within the athlete development framework, special attention should be given to the cumulative mental health
challenges faced by athletes as their training duration and rank increase.
In terms of the relationship between sleep quality and athlete burnout, the present study found a relatively weak association
between ASSQ and ABQ (β = 0.92). Despite this weak correlation, the ndings hold signicant theoretical and practical
implications. Physiologically, sleep disorders may inuence athlete burnout through several mechanisms. Firstly, they may
engage the autonomic nervous system, particularly through parasympathetic activation, which promotes relaxation and
reduces fatigue [59]. Secondly, they can impact the central nervous system, alleviating anxiety and negative emotions.
Notably, athletes experiencing burnout self-reported more symptoms of insomnia, and higher levels of burnout at baseline
signicantly predicted more severe insomnia symptoms six months later [60]. Ruminative thinking and excessive focus on
sleep issues among burned-out athletes may exacerbate subjective sleep distress, creating a vicious cycle. Athletes with
high-quality sleep tend to have lower levels of tension, and sleep indirectly affects athletic performance by regulating mood
[61]. Conversely, poor sleep quality directly contributes to increased negative emotions and can exacerbate detrimental
habits, such as media addiction [62]. The low explanatory power of the ASSQ for ABQ in this study may stem from two
factors: rstly, the ASSQ as a sleep assessment tool may not fully capture the complexities of athletes' sleep issues;
secondly, some athletes may have temporarily alleviated their sleep symptoms through articial means, such as
medication, which masks the true severity of the problem. Future research should incorporate more objective monitoring
tools, such as brainwave analysis or salivary cortisol testing, to enhance the objectivity of sleep-related inuences on
athlete burnout. Additionally, sleep-related health education interventions should be integral components of psychological
support programs for athletes.
Although screen time (ST) and stress coping strategies (CSCA) did not reach statistical signicance in the nal model,
these variables exhibited some predictive value and warrant further exploration. One possible explanation is that athletes'
general stress coping abilities may not effectively translate to coping with sport-specic stress, which has a limited impact
on alleviating athlete burnout [63]. Additionally, the sample was predominantly composed of adolescent athletes, who may
possess limited social and psychological resources to manage stress, potentially explaining the non-signicant results
related to stress coping strategies. The signicant negative correlation between the stress coping strategy CSCA and ABQ
Page 13/22
(r = -0.22, p < 0.01) underscores the importance of psychological adjustment skills. Conversely, the weak correlation
between screen time and ABQ (r = 0.27, p < 0.01) may reect the dual nature of this indicator's inuence on athletes' mental
health [64]. These 'borderline signicant' ndings suggest that factors such as media management and coping skills
training should be considered when developing a comprehensive intervention program. Furthermore, future research could
further validate these relationships using more rened measures and larger samples.
Limitation
The limitations of the present study include challenges in establishing causal inference due to its cross-sectional design.
Future studies should consider adopting a longitudinal tracking approach, complemented by multi-centre sampling and the
incorporation of more objective physiological measures. This would enhance our understanding of the mechanisms
underlying athlete burnout. Particularly in the post-COVID-19 era, it is crucial to investigate the long-term effects of the
'training interruption-recovery' model on athletes' mental health. Such in-depth explorations will provide a signicant
scientic basis for developing a more effective mental health support system for athletes. Additionally, it is essential to
examine whether there exists a mediating pathway between the effects of stress coping strategies and screen time on
athlete burnout.
Conclusion
The present study revealed that perceived psychological strain (APSQ) is the strongest predictor of athlete burnout (ABQ)
among Chinese swimmers (β = 5.07), thereby validating the central role of psychological strain management in preventing
athlete burnout. Additionally, increasing age (β = 2.19) and elevated levels of exercise (β = 3.76) signicantly exacerbate the
risk of athlete burnout. Although the signicant effect of sleep disorders (ASSQ) was noted, it contributed weakly (β = 0.92),
underscoring the necessity of including sleep quality improvement in a comprehensive intervention strategy. The
phenomenon of aging at a temporal inection point in the development of athlete burnout elucidates the developmental
characteristics of burnout at various career stages, highlighting the need for targeted interventions. While screen time and
stress coping strategies (CSCA) did not pass the nal model screening, their potential impact warrants further exploration
in subsequent studies. The ndings provide a pathway for mental health management in competitive swimmers, focusing
on stress regulation, hierarchical management, and synergistic sleep-stress interventions. Future longitudinal studies are
essential to validate the causal pathways and integrate objective measures to deepen the analysis of underlying
mechanisms.
Declarations
Acknowledgements
Not applicable.
Authors’ contributions
Lei Wang, Yezhou Guo, and Zejun Yan contributed to data collection, analysis, and interpretation. Lei Wang, Yezhou Guo, and
Zejun Yan contributed to the writing of the manuscript. Lei Wang, Yezhou Guo contributed to the critical revision of the
manuscript. All authors read and approved the manuscript and have given consent for the submission of the nal article.
Funding
No funding was received for this project.
Data availability
All data supporting the ndings of this study are available within the paper and its Supplementary Information les.
Page 14/22
Ethics approval and consent to participate
This study, approved by the Ethics Review Committee of Shanghai University of Sport (Approval No.: 102772022RT113),
required all participants or their legal guardians to provide online informed consent prior to completing the questionnaire.
The collected data were anonymized to ensure condentiality.All procedures were performed in accordance with the
ethical standards of the Declaration of Helsinki.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
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Figures
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Figure 1
Regularized path diagram
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Figure 2
Cross validation error
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Figure 3
Lasso forecast trend Figure
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Figure 4
Lasso regression coecient forest plot
