Athletes’ psychological strain, negative emotions, and suicidal ideation: a mixed methods study

Abstract

Background

Suicidal ideation is an emerging issue among athletes, posing significant risks to their mental health. The mechanisms linking psychological strain to suicidal ideation remain unclear. This study addresses this gap by examining the mediating role of negative emotions in this relationship. Additionally, it employs latent profile and network analyses to identify key symptoms of psychological strain and negative emotions, providing novel insights for targeted interventions.

Methods

A total of 957 athletes from various sports in China were surveyed via the Athlete Psychological Strain Questionnaire (APSQ), the Depression, Anxiety, and Stress Scale (DASS-21), and the Positive and Negative Suicide Ideation Inventory (PANSI). Participants were recruited via convenience sampling from sports teams and clubs nationwide. Latent profile analysis was employed to classify the athletes into distinct groups on the basis of their psychological strain levels. This was followed by correlation analysis, structural equation modeling, and network analysis to examine the relationships and mediating effects among the variables.

Results

The results of the latent profile analysis indicated that athletes could be categorized into two groups: high- and low-psychological-strain groups. In the high-psychological-strain group, significant positive correlations were found between psychological strain, negative emotions, and suicidal ideation. Negative emotions partially mediated the relationship between psychological strain and suicidal ideation, accounting for 33% of the total effect. Network analysis revealed that “I worried about life after sport” was the core symptom of psychological strain, whereas “Life was meaningless” was the core symptom of negative emotions.

Conclusions

This study provides evidence for the crucial role of negative emotions in the relationship between athletes’ psychological strain and suicidal ideation. Network analysis highlights the core symptoms of psychological strain and negative emotions. The findings contribute to a deeper understanding of athletes’ mental health issues and offer valuable insights for developing targeted psychological interventions to enhance athletes’ psychological well-being and sports performance.

Clinical trial number

Not applicable.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12888-026-08099-6.

Introduction

In recent years, research in sports psychology has delved deeper into athletes’ mental health issues, revealing a high prevalence of such problems among athletes [1–5]. Despite the common perception of athletes as the epitome of health, their careers are fraught with challenges, crises, and persistent stress, significantly increasing their risk of mental health issues, with suicidal ideation being particularly prominent [6–9]. Suicidal ideation is not only an extreme manifestation of mental health problems but also a high-risk factor for suicidal behavior [10–12]. Thus, exploring the causes, risk factors, and underlying mechanisms of suicidal ideation in athletes is crucial.

This exploration can reveal the needs and challenges athletes face in states of psychological distress. It can also provide more effective support for athletes, helping them better cope with life challenges. Moreover, studying suicidal ideation and its risk factors can help identify high-risk groups, enabling timely intervention before suicidal ideation develops and preventing suicidal behavior. Therefore, investigating suicidal ideation in athletes is not only an academic imperative but also a responsibility for athletes’ mental well-being. It can also increase awareness of athletes’ mental health among academics, the general public, and governmental departments.

The theory of psychological strain provides a valuable framework for exploring the issue of suicidal ideation in athletes [13]. Psychological strain refers to the complex psychological state that individuals experience when facing multiple conflicting stressors [14, 15]. This multidirectional conflict is a key feature that distinguishes psychological strain from simple stress, which typically involves a single stressor. Unlike simple stress, psychological strain involves at least two conflicting stressors that exert influence from different directions, causing individuals to experience intense psychological conflict and distress, thereby leading to significant psychological discomfort [16, 17]. Suicidal ideation and behavior are believed to be influenced by a variety of biological, social, and psychological factors, and the theory of psychological strain has been proposed to explain the causes of suicidal behavior [18].

The theory of psychological strain posits that it is not merely the sum of individual stressors but rather the synergistic effect of multiple conflicting stressors that amplifies the psychological impact [13]. While the theory of psychological strain has been extensively studied in university students and adolescent populations, its application to athletes has yet to be empirically validated. Given the unique challenges and stressors that athletes face, such as performance pressures, injury, and career transitions, the theory of psychological strain may provide a more comprehensive understanding of the psychological distress experienced by athletes compared to traditional stress models [19–21]. Athletes often encounter more frequent and intense stressors in their careers than the general population, which can lead to significant psychological stress. However, those who have grown up in adversity may have developed greater psychological resilience to cope with these stressful situations [22–24]. Therefore, examining the relationship between psychological strain and suicidal ideation in athletes could provide valuable insights into the unique psychological dynamics of this population.

Some evidence suggests a link between psychological strain and suicidal ideation [25–27], yet research on the underlying mechanisms of this relationship is relatively insufficient. In recent years, the mediating role of negative emotions has garnered increasing attention [28–30]. Negative emotions, including depression, anxiety, and stress, are characterized by negative evaluations of current situations, a lack of confidence in the future, and the attribution of negative meanings to personal experiences [31]. These emotions have been widely reported to be associated with an increased risk of suicide [32, 33]. For example, Rogers et al. [34] examined the link between negative emotions and suicide risk in veterans. They found that when exposed to external stressors, negative emotions can heighten perceived burdens in veterans, consequently increasing their suicide risk. Similarly, when athletes face overwhelming conflict situations, negative emotions may be triggered, leading to suicidal ideation [35]. For some athletes, suicide may be perceived as an extreme way to relieve negative emotions. Additionally, research has revealed a strong correlation between psychological strain and depression, a typical negative emotion closely associated with suicidal behavior [36–39]. Therefore, it is imperative to investigate whether negative emotions mediate the relationship between psychological stress and suicidal ideation in athletes.

Currently, research on the relationships between athletes’ psychological strain and other variables has focused predominantly on examining relationships, with a lack of in-depth exploration on the basis of individual differences and core symptom identification. This study employs latent profile analysis, which groups continuous manifest variables into homogeneous categories, classifying individuals with similar characteristics into the same category for subsequent analysis. Additionally, network analysis is used as a supplement to traditional latent variable models. Network analysis creates a relationship map among observed variables, showing how strongly different factors are connected. By describing indicators on the basis of node characteristics (such as strength and centrality), network analysis offers a new visual and conceptual perspective for studying various psychological phenomena. Researchers calculate a regularized partial correlation network on the basis of the Gaussian graphical model. In this network, variables are represented by undirected nodes, with the thickness of the connecting lines (edges) proportional to the strength of the relationship between nodes. The blue lines indicate positive relationships, whereas the red lines indicate negative relationships [40, 41].

This study aims to investigate the relationships among psychological strain, suicidal ideation, and negative emotions and to explore the mediating role of negative emotions between psychological strain and suicidal ideation. Furthermore, it aims to investigate the core symptoms of psychological strain and negative emotions in athletes with high psychological strain scores and to explore the key aspects of these core symptoms within the traditional structural equation model framework. This approach can provide a clearer understanding of the significant internal characteristics and external behavioral manifestations of this group in terms of psychological strain and negative emotions, thereby offering targeted interventions and guidance for these athletes. Additionally, this study aims to help athletes manage their psychological states in real-life situations, reduce the negative impact of psychological strain and negative emotions on their mental health and performance, and enhance their psychological resilience and athletic performance. Figure 1 illustrates the hypothetical model of this study.

Fig. 1.

The hypothetical model

Method

Participants

This study employed a convenience sampling method, recruiting 1,001 athletes from various sports. To ensure data quality, the questionnaire included one lie scale item. Participants who failed the lie scale (N = 32), those who spent less than 60 s on the questionnaire (N = 10), and those who selected the same option for all questions (N = 2) were excluded. After screening, a final valid sample of 957 participants was obtained. Among them, 582 were male (60.8%) and 375 were female (39.2%). The sample included 50 elite athletes (5.2%), defined as those who finished in the top eight at national competitions [42], and 907 non-elite athletes (94.8%). The classification of elite and non-elite athletes was based on their competitive achievements, with elite athletes being those who had placed in the top eight at national competitions [42]. The sample included 496 athletes from physical strength-dominated sports (51.8%) and 461 from skill-dominated sports (48.2%). These classifications were based on the primary demands of the sports, with physical strength-dominated sports including athletics, swimming, and weightlifting, and skill-dominated sports including tennis, table tennis, and gymnastics. The participants’ ages ranged from 13 to 27 years, with an average age of 19.76 years (SD = 3.73). Their training durations ranged from 3 to 15 years, with an average training duration of 6.69 years (SD = 2.76). The athletes represented various teams and clubs from across China. Data collection was conducted through the online survey platform WenJuanXing (https://www.wjx.cn/), ensuring anonymity and confidentiality. The Ethics Committee of Chongqing University approved the research protocol, and all participants provided informed consent before participation.

Measurement

Athlete psychological strain questionnaire (APSQ)

The APSQ, designed by Rice et al. [43], is a brief self-report scale tailored to evaluate the psychological strain experienced by elite athletes. It consists of 10 items divided into three subscales: self-regulation (e.g., “I was less motivated”), performance, and external coping. Each item is rated on a 5-point Likert scale from 1 (never) to 5 (always). Subscale scores are obtained by summing the respective items, ranging from 4 to 20, while the total score ranges from 10 to 50, with higher scores indicating greater psychological strain. Previous studies have confirmed its high reliability and validity [44, 45]. The scale has also been effectively validated among Chinese athletes [46]. In this study, the Cronbach’s α coefficient was 0.851, and McDonald’s omega coefficient was 0.852. Confirmatory factor analysis (CFA) was conducted to assess the factorial structure of the APSQ. The results of the CFA were as follows: χ²/df = 2.274, CFI = 0.988, TLI = 0.983, SRMR = 0.020, and RMSEA = 0.036, indicating a good fit.

Depression, anxiety, and stress scale (DASS-21)

The DASS-21, developed by Lovibond et al. [31], is a concise self-report scale that assesses individuals’ levels of depression, anxiety, and stress. It includes 21 items across three subscales: depression (e.g., “Unable to become enthusiastic”), anxiety, and stress. Each item is scored on a 4-point Likert scale ranging from 0 (never) to 3 (always). The subscale scores are calculated by summing the relevant items, which range from 0 to 21. The total score ranges from 0 to 63, with higher scores indicating higher levels of negative emotions. Previous studies have confirmed its high reliability and validity, including Chinese individuals [47–49]. In this study, the Cronbach’s α coefficient was 0.960, and McDonald’s omega coefficient was 0.960. The results of the CFA were as follows: χ²/df = 1.654, CFI = 0.991, TLI = 0.990, SRMR = 0.017, and RMSEA = 0.026, indicating a good fit.

Positive and negative suicide ideation inventory (PANSI)

The PANSI, created by Osman et al. [50] in 1998, assesses individuals’ suicidal ideation through two dimensions, positive and negative, with a total of 14 items. Drawing on the work of Xu et al. [51], this study only used the negative suicidal ideation dimension, which includes 7 items (e.g., “Thought about killing yourself because you could not find a solution to a personal problem?“). Each item is scored on a 5-point Likert scale from 1 (never) to 5 (always), with higher total scores indicating higher levels of negative suicidal ideation. The scale has been widely used among Chinese populations, and its high reliability and validity have been confirmed [52, 53]. In this study, the Cronbach’s α coefficient was 0.942, and McDonald’s omega coefficient was 0.942. The results of the CFA were as follows: χ²/df = 0.91, CFI = 1, TLI = 1, SRMR = 0.006, and RMSEA = 0, indicating a good fit.

Data analysis

In the preliminary analysis, the data were cleaned and screened. Initially, responses that were missing or invalid were removed, and outliers were excluded. The initial sample size was 1,001 athletes, and after deleting data with missing or invalid responses, the final sample size was reduced to 957. In addition, to meet the assumption of normal distribution, the skewness and kurtosis values of the variables were analyzed. The skewness and kurtosis values of all variables were within the range of -1.5 to + 1.5, indicating that the data met the criteria for normal distribution [54]. These preliminary analyses ensured the quality and reliability of the data.

To explore the latent classes of athletes’ psychological strain, latent profile analysis (LPA) was conducted using Mplus 8.3. Starting with a one-class model, the number of classes was gradually increased, and the fit indices of all models were calculated. The fit indices evaluated included the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and the adjusted Bayesian Information Criterion (aBIC). Smaller values of these indices indicated better model fit. The larger the entropy index, the higher the classification accuracy. If the entropy value was greater than 0.8, the classification accuracy of the model exceeded 90%. The Lo-Mendell-Rubin (LMR) likelihood ratio test and the bootstrapped likelihood ratio test (BLRT) indicated that if the test result p was less than 0.05, then the K-class model was significantly better than the K-1 class model [55, 56]. Considering all the fit indices, the best latent profile model was determined.

After determining the latent classes of athletes’ psychological strain, the relationships among psychological strain, negative emotions, and suicidal ideation were further explored. Correlation analysis was conducted using SPSS 26.0 to assess the associations among these variables. In addition, a structural equation model was constructed using SPSS PROCESS 3.5 (Model 4) to test the mediating role of negative emotions between psychological strain and suicidal ideation. Gender, age, training duration, and athletic level were controlled for in the analysis.

Finally, to better understand the core symptoms of psychological strain and negative emotions, network analysis was performed using the qgraph package in R 4.3.1. The network analysis measured and analyzed the data through partial correlation methods, calculating indicators such as strength and closeness centrality. The higher the centrality index of a node, the more important that node is in the network, thus allowing the identification of core nodes. Strength centrality refers to the sum of the absolute values of the connection strengths between a node and other nodes, reflecting the connection strength of that node in the network. Closeness centrality refers to the average distance between a node and all other nodes in the network, which is the inverse of the sum of the shortest path distances between that node and other nodes, reflecting the central position of that node in the network.

Results

Athletes’ psychological strain profiles and their characteristics

This study employed the 10 items from the psychological strain scale as indicators to construct a latent profile model, estimating solutions ranging from 1 to 5 classes. As presented in Table 1, the results indicated that the AIC, BIC, and aBIC all decreased as the number of classes increased. Entropy, a measure of classification accuracy, was highest for the 2-class model. Following the model selection criteria, which favor smaller AIC, BIC, and aBIC values, an Entropy value above 0.8, and significant likelihood ratio tests (LMR-LRT and BLRT) with a minimum class size of 5% of the sample [55, 56], the 2-class model was chosen as the optimal solution. To further support our decision, we also examined the elbows in the line plot of model fit indices, as suggested by Espinoza et al. [57]. The line plot showed a clear elbow at the 2-profile solution (See Fig. 2), suggesting that adding more profiles beyond this point did not significantly improve model fit. This graphical analysis, combined with the model fit indices, led us to select the 2-profile model as the optimal solution.

Table 1.

Model fit indices for latent profile analysis

Model	AIC	BIC	aBIC	LMR(p)	BLRT(p)	Entropy	Attribution probability
Class1	33082.849	33180.125	33116.606	/	/	/	100
Class2	30734.904	30885.682	30787.227	< 0.001	< 0.001	0.909	29.36/70.64
Class3	30201.432	30405.712	30272.321	< 0.001	< 0.001	0.861	27.06/31.45/41.49
Class4	29762.958	30020.74	29852.414	< 0.001	< 0.001	0.870	27.06/26.65/27.27/19.02
Class5	29556.714	29867.998	29664.736	< 0.001	< 0.001	0.864	25.91/26.96/11.70/15.05/20.38

Note. AIC: Akaike Information Criterion; BIC: Bayesian Information Criterion; aBIC: adjusted Bayesian Information Criterion; LMR(p): Lo-Mendell-Rubin Adjusted LRT p-value); BLRT(p): Bootstrapped Likelihood Ratio Test p-value

Fig. 2.

Scree plot from LPA analysis

In the 2-class model, Class 1 presented lower mean scores across all 10 items and was labeled the “low-psychological-strain group,” comprising 281 participants (29.36%). Class 2, with higher mean scores on all items, was designated the “high-psychological-strain group” and included 676 participants (70.64%), as shown in Fig. 3. LPA is a person-centered approach that classifies individuals into distinct subgroups based on their profiles of continuous variables. The uneven distribution of participants across classes is a common outcome of LPA, as it aims to identify subgroups with distinct patterns rather than equal-sized groups. We focused on the high-psychological-strain group for subsequent analyses because this group exhibited substantial levels of psychological strain, making them more relevant for examining the core symptoms of psychological strain and their relationships with negative emotions and suicidal ideation. In contrast, the low-psychological-strain group had minimal levels of psychological strain, with scores significantly lower than the mean on most items.

Fig. 3.

Comparison of profile z-score values. Note: This figure illustrates the comparison of z-score values for psychological strain between the high-psychological-strain group and the low-psychological-strain group. The z-scores represent standardized scores that allow for a direct comparison of psychological strain levels between the two groups. Higher z-scores indicate greater levels of psychological strain

Descriptive statistics and correlation analysis

In the high psychological strain group (N = 676), the mean scores for psychological strain (M = 3.241, SD = 0.521), negative emotions (M = 1.436, SD = 0.731), and suicidal ideation (M = 2.935, SD = 0.985) were calculated. Correlation analysis revealed significant positive correlations between psychological strain and negative emotions (r = 0.371, p < 0.01), between psychological strain and suicidal ideation (r = 0.250, p < 0.01), and between negative emotions and suicidal ideation (r = 0.283, p < 0.01). According to Tabachnick and Fidell [54], skewness and kurtosis values within the range of − 1.5 to + 1.5 indicate that the data are normally distributed. The data in this study met this criterion. For a detailed overview of the descriptive statistics and correlations, see Table 2.

Table 2.

Descriptive and correlation analysis (N = 676)

Variables	1	2	3
1. Psychological Strain	1
2. Negative Emotions	0.371**	1
3. Suicidal Ideation	0.250**	0.283**	1
M	3.241	1.436	2.935
SD	0.521	0.731	0.985
Skewness	0.541	-0.511	-0.330
Kurtosis	-0.352	-1.085	-1.219

Note. ^***p < 0.001, M: Mean; SD: Standard Deviation

Mediating role of negative emotions in the relationship between psychological strain and suicidal ideation

A structural equation model was constructed with psychological strain as the independent variable, suicidal ideation as the dependent variable, and negative emotions as the mediating variable. And this study controlled for gender, age, training duration, and athletic level.

The results indicated that after controlling for gender, age, training duration, and athletic level, there was a significant positive correlation between psychological strain and negative emotions (β = 0.370, p < 0.001), between psychological strain and suicidal ideation (β = 0.165, p < 0.001), and between negative emotions and suicidal ideation (β = 0.224, p < 0.001). The specific path diagram is presented in Fig. 4. See Table 3 for the detailed results of the mediation analysis. Mediation analysis revealed that negative emotions partially mediated the relationship between psychological strain and suicidal ideation, with a mediating effect size of β = 0.083, accounting for 33% of the total effect, and its bootstrap 95% confidence interval did not contain 0 (0.051, 0.119), which indicates that its mediation effect was significant.

Fig. 4.

Standardized mediation pathway diagram of negative emotions

Table 3.

Mediation effect analysis

	Effect	SE	LLCL	ULCI	Proportion of effect
Total effect	0.248	0.038	0.174	0.322	100%
Direct effect	0.165	0.040	0.087	0.243	67%
Indirect effect	0.083	0.017	0.051	0.119	33%

Note. SE: Standard Error; LLCL: Lower Limit Confidence Interval; ULCI: Upper Limit Confidence Interval

This suggests that a substantial portion of the relationship between psychological strain and suicidal ideation can be explained by the presence of negative emotions. In other words, psychological strain not only directly increases the risk of suicidal ideation but also indirectly contributes to it by increasing negative emotions, which in turn heighten the risk of suicidal ideation. This finding underscores the importance of addressing negative emotions as a key factor in the relationship between psychological strain and suicidal ideation among athletes.

Core symptoms of psychological strain and negative emotions identified through network analysis

Correlation analysis and mediation testing revealed significant positive correlations among psychological strain, negative emotions, and suicidal ideation. As psychological strain increases in athletes, negative emotions also increase, in turn increasing the risk of suicidal ideation. Negative emotions partially mediate this relationship, with greater psychological strain leading to increased negative emotions and, consequently, a greater risk of suicidal ideation.

To better understand the associations between anxiety symptoms and psychological strain and the content of negative emotions, network analysis was employed to explore the core symptoms of psychological strain and negative emotions, guiding practical psychological interventions for athletes. The study calculates two important indicators in network analysis: strength and closeness. The network was estimated using the Gaussian graphical model with the graphical lasso method for regularization. This method helps in identifying the most relevant connections between variables by penalizing the partial correlation coefficients, resulting in a sparse network that highlights the core symptoms. In the constructed network, variables are represented as undirected nodes, with the thickness of the connecting lines (edges) proportional to the strength of the relationship between nodes. The blue lines indicate positive relationships, whereas the red lines indicate negative relationships.

In the network analysis results, the item “I worried about life after sport” (concern about life after sports career) was identified as having the highest strength and closeness on the psychological strain scale. This item is considered the core symptom of the scale and has the most significant impact on network stability. Changes in this item are more likely to trigger global changes. One of the most significant concerns for athletes facing multiple conflicting pressures is their worry about life after their sports career. Such worry can exacerbate psychological strain. This concern may stem from uncertainty about the future, the pressure of a career transition, and changes in identity. After investing a significant amount of time and effort in their careers, athletes may feel lost and helpless when facing retirement or career transitions, which is a crucial source of psychological strain (see Fig. 5).

Fig. 5.

Visualization of node relationships and influence in the sense of psychological strain

In the network analysis of the negative emotion scale, the item “Life was meaningless” (life feels meaningless) was found to have the highest strength and closeness (see Fig. 6). This finding indicates that this item is the core symptom of negative emotions in athletes. When athletes experience negative emotions, their emotional state and confidence in the future are most vulnerable. This further reveals the core dilemma athletes face when experiencing psychological strain: on the one hand, they struggle to effectively cope with the helplessness and psychological conflicts caused by stress; on the other hand, this stress may lead to persistent low moods and pessimistic expectations about the future. Clinically, this core symptom is significant as it reflects existential despair and a lack of purpose, which are strong predictors of suicidal ideation. Athletes experiencing this symptom may feel hopeless and disconnected, exacerbating their psychological distress. Identifying this core symptom underscores the need for targeted interventions, such as psychological counseling and support from coaches and peers, to help athletes find meaning and purpose in their lives.

Fig. 6.

Visualization of node relationships and influence in the sense of negative emotions

Discussion

This study employed latent profile analysis to categorize athletes into two distinct groups based on their levels of psychological strain: those experiencing high psychological strain and those with low psychological strain. Our primary focus was on the high-psychological-strain group, where we conducted a series of analyses, including correlation analysis, mediation effect testing, and network analysis. These analyses revealed that psychological strain and negative emotions significantly contribute to suicidal ideation in athletes with high levels of psychological strain. Specifically, negative emotions were found to partially mediate the relationship between psychological strain and suicidal ideation, accounting for 33% of the total effect. Additionally, network analysis identified “concern about life after a sports career” as a core symptom of psychological strain and “life feels meaningless” as a core symptom of negative emotions. These findings highlight the critical role of negative emotions in the relationship between psychological strain and suicidal ideation and underscore the importance of addressing these core symptoms in targeted interventions.

From a psychological perspective, individuals with high levels of psychological strain exhibit more intense emotional conflicts and higher levels of distress. Studying such groups can precisely reveal their internal psychological mechanisms and provide a basis for targeted psychological interventions [58, 59]. Simultaneously, from a statistical standpoint, these individual data are more representative, with relatively consistent behavioral and psychological characteristics. Homogeneous grouping through methods like latent profile analysis can enhance the accuracy and explanatory power of statistical models [60, 61]. Moreover, from an intervention perspective, individuals with high psychological strain better exemplify the characteristics of mental health issues [62]. Studying them can summarize effective intervention strategies, provide models for other athletes, and promote the precise implementation of intervention measures.

Analysis of athletes’ psychological strain profiles

Among Chinese athletes, two distinct profiles of psychological strain were identified through LPA. Athletes in the high-psychological-strain group accounted for 70.64% (676 individuals) of the total sample. These athletes, when confronted with pressures such as competitive demands, training intensity, and career planning, are more likely to experience significant psychological burdens and higher levels of psychological strain. They may exhibit more negative emotions and, in extreme cases, ideations of self-harm or suicide. This indicates that athletes in the high-psychological-strain group face a higher risk to their mental health and require greater attention and support.

Athletes in the low-psychological-strain group made up 29.36% (281 individuals) of the sample. This group demonstrated relatively lower levels of psychological strain. When facing stress, they may possess better coping strategies, stronger psychological resilience, and more robust social support systems. These factors help them maintain a relatively stable psychological state when confronted with similar challenges, thereby reducing the generation of psychological strain. To our knowledge, no prior studies have conducted latent profile analysis on psychological strain among athletes. This study is the first to reveal the heterogeneity of psychological strain among athletes through LPA, which is significant for understanding the mental health issues of athletes. This classification not only helps identify high-risk groups but also provides a scientific basis for developing targeted mental health interventions. By identifying and understanding athletes with different psychological strain profiles, we can better provide personalized support to promote their mental health and athletic performance.

Correlations among psychological strain, negative emotions, and suicidal ideation in athletes

This study revealed a significant positive correlation between psychological strain and negative emotions, indicating that higher levels of psychological strain are associated with increased negative emotions. This finding is consistent with prior research that has documented the significant impact of negative emotions on psychological strain and adverse mental health outcomes [63, 64]. For instance, Kiosses et al. [32] found that negative emotions significantly contribute to suicidal ideation in older adults with major depression and cognitive impairment. Similarly, Rogers et al. [34] examined the link between negative emotions and suicide risk in veterans, finding that negative emotions can heighten perceived burdens and increase suicide risk. These studies collectively highlight the critical role of negative emotions in exacerbating psychological strain and contributing to adverse mental health outcomes.

According to the theory of psychological strain [13], athletes facing multiple conflicting stressors are likely to experience psychological strain, which can lead to negative emotions [65–67]. When athletes are under high levels of psychological strain, they may feel desperate and depressed, increasing their risk of suicidal ideation [42, 68, 69]. Both psychological strain and negative emotions were significantly and positively correlated with suicidal ideation [14, 25]. The mediation analysis revealed that negative emotions partially mediate the relationship between psychological strain and suicidal ideation, accounting for 33% of the total effect. This suggests that psychological strain not only directly increases the risk of suicidal ideation but also indirectly contributes to it through the exacerbation of negative emotions [6, 70]. For example, when athletes face competition failure, injury-related distress, or career uncertainty, they may experience intense helplessness and despair. If these negative emotions are not promptly alleviated, they may lead to suicidal ideation. An athlete’s career is fraught with challenges and pressures. When athletes’ psychological needs remain unmet, they may develop unhealthy coping strategies, which can lead to negative emotions and even suicidal ideation [9].

Mediating role of negative emotion in the relationship between psychological stress and suicidal ideation in athletes

This study indicates that psychological strain can both directly increase suicidal ideation in athletes and indirectly contribute to it by inducing negative emotions. Psychological strain causes significant negative emotions in athletes [45], which, under limited cognitive and emotional resources, directly impact their mental health and escalate the risk of suicidal ideation [8]. Additionally, an athlete’s psychological resilience and social support system can, in turn, influence their level of psychological strain. Athletes with lower psychological resilience are more prone to feelings of despair and depression when facing pressure, and those lacking social support are more likely to experience psychological distress. To address this, it is crucial to focus on athletes’ mental health in both training and life and detect and intervene in psychological strain early on. Moreover, strengthening team support, coaches’ care, and family understanding are essential, as the social support system plays a positive role in athletes’ mental well-being. Social support is effective because it provides emotional validation and a sense of belonging, reducing feelings of isolation. It also offers practical help, such as assistance with training schedules and career planning, which can alleviate logistical burdens. Additionally, social support enhances athletes’ self-efficacy and resilience, helping them cope better with stressors. These benefits collectively contribute to improved mental health.

Previous research has shown that adverse life events experienced by athletes, such as competition failure and injury-related distress, can directly lead to psychological strain [71–73]. Factors such as helplessness, low achievement, and mental exhaustion due to psychological strain have a significant negative effect on athletes’ mental health [65, 74]. Combining these findings with our findings, it appears that not only does psychological strain directly trigger negative emotions in athletes, but also that adverse life events generate negative emotions and learned helplessness in athletes with unmet needs. These states, such as self-doubt, reality avoidance, or effort abandonment, directly reduce psychological resilience and increase the risk of suicidal ideation.

Psychological strain can cause an imbalance in athletes’ cognitive resource allocation and is often accompanied by issues such as high competitive pressure, lack of recognition, and psychological barriers. Adverse life events can impact various aspects of an athlete’s environment, increasing negative emotions and reducing psychological resilience. This leads to a lack of focus, which in turn affects mental health and athletic performance. When addressing athletes’ mental health issues, individual and environmental factors should be considered together, with emphasis on the impact and bridging role of environmental factors. On the basis of the conclusions of this study, negative emotions and adverse consequences of psychological strain can directly enhance an individual’s self-evaluation of life events. Given that stressful events experienced by athletes can directly lead to psychological strain and affect mental health, efforts should be made to minimize athletes’ exposure to such events. Additionally, from a positive psychology perspective, relevant courses and specific psychological counseling should be implemented to cultivate athletes’ psychological resilience and mitigate the negative impact of stressful events on their mental health [75].

Network analysis of core symptoms of psychological strain and negative emotions

This study employed network analysis to explore the core symptoms of psychological strain and negative emotions. Research has shown that mental health issues result from the interaction of various symptoms, and the theory of psychological strain also suggests that psychological strain arises from multiple conflicting stressors [76]. The analysis revealed that the core symptom of psychological strain is “worry about life after a sports career.” The high strength and closeness of these symptoms indicate that athletes’ intense concern about their postcareer life not only affects their current psychological state but also exacerbates their psychological strain. This worry often stems from uncertainty about the future, the pressure of a career transition, and changes in identity. After they invest significant time and effort in their careers, athletes may feel lost and helpless when facing retirement or career transitions, which can become a significant source of psychological strain.

The core symptom of negative emotions was found to be “feeling that life is meaningless.” Its high strength and closeness suggest that when athletes experience negative emotions, their emotional state and confidence in the future are most affected [77]. This persistent low mood and pessimistic outlook on the future can lead to a loss of interest and motivation in life, which in turn further impacts their mental health and athletic performance. Negative emotions not only reduce athletes’ psychological resilience but can also cause cognitive biases, leading them to make negative evaluations of their abilities and future prospects.

The study concluded that psychological strain does not stem from a single pressure but rather from a conflicted psychological state caused by the interaction of multiple stressors. It is an external manifestation of unmet psychological needs. Therefore, interventions targeting a single stressor cannot fundamentally help athletes reduce their psychological strain and negative emotions. Instead, efforts should focus on adjusting athletes’ emotional states and meeting their inner needs. This includes considering their career development and psychological needs and planning for their cognitive preferences about the future, emotional experiences, and volitional tendencies. Enhancing athletes’ perceptual breadth and optimistic attitudes toward the future is essential, as it can help them recognize the differences between the virtual and real worlds, help them focus on reality and allocate their time and energy more effectively to training and competition. Throughout this process, attention should be given to the impact of negative emotions as psychological states. Reducing tension and anxiety caused by sudden stressful events in the environment, alleviating athletes’ psychological pressure, strengthening their psychological resilience, and promoting their overall physical and mental development are also crucial. To achieve these goals, comprehensive support systems including regular psychological assessments, access to mental health professionals, and stress management workshops are essential. These measures can help athletes develop effective coping strategies and enhance their stress management abilities. Additionally, interventions targeting “worry about life after a sports career” could include career counseling and transition programs. For the core symptom of “feeling that life is meaningless,” interventions could focus on enhancing athletes’ sense of purpose and meaning through activities such as meaning-focused therapy and engagement in meaningful community service. These efforts aim to strengthen athletes’ psychological resilience and improve their overall mental health.

Research significance and limitations

The findings of this study carry substantial theoretical and practical weight in understanding athletes’ mental health issues. First, by applying psychological strain theory to athletes, this study further confirms the theory’s utility in elucidating suicidal ideation. The analysis of the mediating role of negative emotions enriches the theoretical framework of psychological strain theory within sports psychology. Second, through network analysis, the study pinpointed the core symptoms of psychological strain and negative emotions, offering valuable reference points for future research. Third, the results provide a scientific foundation for developing targeted psychological interventions. By identifying athletes with high psychological strain and negative emotions, early intervention can reduce the risk of suicidal ideation. Finally, the study underscores the importance of bolstering athletes’ psychological resilience and social support systems. Support from families, coaches, and teams can help athletes manage psychological strain, reduce negative emotions, and ultimately enhance their mental well-being and athletic performance.

The study also acknowledges certain limitations. First, this study relies on self-reported data, which may limit the objectivity of the findings. It should be considered that personal biases may have influenced participants’ responses. Second, the measurement tools used, such as the APSQ and the DASS-21, have their limitations in capturing the full spectrum of psychological experiences. Third, the data sources are limited, with elite athletes being significantly underrepresented compared to non-elite athletes. This imbalance may affect the generalizability of the findings. Future research should consider incorporating multiple data sources, such as interviews, observational data, and physiological measures, to provide a more comprehensive understanding of the constructs being measured. Fourth, another important limitation of this study is that it relies on self-reported data, which may limit the objectivity of the findings. It should be considered that personal biases may have influenced participants’ responses. Finally, the study relies on cross-sectional data for mediation analyses, which has significant limitations as demonstrated by Maxwell and Cole [78]. This restricts the ability to establish causality and may affect the interpretation of the mediation effects. Future research should consider incorporating multiple data sources, such as interviews, observational data, and physiological measures, to provide a more comprehensive understanding of the constructs being measured. Additionally, longitudinal studies could help address the limitations of cross-sectional mediation analyses.

Conclusion

This study explored the relationships among athletes’ psychological strain, negative emotions, and suicidal ideation via latent profile analysis, mediation effect testing, and network analysis. Psychological strain indirectly affects suicidal ideation through negative emotions such as hopelessness and depression, which act as significant mediators. Network analysis also revealed the core symptoms of psychological strain and negative emotions, offering a fresh perspective on athletes’ mental health. Despite limitations in terms of sampling, design, and measurement tools, this study has important theoretical and practical value. Future research should expand sampling, adopt longitudinal designs, conduct cross-cultural studies, and use more precise measurement tools. This will provide a more comprehensive understanding of the mechanisms behind athletes’ mental health issues and help develop more effective psychological interventions.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

AIC

Akaike Information Criterion

BIC

Bayesian Information Criterion

aBIC

Sample Size-Adjusted Bayesian Information Criterion

LMR-LRT

Lo-Mendell-Rubin Likelihood Ratio Test

BLRT

Bootstrap Likelihood Ratio Test

APSQ

Athlete Psychological Strain Questionnaire

DASS-21

Depression, Anxiety, and Stress Scale

PANSI

Positive and Negative Suicide Ideation Inventory

CFI

Comparative Fit Index

TLI

Tucker-Lewis Index

SRMR

Standardized Root Mean Square Residual

RMSEA

Root Mean Square Error of Approximation

CFA

Confirmatory Factor Analysis

LLCL

Lower Limit Confidence Interval

ULCI

Upper Limit Confidence Interval

LPA

Latent Profile Analysis

Author contributions

Conceptualization, C.H. and Z.J.; methodology, C.H.; software, W.H.; validation, C.H. and W.H.; formal analysis, J.Q.; investigation, W.Z.; resources, W.Z.; data curation, C.H.; writing—original draft preparation, C.H.; writing—review and editing, W.H.; visualization, C.H.; supervision, Z.J.; project administration, W.Z.; funding acquisition, W.H. All authors have read and agreed to the published version of the manuscript.

Funding

Fundamental Research Funds for the Central Universities (2024CDSKXYTY003).

Data availability

The data presented in this study are available on request from the corresponding author.

Declarations

Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Chongqing University (CQU-20240910). Informed consent was obtained from both the guardians and the subjects themselves who participated in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.