The relationship between diet, sleep, screen time, stress coping strategies with psychological strain and athlete burnout in Chinese competitive swimmers: a cross-sectional study

The relationship between diet, sleep, screen time, stress coping strategies with psychological strain and athlete burnout in Chinese competitive swimmers: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article 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in Chinese competitive swimmers: a cross-sectional study Zejun Yan, Yezhou Guo, Lei Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6496771/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 May, 2026 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted 11 You are reading this latest preprint version Abstract Background Athlete burnout significantly affects both athlete well-being and performance, potentially influenced 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 significant contribution to ABQ (β = 0.92). A temporal inflection point in age-related burnout trajectories was identified at 19 years. Conclusion The findings 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. athlete burnout stress coping sleep quality adolescent athletes psychological intervention Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Competitive swimmers face substantial pressures from both competition and training. Mental health challenges, particularly anxiety and depression, have emerged as critical factors influencing 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 significant 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 influencing 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 contributors to these sleep disturbances. In addition, sleep deprivation of less than six hours significantly 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, significant dietary behavior issues are prevalent among competitive athletes, including macronutrient intake that exceeds recommended dietary allowance (RDA) standards [ 11 ], deficiencies in breakfast consumption, insufficient 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 defined 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 influence mental health trajectories. Nicholls et al. categorized athletes' stress coping strategies into three types [ 20 ]: first, 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 findings 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 influenced 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: difficulties in self-regulation, performance anxiety, and external coping pressures. Previous studies have demonstrated that perceived stress is significantly 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 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. identified 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 reflects 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 significant 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 significantly 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 influence 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) significant 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) specific daily health behaviors, coping strategies, and measures of stress perception may significantly 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, specifically 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) insufficient 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 confidentiality. The data collection period spanned from June to August 2024, during which data from 1,071 valid samples (477 females, 44.5%) were ultimately confirmed. Detailed information of the athlete subjects are presented in Additional file 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%), butterfly 116 (10.8%), and individual medley 28 (2.6%). 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 file 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 satisfied or dissatisfied 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 (SMHAT-1) package [ 42 ]. The internal consistency of the scale was good (α = 0.898) when fitted 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 difficulty 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: difficulties 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 file 3 and 4 for verification 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% confidence 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 coefficients were assessed using the Bootstrap method, which included the estimation of 95% confidence intervals [ 47 ]; (3) Regression analysis: Lasso regression was utilized to compress the coefficients 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 final model, which retains only non-zero coefficients; 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 significance is indicated by p-values less than 0.05, denoted by an asterisk (*), and p-values less than 0.01, denoted by two asterisks (**). Results Descriptive statistics The main profile of the athletes is presented in Table 1 . 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%). Significant 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, significant variations in these three indicators were noted across different athletic classifications, with height, weight, and BMI increasing with higher sports grades and age. However, no significant differences were found between International Master athletes and National Master regarding fitness levels. Table 1 Demographics of athlete samples Category(N.) Age, y Training year, y Height, cm Weight, kg BMI, kg/m 2 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. Table 2 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 findings 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, significantly 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. 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 Time༛ASSQ: Athlete Sleep Questionnaire༛APSQ: Athlete Psychological strain Questionnaire༛CSCA: Coping Scale for Chinese Athletes༛ABQ: Athlete Burnout Questionnaire Table 3 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 significantly 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 significantly 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 significantly and positively correlated with years of training (r = 0.50, p < 0.001), and age was also significantly and positively correlated with ASSQ scores (r = 0.34, p < 0.001), suggesting that symptoms of sleep difficulties become more pronounced as athletes age. Furthermore, screen time was positively and significantly correlated with the ASSQ (r = 0.35, p < 0.001), indicating a potential synergistic relationship between screen time and sleep difficulties. The ASSQ also showed a significant positive correlation with the ABQ (r = 0.39, p < 0.001), suggesting a strong association between sleep difficulties and athlete burnout symptoms. Moderate to strong correlations between the APSQ and both the ASSQ and ABQ indicate that these variables may serve as significant predictors of athlete burnout. Overall, age was significantly correlated with several variables (screen time, years of training, ASSQ, ABQ), suggesting its potential role as a confounding variable. The final indicator of weak correlation was a significant negative correlation between APSQ and CSCA (r = -0.28, p < 0.001). This finding suggests that athletes' perceptions of stress and their coping strategies are inversely related; specifically, higher stress perceptions are associated with less effective coping strategies. Additionally, significant 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 significant, 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 insignificant 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 coefficients. Furthermore, CSCA exhibited significant 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. 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% confidence 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 significant 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 Table 4 . Table 4 Screening results of the first 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 five core predictors for the first 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 identified as the next most significant 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 significant 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 significant but weaker influence. The results of the quadratic regression model based on the initial screening of the variables are presented in Table 5 . Table 5 Coefficients 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 final model results show that factors with significant predictive power include AGE, athletegrade.L, APSQ, and ASSQ, while ST and CSCA are no longer identified as significant independent variables. Specifically, 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 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 significant 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 significant. A forest plot of the regression coefficients, 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 significantly correlated with higher ABQ levels. Building upon previous studies, a segmented regression analysis was performed on the significant predictor 'age' using Lasso regression to elucidate the relationship between age and ABQ scores. The results presented in Table 6 indicate a significant inflection 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 inflection point (p < 0.001), which transitioned to a 1.708-point decrease following the inflection 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 significant improvement over the linear model (F = 14.56, p |t|) 95% CI Inflection point parameters Standardised inflection points 0.41 0.18 - < 0.05 * [0.06, 0.76] Slope parameter Pre-inflection point slope 6.32 0.73 8.66 < 0.01 *** - Slope after inflection 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 identified 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]), confirming 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 significant 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 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 finding 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 significant positive correlation between increasing age and ABQ levels (β = 2.19). Further regression analyses revealed an inflection point at around age 19, after which the correlation weakened. This finding 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 inflection 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 findings that these athletes train more frequently and endure significantly 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 findings hold significant theoretical and practical implications. Physiologically, sleep disorders may influence 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 significantly 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: firstly, 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 artificial 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 influences 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 significance in the final 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-specific 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-significant results related to stress coping strategies. The significant negative correlation between the stress coping strategy CSCA and ABQ (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 reflect the dual nature of this indicator's influence on athletes' mental health [ 64 ]. These 'borderline significant' findings 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 refined 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 significant scientific 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) significantly exacerbate the risk of athlete burnout. Although the significant 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 inflection 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 final model screening, their potential impact warrants further exploration in subsequent studies. The findings 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 final article. Funding No funding was received for this project. Data availability All data supporting the findings of this study are available within the paper and its Supplementary Information files. 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 confidentiality. 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. References Walsh NP, Halson SL, Sargent C, Roach GD, Nédélec M, Gupta L, et al. Sleep and the athlete: narrative review and 2021 expert consensus recommendations. Br J Sports Med. 2021;55:356–68. Reardon CL, Hainline B, Aron CM, Baron D, Baum AL, Bindra A, et al. 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Supplementary Files Detailedinformationoftheathletesubjects.docx Rcodefordataprocessingandanalysis.r Questionnaire.docx Rawdata.xlsx Cite Share Download PDF Status: Published Journal Publication published 01 May, 2026 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted Editorial decision: Revision requested 30 Sep, 2025 Reviews received at journal 27 Sep, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviewers agreed at journal 21 May, 2025 Reviews received at journal 16 May, 2025 Reviewers agreed at journal 15 May, 2025 Reviewers invited by journal 07 May, 2025 Editor assigned by journal 29 Apr, 2025 Editor invited by journal 28 Apr, 2025 Submission checks completed at journal 27 Apr, 2025 First submitted to journal 27 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6496771","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453350997,"identity":"f38f8a7b-281a-4535-b33b-1379db449345","order_by":0,"name":"Zejun Yan","email":"","orcid":"","institution":"Shanghai Nanhui Middle School","correspondingAuthor":false,"prefix":"","firstName":"Zejun","middleName":"","lastName":"Yan","suffix":""},{"id":453350998,"identity":"a5eeac37-b77c-47f7-b50a-35c20d2a6918","order_by":1,"name":"Yezhou 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4","display":"","copyAsset":false,"role":"figure","size":106854,"visible":true,"origin":"","legend":"\u003cp\u003eLasso regression coefficient forest plot\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6496771/v1/a0383b91d1c0be81163cdba9.png"},{"id":82549222,"identity":"f5ca6427-ab79-483e-af13-d34462f11003","added_by":"auto","created_at":"2025-05-12 19:28:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":118016,"visible":true,"origin":"","legend":"\u003cp\u003eThe segmented regression relationship between age and ABQ\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6496771/v1/376f6e2304bce418bf7bd45f.png"},{"id":108437825,"identity":"303a0850-0443-4f56-bbde-5021149d660d","added_by":"auto","created_at":"2026-05-04 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19:36:26","extension":"r","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6494,"visible":true,"origin":"","legend":"","description":"","filename":"Rcodefordataprocessingandanalysis.r","url":"https://assets-eu.researchsquare.com/files/rs-6496771/v1/89761e98b942d073ac513583.r"},{"id":82549207,"identity":"f1118ee6-98ed-405f-ac8f-1bf9e2cb0e83","added_by":"auto","created_at":"2025-05-12 19:28:26","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":26209,"visible":true,"origin":"","legend":"","description":"","filename":"Questionnaire.docx","url":"https://assets-eu.researchsquare.com/files/rs-6496771/v1/e76b7a63da8760928826854e.docx"},{"id":82549218,"identity":"ee0f9205-ce9e-4050-ba98-9d41e8b9a1b6","added_by":"auto","created_at":"2025-05-12 19:28:26","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":142640,"visible":true,"origin":"","legend":"","description":"","filename":"Rawdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6496771/v1/c6f4e6a07d028b445d60f65a.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The relationship between diet, sleep, screen time, stress coping strategies with psychological strain and athlete burnout in Chinese competitive swimmers: a cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCompetitive swimmers face substantial pressures from both competition and training. Mental health challenges, particularly anxiety and depression, have emerged as critical factors influencing athletic performance and career longevity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Notably, athletes' self-developed daily stress management mechanisms may exert mitigating and protective effects against perceived external pressures. Current research indicates significant correlations between athletes' lifestyle patterns\u0026mdash;such as sleep deprivation, dietary irregularities, and social media engagement\u0026mdash;and their psychological regulation capacities, particularly in relation to stress-coping strategies and the development of athlete burnout [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, within the cohort of Chinese competitive swimmers, the precise mechanisms through which these factors interact remain underexplored.\u003c/p\u003e \u003cp\u003eSleep serves as the neurobiological foundation for athletes' psychophysiological recovery [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], with its quality directly regulating cortisol secretion rhythms through modulation of the hypothalamic-pituitary-adrenal (HPA) axis, thereby influencing mental health outcomes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. One investigation found that approximately 62% of elite athletes experience clinical sleep disorders [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Factors such as pre-competition anxiety [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and competing across time zones [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] are likely primary contributors to these sleep disturbances. In addition, sleep deprivation of less than six hours significantly increases the risk of athlete burnout [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiet 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 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, significant dietary behavior issues are prevalent among competitive athletes, including macronutrient intake that exceeds recommended dietary allowance (RDA) standards [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], deficiencies in breakfast consumption, insufficient intake of fruits and vegetables, and a preference for high-sugar diets [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, issues such as alcohol abuse [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and extreme dieting behaviors [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] are also common. Furthermore, caffeine abuse, along with factors such as medication-assisted sleep, can further impair autonomic regulation in athletes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eScreen 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\u0026ndash;5 h of recreational screen time per day [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and 70% use multiple mobile devices within 1 h before bedtime [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Frequent social media use directly contributes to the onset of burnout, with negative social comparisons associated with decreased achievement and exercise devaluation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eStress is defined as the cognitive and behavioral patterns adopted by an individual when perceiving that internal or external demands exceed their available resources [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. 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 influence mental health trajectories. Nicholls et al. categorized athletes' stress coping strategies into three types [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]: first, problem-centered coping, which includes goal setting and time management [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]; 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 findings 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 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In terms of mechanisms of action, coping strategies primarily prevent burnout by addressing stressors. Furthermore, positive coping strategies enhance subjective well-being [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and mitigate the development of burnout by reducing anxiety symptoms and psychological distress, which are further alleviated.\u003c/p\u003e \u003cp\u003eAthletes' perceived psychological strain is influenced by the distinctive characteristics of competitive sports, which are marked by high levels of competitiveness, substantial responsibility, and elevated expectations [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Moderate psychological strain can enhance athletic performance [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]; however, chronic high-intensity stress has detrimental effects on both physical and mental health [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The perceived psychological strain in athletes encompasses three primary dimensions: difficulties in self-regulation, performance anxiety, and external coping pressures. Previous studies have demonstrated that perceived stress is significantly associated with athlete injuries and well-being [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and serves as a precursor to burnout [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The strong correlation between the Athlete Psychological Strain and the Athlete Burnout aligns with Smith\u0026rsquo;s Cognitive-Affective Stress Model [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Nevertheless, the interplay between athletes\u0026rsquo; perceived stress and burnout across diverse cultural contexts requires further empirical validation.\u003c/p\u003e \u003cp\u003eAthlete 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 withdrawal from athletic activities [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Initially termed \"sport-related mental fatigue,\" athlete burnout is now recognized as a multidimensional psychopathological construct. In terms of etiology, Isoard-Gautheur et al. identified overtraining as a key precipitating factor [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The three dimensions of the Athlete Burnout Questionnaire (ABQ)\u0026mdash;exhaustion, reduced accomplishment, and devaluation\u0026mdash;align with the progressive stages of burnout development: resource depletion, self-denigration, and behavioral disengagement. This reflects 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 significant predictors of burnout [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Notably, the COVID-19 pandemic has exacerbated burnout risks through both acute psychological trauma and chronic latent effects [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearch on athlete burnout can significantly 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 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. 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 influence the mechanisms of athlete burnout [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eBuilding 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) significant 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) specific daily health behaviors, coping strategies, and measures of stress perception may significantly predict the manifestation of burnout.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe 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, specifically 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) insufficient time taken to complete the questionnaire.\u003c/p\u003e \u003cp\u003eThis 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 confidentiality. The data collection period spanned from June to August 2024, during which data from 1,071 valid samples (477 females, 44.5%) were ultimately confirmed. Detailed information of the athlete subjects are presented in Additional file 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%), butterfly 116 (10.8%), and individual medley 28 (2.6%).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurements\u003c/h3\u003e\n\u003cp\u003eIn this study, we utilized the web-based questionnaire tool \u0026lsquo;Questionnaire Star\u0026rsquo; (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 file 2. The athlete grades were referenced from the \u0026lsquo;Chinese Swimmer Technical Grade Standard\u0026rsquo; [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Furthermore, all scales lacking Chinese versions were translated to ensure cross-cultural adaptation.\u003c/p\u003e\n\u003ch3\u003eBrief Eating Disorder in Athletes Questionnaire\u003c/h3\u003e\n\u003cp\u003e 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 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The scale demonstrated good reliability, and its internal consistency was also satisfactory when analyzed with the data from this study(α\u0026thinsp;=\u0026thinsp;0.626).\u003c/p\u003e\n\u003ch3\u003eAthlete Sleep Screening Questionnaire\u003c/h3\u003e\n\u003cp\u003eThe 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 satisfied or dissatisfied 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 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In conjunction with the data from this study, the scale exhibited good internal consistency (α\u0026thinsp;=\u0026thinsp;0.751).\u003c/p\u003e\n\u003ch3\u003eScreen Time\u003c/h3\u003e\n\u003cp\u003eScreen 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.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCoping Scale for Chinese Athletes\u003c/h2\u003e \u003cp\u003eThe Coping Scale for Chinese Athletes (CSCA), developed by Zhong Boguang et al. (2004), comprises 24 items, including statements such as \u0026lsquo;solving problems step by step\u0026rsquo;, \u0026lsquo;actively utilizing mental skills to alleviate stress\u0026rsquo;, and \u0026lsquo;focusing on essential tasks\u0026rsquo;. 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\u0026thinsp;=\u0026thinsp;0.82; Cronbach α\u0026thinsp;=\u0026thinsp;0.68\u0026ndash;0.87 for all dimensions) in a previous study [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and has been included in the Sport Mental Health Assessment Tool (SMHAT-1) package [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The internal consistency of the scale was good (α\u0026thinsp;=\u0026thinsp;0.898) when fitted with the data from this study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAthlete Psychological strain Questionnaire\u003c/h3\u003e\n\u003cp\u003eThe 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 difficulty 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\u0026ndash;16 indicates moderate stress, 17\u0026ndash;19 indicates high stress, and 20 or above indicates very high stress, with a general score of \u0026ge;\u0026thinsp;17 warranting clinical observation. The APSQ is structured into three dimensions: difficulties with self-discipline (items 1\u0026ndash;4), performance anxiety (items 5\u0026ndash;8), and external coping (items 9\u0026ndash;10). Previous studies have validated the scale, demonstrating high internal consistency (Cronbach's α\u0026thinsp;=\u0026thinsp;0.82; Cronbach's α\u0026thinsp;=\u0026thinsp;0.68\u0026ndash;0.87 for each dimension) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In this study, the internal consistency of the scale was excellent (α\u0026thinsp;=\u0026thinsp;0.898).\u003c/p\u003e\n\u003ch3\u003eAthlete Burnout Questionnaire\u003c/h3\u003e\n\u003cp\u003eThe 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' [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The validity of the scale has been demonstrated to be good in a sample of athletes from China [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Additionally, the internal consistency of the ABQ scale was found to be strong (α\u0026thinsp;=\u0026thinsp;0.902) when analyzed with the data from this study.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eMissing values in the questionnaire data were supplemented with either the mean value of the variable or the value of the nearest observation [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. 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 file 3 and 4 for verification 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% confidence 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 coefficients were assessed using the Bootstrap method, which included the estimation of 95% confidence intervals [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]; (3) Regression analysis: Lasso regression was utilized to compress the coefficients 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.\u003c/p\u003e \u003cp\u003eThe 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 final model, which retains only non-zero coefficients; and (4) constructing a multiple linear regression model based on the results of the variable selection process. The analyses were conducted using the '\u003cem\u003estats\u003c/em\u003e', '\u003cem\u003epacman\u003c/em\u003e', '\u003cem\u003eglmnet\u003c/em\u003e', and '\u003cem\u003ecaret\u003c/em\u003e' packages. In this study, statistical significance is indicated by p-values less than 0.05, denoted by an asterisk (*), and p-values less than 0.01, denoted by two asterisks (**).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eThe main profile of the athletes is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. 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%). Significant 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, significant variations in these three indicators were noted across different athletic classifications, with height, weight, and BMI increasing with higher sports grades and age. However, no significant differences were found between International Master athletes and National Master regarding fitness levels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics of athlete samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory(N.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge, y\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining year, y\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeight, cm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeight, kg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal(1071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(15,22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(1,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e178.0(169.0,185.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70(58,80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.3(20.0,24.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale(594)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(12,20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(1,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e176.0(158.0,182.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65(45,75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.8(18.2,23.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale(477)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(10,19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(1,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e165.0(150.0,170.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53(39,60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.5(16.9,21.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.8**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-11.8**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.5**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-7.4**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInt. Master(15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(17,23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(2,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e179.0(170.0,184.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.0(60.0,80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.1(20.4,23.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster(120)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(16,21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(2,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180.0(174.0,185.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.9(63.0,79.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.9(20.6,23.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst Class(321)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(15,21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(2,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e175.0(170.0,181.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.0(57.0,75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.2(19.8,23.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond Class(296)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(13,20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(2,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e168.8(161.0,176.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.0(49.6,67.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.8(17.9,22.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThird Class(319)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(8,11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e141.0(135.0,154.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.0(28.0,44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.4(14.9,19.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e528.9**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102.3**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e555.8**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e476.2**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e285.3**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote\u003c/em\u003e: 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\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e 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 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The ASSQ, measuring sleep quality, also demonstrates a right-skewed distribution, consistent with findings 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, significantly exceeding the mean, which indicates that certain athletes perceive stress at extreme levels. The distribution of Screen Time is nearly symmetrical (skewness\u0026thinsp;=\u0026thinsp;0.28), although extreme values (1.99) are also present. Overall, the data distribution is appropriate for conducting correlation analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of characteristic values of main indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS. D.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBEDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.T.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote\u003c/em\u003e: BEDA: Brief ED in Athletes Questionnaire;S.T: Screen Time༛ASSQ: Athlete Sleep Questionnaire༛APSQ: Athlete Psychological strain Questionnaire༛CSCA: Coping Scale for Chinese Athletes༛ABQ: Athlete Burnout Questionnaire\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e 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 significantly and positively correlated with screen time (r\u0026thinsp;=\u0026thinsp;0.61, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that older athletes tend to spend more time using screens. Additionally, APSQ was significantly and positively correlated with ABQ (r\u0026thinsp;=\u0026thinsp;0.56, p\u0026thinsp;\u0026lt;\u0026thinsp;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 significantly and positively correlated with years of training (r\u0026thinsp;=\u0026thinsp;0.50, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and age was also significantly and positively correlated with ASSQ scores (r\u0026thinsp;=\u0026thinsp;0.34, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that symptoms of sleep difficulties become more pronounced as athletes age. Furthermore, screen time was positively and significantly correlated with the ASSQ (r\u0026thinsp;=\u0026thinsp;0.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a potential synergistic relationship between screen time and sleep difficulties. The ASSQ also showed a significant positive correlation with the ABQ (r\u0026thinsp;=\u0026thinsp;0.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting a strong association between sleep difficulties and athlete burnout symptoms. Moderate to strong correlations between the APSQ and both the ASSQ and ABQ indicate that these variables may serve as significant predictors of athlete burnout. Overall, age was significantly correlated with several variables (screen time, years of training, ASSQ, ABQ), suggesting its potential role as a confounding variable.\u003c/p\u003e \u003cp\u003eThe final indicator of weak correlation was a significant negative correlation between APSQ and CSCA (r = -0.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding suggests that athletes' perceptions of stress and their coping strategies are inversely related; specifically, higher stress perceptions are associated with less effective coping strategies. Additionally, significant negative correlations were observed between BEDA and CSCA (r = -0.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as between BEDA and ABQ (r = -0.09, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These correlations, although significant, 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 insignificant 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 coefficients. Furthermore, CSCA exhibited significant negative correlations with ASSQ, APSQ, and ABQ (r = -0.24, r = -0.28, r = -0.22, p\u0026thinsp;\u0026lt;\u0026thinsp;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.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ecorrelations between all indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.Age\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.Trainingyear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.BEDA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.ST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.ASSQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.APSQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.CSCA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003cp\u003e(0.44,0.56)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003cp\u003e(-0.09,0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003cp\u003e(-0.05,0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003cp\u003e(0.56,0.65) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003cp\u003e(0.29,0.41)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003cp\u003e(-0.01,0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003cp\u003e(0.28,0.41) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003cp\u003e(0.11,0.25)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003cp\u003e(-0.01,0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003cp\u003e(0.28,0.41)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003cp\u003e(0.16,0.30) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003cp\u003e(0.02,0.16)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003cp\u003e(0.14,0.28)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003cp\u003e(0.14,0.28)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003cp\u003e(0.39,0.51)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003cp\u003e(-0.09,0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003cp\u003e(-0.03,0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003cp\u003e(0.05,0.19)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003cp\u003e(-0.08,0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003cp\u003e(-0.31,-0.17)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003cp\u003e(-0.35,-0.21)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003cp\u003e(0.29,0.41) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003cp\u003e(0.15,0.30)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003cp\u003e(0.02,0.16)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003cp\u003e(0.20,0.34)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003cp\u003e(0.32,0.45)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003cp\u003e(0.49,0.61)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003cp\u003e(-0.30,-0.14)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, performed correction Bootstrap\u0026thinsp;=\u0026thinsp;1,000, 95% confidence interval in parentheses\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRegression analysis\u003c/h2\u003e \u003cp\u003eLasso 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 significant 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 Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eScreening results of the first Lasso regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecoef\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eathletegrade.L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eathletegrade.C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAPSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS.T.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: R\u0026sup2;=0.3417861, RMSE\u0026thinsp;=\u0026thinsp;8.958359; Optimal regularization parameter: lambda (min)\u0026thinsp;=\u0026thinsp;1.92\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLasso regression was employed to identify five core predictors for the first time: age, athlete grade, APSQ, ASSQ, ST, and CSCA. Regularized path diagrams and cross-validated error diagrams were presented (see Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). 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 identified as the next most significant 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 significant 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 significant but weaker influence. The results of the quadratic regression model based on the initial screening of the variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoefficients of Lasso regression second model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePr(\u0026gt;|t|)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[21.54, 23.38]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.40, 2.98]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eathletegrade.L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[2.10, 6.54]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eathletegrade.Q\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eathletegrade.C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAPSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[4.12, 5.93]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.14, 1.77]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-0.53, 1.25]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-1.12, 0.40]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLasso regression was used for variable selection and the regularisation path is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The optimal penalty parameters were determined by 10-fold cross-validation, and the cross-validation error results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The Lasso regression prediction trends are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The final model results show that factors with significant predictive power include AGE, athletegrade.L, APSQ, and ASSQ, while ST and CSCA are no longer identified as significant independent variables. Specifically, 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 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 significant independent variables. The regression model demonstrated an R\u0026sup2; of 0.343 and an adjusted R\u0026sup2; of 0.336, indicating that approximately 33.6% of the variance in ABQ is explained by the model (F(8, 743)\u0026thinsp;=\u0026thinsp;48.56, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting the model is statistically significant. A forest plot of the regression coefficients, generated through Bootstrap (1000 iterations), is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. 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 significantly correlated with higher ABQ levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBuilding upon previous studies, a segmented regression analysis was performed on the significant predictor 'age' using Lasso regression to elucidate the relationship between age and ABQ scores. The results presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e indicate a significant inflection point (p\u0026thinsp;\u0026lt;\u0026thinsp;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 inflection point (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which transitioned to a 1.708-point decrease following the inflection point (p\u0026thinsp;=\u0026thinsp;0.02), resulting in a change in slope of -8.023 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Additionally, the segmented model was validated through ANOVA, demonstrating a significant improvement over the linear model (F\u0026thinsp;=\u0026thinsp;14.56, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAge and athlete burnout piecewise regression coefficients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePr(\u0026gt;|t|)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point parameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandardised inflection points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[0.06, 0.76]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSlope parameter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-inflection point slope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope after inflection point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel Comparison\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmented Model vs Linear Model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u0026thinsp;=\u0026thinsp;14.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel Fit\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual Standard Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, LASSO regression with Bootstrap stability validation identified four core predictors affecting ABQ: age, athlete grade, APSQ, and ASSQ. Among these, APSQ emerged as the strongest positive predictor of ABQ (β\u0026thinsp;=\u0026thinsp;5.07, 95% CI [4.12, 5.93]), confirming the strong correlation established by previous studies [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. 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 significant moderator of athlete burnout, either amplifying or attenuating the effects of other variables on burnout [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. 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 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 [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. 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 finding supports the use of the APSQ as a key indicator for screening athlete burnout and as a target for mental toughness training interventions [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study found a significant positive correlation between increasing age and ABQ levels (β\u0026thinsp;=\u0026thinsp;2.19). Further regression analyses revealed an inflection point at around age 19, after which the correlation weakened. This finding partially supports Gustafsson et al.'s conclusion that developmental changes in ABQ are not linear[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], suggesting that athletes may be less inclined to leave the sport despite experiencing fatigue, frustration, and negativity\u0026mdash;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 inflection 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\u0026thinsp;=\u0026thinsp;3.76), corroborating Reche's findings that these athletes train more frequently and endure significantly higher stress levels compared to lower-level athletes[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. High-level athletes contend with greater event intensities and public expectations [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], alongside the depletion of physical and mental resources resulting from years of specialized training [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Furthermore, they face increased complexities arising from the need to balance professional, academic, and personal life issues during their development [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eIn terms of the relationship between sleep quality and athlete burnout, the present study found a relatively weak association between ASSQ and ABQ (β\u0026thinsp;=\u0026thinsp;0.92). Despite this weak correlation, the findings hold significant theoretical and practical implications. Physiologically, sleep disorders may influence athlete burnout through several mechanisms. Firstly, they may engage the autonomic nervous system, particularly through parasympathetic activation, which promotes relaxation and reduces fatigue [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. 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 significantly predicted more severe insomnia symptoms six months later [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. 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 [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Conversely, poor sleep quality directly contributes to increased negative emotions and can exacerbate detrimental habits, such as media addiction [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The low explanatory power of the ASSQ for ABQ in this study may stem from two factors: firstly, 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 artificial 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 influences on athlete burnout. Additionally, sleep-related health education interventions should be integral components of psychological support programs for athletes.\u003c/p\u003e \u003cp\u003eAlthough screen time (ST) and stress coping strategies (CSCA) did not reach statistical significance in the final 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-specific stress, which has a limited impact on alleviating athlete burnout [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Additionally, the sample was predominantly composed of adolescent athletes, who may possess limited social and psychological resources to manage stress, potentially explaining the non-significant results related to stress coping strategies. The significant negative correlation between the stress coping strategy CSCA and ABQ (r = -0.22, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) underscores the importance of psychological adjustment skills. Conversely, the weak correlation between screen time and ABQ (r\u0026thinsp;=\u0026thinsp;0.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) may reflect the dual nature of this indicator's influence on athletes' mental health [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. These 'borderline significant' findings 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 refined measures and larger samples.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitation\u003c/h2\u003e \u003cp\u003eThe 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 significant scientific 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.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study revealed that perceived psychological strain (APSQ) is the strongest predictor of athlete burnout (ABQ) among Chinese swimmers (β\u0026thinsp;=\u0026thinsp;5.07), thereby validating the central role of psychological strain management in preventing athlete burnout. Additionally, increasing age (β\u0026thinsp;=\u0026thinsp;2.19) and elevated levels of exercise (β\u0026thinsp;=\u0026thinsp;3.76) significantly exacerbate the risk of athlete burnout. Although the significant effect of sleep disorders (ASSQ) was noted, it contributed weakly (β\u0026thinsp;=\u0026thinsp;0.92), underscoring the necessity of including sleep quality improvement in a comprehensive intervention strategy. The phenomenon of aging at a temporal inflection 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 final model screening, their potential impact warrants further exploration in subsequent studies. The findings 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eLei 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 final article.\u003c/p\u003e\n\u003cp\u003eFunding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo funding was received for this project.\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are available within the paper and its Supplementary Information files.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis 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 confidentiality.\u0026nbsp;All procedures were performed in accordance with the ethical standards of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWalsh NP, Halson SL, Sargent C, Roach GD, N\u0026eacute;d\u0026eacute;lec M, Gupta L, et al. Sleep and the athlete: narrative review and 2021 expert consensus recommendations. Br J Sports Med. 2021;55:356\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReardon CL, Hainline B, Aron CM, Baron D, Baum AL, Bindra A, et al. Mental health in elite athletes: International olympic committee consensus statement (2019). 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Psychol Sport Exerc. 2024;74:102681.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"athlete burnout, stress coping, sleep quality, adolescent athletes, psychological intervention","lastPublishedDoi":"10.21203/rs.3.rs-6496771/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6496771/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAthlete burnout significantly affects both athlete well-being and performance, potentially influenced 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.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA 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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eData 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\u0026thinsp;=\u0026thinsp;5.07), followed by age (β\u0026thinsp;=\u0026thinsp;2.19) and athlete level (β\u0026thinsp;=\u0026thinsp;3.76). Sleep disturbances (ASSQ) demonstrated a weaker yet significant contribution to ABQ (β\u0026thinsp;=\u0026thinsp;0.92). A temporal inflection point in age-related burnout trajectories was identified at 19 years.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe findings 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.\u003c/p\u003e","manuscriptTitle":"The relationship between diet, sleep, screen time, stress coping strategies with psychological strain and athlete burnout in Chinese competitive swimmers: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-12 19:28:21","doi":"10.21203/rs.3.rs-6496771/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-30T05:54:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-27T14:43:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306950094412851559017407758274532849389","date":"2025-08-22T06:52:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290140558119211781387655927181173539835","date":"2025-05-21T15:06:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-16T13:00:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203595430026217723609812309990035656533","date":"2025-05-15T15:39:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-07T09:58:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-29T09:49:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-28T07:16:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-27T14:43:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Sports Science, Medicine and Rehabilitation","date":"2025-04-27T14:42:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"31ff36a8-205e-4184-b22e-9dbaa28c245f","owner":[],"postedDate":"May 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:02:44+00:00","versionOfRecord":{"articleIdentity":"rs-6496771","link":"https://doi.org/10.1186/s13102-026-01695-9","journal":{"identity":"bmc-sports-science-medicine-and-rehabilitation","isVorOnly":false,"title":"BMC Sports Science, Medicine and Rehabilitation"},"publishedOn":"2026-05-01 15:57:39","publishedOnDateReadable":"May 1st, 2026"},"versionCreatedAt":"2025-05-12 19:28:21","video":"","vorDoi":"10.1186/s13102-026-01695-9","vorDoiUrl":"https://doi.org/10.1186/s13102-026-01695-9","workflowStages":[]},"version":"v1","identity":"rs-6496771","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6496771","identity":"rs-6496771","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}