Psychological Profiles, Stress, Sleep Quality, and Psychological Risk Indicators in Elite Football Players: Differences by Sex and Competitive Level

1. Introduction

In recent years, psychological well-being and mental health in high-performance sport have received increasing attention in both research and professional practice, particularly because of the need to identify and address signs of psychological distress in elite athletes at an early stage (Gulliver et al., 2012; Reardon et al., 2019). In highly demanding environments, variables such as stress, anxiety, mood, perfectionism, motivation, and stress control may influence how athletes respond to training, competition, and broader social demands. These variables have been associated with athletes’ psychological adaptation, well-being, and responses to the demands of training and competition, as well as with vulnerability to adverse outcomes, including impaired performance and injury. However, these relationships should be understood within a multifactorial framework rather than interpreted as direct or causal associations (Gil-Caselles et al., 2025; Ivarsson et al., 2017; Johnson & Ivarsson, 2025; Sánchez-Ruiz et al., 2025; Tranaeus et al., 2024).

This issue is particularly relevant in elite football, where physical, competitive, and social demands extend from the developmental stages through to the professional level. Selection and deselection processes, transitions between age groups and competitive categories, performance expectations, and the uncertainty associated with athletic progression may shape players’ psychological experiences and challenge their ability to cope with the demands of the football environment (Mills et al., 2012; Saward et al., 2020; Swainston et al., 2020). In this context, the systematic assessment of psychological functioning may help identify players with differing support needs and inform appropriate monitoring, referral, and intervention strategies (Gouttebarge et al., 2021). However, indicators obtained through psychological assessment should be interpreted as signs of potential distress or less adaptive functioning rather than as clinical diagnoses or direct predictors of injury or impaired performance (Johnson & Ivarsson, 2025; Purcell et al., 2019; Reardon et al., 2019).

Psychological characteristics related to sport performance have long been examined as relevant components of athletes’ adaptation and performance across different sporting disciplines. Early approaches highlighted the potential contribution of variables such as anxiety, motivation, concentration, self-confidence, mental preparation, and coping with competitive demands to athletic performance (Mahoney et al., 1987; Mahoney & Meyers, 2021). Subsequent research across different sports has further shown that psychological characteristics may be associated with sport engagement, the development of expertise, and competitive performance, although these relationships depend on the athlete, the task, and the sporting context (Fawver et al., 2020; Lochbaum et al., 2022).

Building on this line of research, Gimeno et al. (2001) developed the Cuestionario de Características Psicológicas Relacionadas con el Rendimiento Deportivo (CPRD), which provides a multidimensional assessment of stress control, the influence of performance evaluation, motivation, mental skills, and team cohesion. The CPRD has been used to characterize psychological profiles in different sports, including triathlon and road cycling (Olmedilla et al., 2018), as well as to examine psychological disposition and perceived performance in young female football players (Olmedilla et al., 2019). Research in elite youth football has also emphasized the relevance of psychological characteristics during the development of sporting excellence (Saward et al., 2023). Together, these findings support the assessment of performance-related psychological characteristics as part of a broader understanding of athletes’ adaptation to training and competition.

Perfectionism is another relevant psychological characteristic in high-performance sport. Contemporary multidimensional models distinguish between relatively adaptive forms of perfectionism, characterized by the pursuit of demanding personal standards and high levels of achievement, and less adaptive forms, characterized by excessive concern over mistakes and negative reactions to perceived imperfection (Stoeber, 2011; Stoeber et al., 2007). These dimensions may have different implications for athletes’ adjustment: maladaptive perfectionism has been associated with competitive anxiety, fear of failure, psychological distress, and burnout (Hill et al., 2016; Hill et al., 2018), whereas adaptive perfectionism may be associated with more favorable motivational and performance-related outcomes when it is not accompanied by excessive concerns over mistakes or negative reactions to failure (Stoeber, 2011).

Trait anxiety represents an important individual-difference variable in competitive sport. Unlike state anxiety, which reflects a transient emotional response to a specific situation, trait anxiety refers to a relatively stable tendency to perceive a wide range of situations as threatening and to respond with heightened anxiety (Spielberger et al., 1983). Athletes with higher trait anxiety may therefore be more likely to experience stronger anxiety responses when confronted with competitive pressure, performance evaluation, or the possibility of failure. Classical and cognitive-developmental perspectives have emphasized that the relationship between anxiety and athletic performance depends not only on the intensity of the anxiety response, but also on how athletes interpret the situation and regulate their cognitive and emotional reactions (Mahoney & Meyers, 2021). Anxiety in sport may also arise within interpersonal and intragroup contexts, including relationships with coaches and teammates, role expectations, and group dynamics (Hanin, 2021). Accordingly, anxiety responses in sport should be understood as the result of interactions between individual predispositions, situational appraisal, perceived coping resources, interpersonal conditions, and the specific demands of competition (Hackfort & Spielberger, 2021; Horikawa & Yagi, 2012).

Symptoms of depression, anxiety, and stress provide complementary information about athletes’ emotional functioning. Although these symptoms should not be equated with psychiatric diagnoses, elevated levels may reflect psychological distress and indicate a need for closer monitoring or further professional evaluation (Purcell et al., 2019; Reardon et al., 2019). In high-performance sport, such symptoms may be influenced by competitive pressure, injury, uncertainty regarding selection, performance expectations, and demands outside sport (Reardon et al., 2019; Vaughan et al., 2020). These factors highlight the importance of considering depression, anxiety, and stress within a broader assessment of athlete well-being.

Alongside these psychological factors, sleep quality has become an increasingly important component of athlete health, recovery, and performance monitoring. Adequate sleep contributes to physical recovery, emotional regulation, cognitive functioning, and readiness to train and compete, whereas poor sleep quality or sleep disruption may negatively affect several aspects of athletic functioning (Fullagar et al., 2015; Walsh et al., 2021). Elite athletes may be particularly vulnerable to sleep difficulties because of intensive training, competition schedules, travel, pre-competition arousal, and academic or personal demands (Gupta et al., 2017). Accordingly, sleep quality should be considered alongside psychological and contextual information within the comprehensive monitoring of athletes.

From a preventive perspective, the joint assessment of these factors may be used to identify psychological risk indicators and to obtain an overall estimate of an athlete’s psychological risk at a given point in time. Rather than constituting a clinical diagnosis or a deterministic prediction of injury or impaired performance, such an estimate can provide a structured basis for identifying athletes who may require closer monitoring and for guiding individualized psychological support or intervention aimed at reducing potential risk and promoting more adaptive functioning (Gouttebarge et al., 2021; Purcell et al., 2019; Reardon et al., 2019).

Despite the relevance of these variables, studies examining psychological functioning, emotional distress, sleep quality, and psychological risk indicators in an integrated manner within the same elite football organization remain limited. Further research is also needed to clarify how psychological profiles and psychological risk indicators are distributed according to sex, competitive level, and contextual factors such as competitive participation, playing position, and living arrangements. Examining these factors jointly may help identify subgroups with different monitoring needs and support the translation of scientific evidence into professional practice.

Accordingly, the general aim of the present study was to describe and compare psychological profiles and psychological risk indicators within the context of athletic development and performance in football players belonging to an elite sporting structure. The first specific objective was to determine whether psychological profiles and the distribution of psychological risk indicators differed according to sex and competitive level. The second specific objective was to examine whether the distribution of psychological risk indicators varied according to competitive participation, playing position, and living arrangements among male and female football players. A third, exploratory objective was to examine whether psychological risk indicators also varied according to years at the club and body-composition status, operationalized through team-specific tertiles of the sum of six skinfolds.

It was hypothesized that psychological profiles and the distribution of psychological risk indicators would differ according to sex and competitive level. It was also expected that players competing at higher competitive levels would show more favorable psychological profiles and lower global psychological risk than players competing at lower levels. In addition, the distribution of psychological risk indicators was expected to vary according to competitive participation, playing position, and living arrangements in both male and female players. Given the limited evidence regarding years at the club and body-composition status, analyses involving these variables were considered exploratory, and no directional hypotheses were established.

2. Materials and Methods

2.1. Design

This study used an observational, descriptive, and cross-sectional design to examine psychological profiles, sleep quality, and psychological risk indicators in elite football players. Reporting followed the Strengthening the Reporting of Observational Studies in Epidemiology guidelines for cross-sectional studies (von Elm et al., 2007).

2.2. Participants

The final sample comprised 376 elite football players from a single high-performance football academy, aged 14 to 37 years (M = 20.3, SD = 5.3). Of these, 245 were male and 131 were female. Participants were distributed across seven male teams and four female teams competing at U16, U19, amateur, and professional levels. All participants were registered within the club’s sporting structure during either the 2023/24 or 2024/25 season and completed the psychological assessment as part of the club’s routine athlete-monitoring program.

2.3. Measures

Psychological assessment was conducted using the measures included in the Psycholight protocol (Olmedilla-Zafra & García-Mas, 2023), following the assessment structure previously applied by Morelló et al. (2026). Psycholight is a systematic framework designed to assess psychological functioning and potential indicators of psychological vulnerability in applied high-performance sport settings. For the present study, nine psychological and sleep-related components were considered: motivation, stress control, influence of performance evaluation, maladaptive perfectionism, trait anxiety, symptoms of depression, anxiety and stress, and global sleep quality. All instruments were administered in validated Spanish-language versions.

2.3.1. Psychological Characteristics Related to Sport Performance Questionnaire

Psychological characteristics related to sport performance were assessed using the Cuestionario de Características Psicológicas Relacionadas con el Rendimiento Deportivo (CPRD; Gimeno et al., 2001). The CPRD comprises 55 items distributed across five subscales: stress control, influence of performance evaluation, motivation, mental skills, and team cohesion. Items are rated on a five-point response scale. In the present study, three of the five CPRD subscales were included: motivation, stress control, and influence of performance evaluation. These subscales were selected because they form part of the Psycholight psychological risk framework. Higher scores indicate more favorable psychological characteristics related to sport performance.

2.3.2. Multidimensional Inventory of Perfectionism in Sport

Sport-specific perfectionism was assessed using the Multidimensional Inventory of Perfectionism in Sport (MIPS; Stoeber et al., 2006; Atienza et al., 2020). The instrument assesses two broad dimensions: perfectionistic strivings and negative reactions to imperfection. In the present study, one of the two MIPS dimensions was included: negative reactions to imperfection, which was used as an indicator of maladaptive perfectionism within the psychological risk framework. Higher scores reflect greater concern about mistakes and more negative responses to perceived failure or imperfection.

2.3.3. State–Trait Anxiety Inventory

Trait anxiety was assessed using the State–Trait Anxiety Inventory (STAI; Spielberger et al., 1983; Buela-Casal et al., 2015). The STAI comprises separate state and trait anxiety subscales. In the present study, only the trait anxiety subscale was included. This subscale comprises 20 items and assesses relatively stable individual differences in the tendency to perceive situations as threatening and to respond with heightened anxiety. Items are rated on a four-point response scale, with higher scores indicating greater trait anxiety.

2.3.4. Depression Anxiety Stress Scales–21

Symptoms of depression, anxiety, and stress were assessed using the Depression Anxiety Stress Scales–21 (DASS-21; Lovibond & Lovibond, 1995; Bados et al., 2005). The instrument comprises 21 items distributed across three seven-item subscales: depression, anxiety, and stress. All three DASS-21 subscales were included in the present study. Participants indicated the extent to which each statement had applied to them during the previous week using a four-point response scale ranging from 0 (did not apply to me at all) to 3 (applied to me very much or most of the time). Higher scores indicate greater symptom severity.

2.3.5. Pittsburgh Sleep Quality Index

Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI; Buysse et al., 1989; Royuela & Macías, 1997). The PSQI comprises 19 self-report items grouped into seven components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. These components are combined to produce a global score ranging from 0 to 21. In the present study, only the global PSQI score was included, and the seven component scores were not analyzed separately. Higher global scores indicate poorer sleep quality.

Together, the selected subscales, dimensions, and global scores provided a multidimensional assessment of psychological functioning, emotional distress, and sleep quality. They were used to characterize players’ psychological profiles and to construct the global psychological risk index.

2.4. Procedure

Data were collected during the 2023/24 and 2024/25 competitive seasons. For the present study, only the baseline psychological assessment conducted at the beginning of the corresponding season was included in the analyses. Participants completed the selected measures included in the Psycholight protocol in digital format within the club’s usual training environment. Assessments were administered and supervised by members of the club’s psychology staff in accordance with the standardized procedures established for the protocol (Olmedilla-Zafra & García-Mas, 2023).

Participation was voluntary, and all data were treated confidentially and anonymized before analysis. All adult participants provided written informed consent. For participants under 18 years of age, written informed consent was obtained from a parent or legal guardian, and the minors also provided assent before participation. The study was approved by the Research Ethics Committee of the University of Murcia on 24 June 2025 (CEI-ACTA23-2025) and was conducted in accordance with the ethical principles of the Declaration of Helsinki (World Medical Association, 2025).

2.5. Psychological Risk Index

A cumulative psychological risk index was constructed according to the criteria of the Psycholight protocol (Olmedilla-Zafra & García-Mas, 2023) and the operationalization previously applied by Morelló et al. (2026). The index comprised nine components: stress control, influence of performance evaluation, and motivation, assessed using the CPRD; maladaptive perfectionism, assessed using the MIPS; trait anxiety, assessed using the STAI; symptoms of depression, anxiety, and stress, assessed using the DASS-21; and global sleep quality, assessed using the PSQI.

Each component was classified into three risk levels—low, medium, and high—according to the interpretation criteria and cut-off scores established for each instrument and integrated into the Psycholight protocol. These levels were coded as 0, 1, and 2, respectively. The sum of the nine components yielded a raw score ranging from 0 to 18, with higher scores indicating greater cumulative psychological vulnerability. This score was subsequently rescaled to a 0–10 metric to facilitate interpretation using the following formula:

Rescaled score = raw score × 10/18.

Based on the cumulative score, participants were classified into three global psychological risk categories. Raw scores from 0 to 4 were classified as low risk, scores from 5 to 9 as medium risk, and scores of 10 or above as high risk. For interpretability, the raw score was linearly rescaled to a 0–10 metric; however, classification into low-, medium-, and high-risk categories was based exclusively on the raw-score thresholds described above.

The index should be interpreted as an indicator of cumulative psychological vulnerability at the time of assessment rather than as a clinical diagnosis or a deterministic predictor of injury or impaired performance.

2.6. Statistical Analysis

Data preprocessing and statistical analyses were conducted using R, version 4.4.1. Psychological and sleep-related variables were summarized using the median and interquartile range, expressed as the 25th and 75th percentiles.

Pairwise comparisons were conducted between teams and, within each sex, between groups defined according to the contextual variables considered: playing position, years at the club, sum of six skinfolds, competitive participation, and living arrangements. Comparisons were performed using the Mann–Whitney U test. Effect sizes were estimated using Wilcoxon’s r and interpreted as small, moderate, and large using reference values of 0.10, 0.30, and 0.50, respectively.

For each psychological variable and for the global index, the proportions of participants classified as low, medium, and high risk were calculated. For inferential analyses, participants classified as medium or high risk were compared with those classified as low risk. Group differences were examined using Fisher’s exact test, and odds ratios (ORs) with 95% confidence intervals were calculated.

To control for multiple comparisons, p values were adjusted using the Benjamini–Hochberg false discovery rate procedure. Statistical significance was set at p < .05.

3. Results

3.1. Differences by Sex and Competitive Level

Descriptive analyses showed differences in psychological profiles across teams according to sex and competitive level. Overall, male teams showed higher scores on psychological characteristics associated with sport performance, particularly motivation and stress control, together with lower scores on indicators of emotional distress, including trait anxiety, anxiety symptoms, depressive symptoms, and stress. These findings should be interpreted within the specific context of the present sample and should not be directly generalized to other sporting environments. Detailed results are presented in Table 1.

Greater between-team variability was observed among female players. Lower-level teams, particularly the female U16 team, showed higher trait anxiety, anxiety symptoms, stress, depressive symptoms, poorer sleep quality, and higher global psychological risk scores. Overall, these results indicate that psychological profiles varied not only by sex but also by competitive level within the club structure. The corresponding pairwise comparisons and effect sizes are presented in Table 1.

3.2. Distribution of Psychological Risk

The analysis of risk distribution complemented the comparison of scores by showing the proportion of players classified as being at low, medium, or high risk for each psychological variable and for the global index. The psychological risk index summarized the information obtained from the different variables assessed and facilitated the identification of differential patterns across groups.

Low-risk classifications predominated across most of the variables assessed in the men’s teams. In contrast, the women’s teams showed higher proportions of players classified as being at medium or high risk, particularly for anxiety, stress, sleep quality, and the global psychological risk index. This pattern was especially evident in the lower competitive levels, where a greater concentration of players with psychological risk indicators was observed. Differences in the proportion of players classified as being at medium or high risk were also quantified using odds ratios, as shown in Table 2.

3.3. Distribution of Psychological Risk According to Contextual Variables

The analysis of contextual variables showed different patterns in male and female players. Among female players, psychological risk was associated with several individual and contextual conditions. Lower competitive participation was associated with higher proportions of players classified as being at medium or high risk across several psychological variables. Differences were also observed according to playing position, with forwards showing less favorable risk distributions than other positional groups in several outcomes. Living arrangements were also associated with psychological risk. In this sample, female players living in the family home showed higher odds of being classified in the medium- or high-risk categories than those living elsewhere for several indicators, including anxiety symptoms, depressive symptoms, stress, and the global psychological risk index.

Among male players, some significant differences were observed according to playing position, years at the club, sum of six skinfolds, competitive participation, and living arrangements. However, these differences did not form a consistent pattern across the psychological variables assessed. The complete distributions and corresponding between-group comparisons are presented in Table 3 and Table 4.

4. Discussion

The general aim of the present study was to describe and compare psychological profiles and psychological risk indicators within the context of athletic development and performance in football players belonging to the same elite sporting structure. The first specific objective was to determine whether psychological profiles and the distribution of psychological risk indicators differed according to sex and competitive level. The second specific objective was to examine whether psychological risk indicators varied according to competitive participation, playing position, and living arrangements among male and female players. A third, exploratory objective was to examine whether psychological risk indicators also varied according to years at the club and body-composition status.

Overall, the findings supported the hypothesis that psychological profiles and psychological risk distributions would differ according to sex and competitive level. However, the expectation that players competing at higher levels would consistently display more favorable psychological profiles and lower global psychological risk was only partially supported, because the observed pattern was not uniform across all psychological variables or competitive categories. The findings also supported the expectation that psychological risk indicators would vary according to competitive participation, playing position, and living arrangements, although these associations were more consistent among female players than among male players. Regarding the third, exploratory objective, some differences were observed according to years at the club and body-composition status, but these findings did not form a consistent pattern across psychological indicators or between male and female players.

Overall, male teams tended to show more favorable profiles across several performance-related and emotional indicators, including higher motivation and stress control and lower levels of trait anxiety, symptoms of emotional distress, sleep difficulties, and cumulative psychological risk. These findings are broadly consistent with previous research in football reporting sex-related differences in symptoms of depression and anxiety. Junge and Feddermann-Demont (2016), for example, identified differences in the prevalence of depressive symptoms between male and female top-level football players, although anxiety symptoms were less frequent and the observed patterns varied according to age and competitive group. In a subsequent study involving 17 female football teams, Junge and Prinz (2019) reported substantial levels of depressive and generalized anxiety symptoms and showed that mental health outcomes were associated with both personal and sport-specific variables. Research involving elite female footballers in England has similarly identified relevant symptoms of anxiety, depression, and other mental health difficulties, together with associations involving sporting status and the perceived need for psychological support (Perry et al., 2022). These findings reinforce the relevance of monitoring psychological functioning in women’s football, while also indicating that the differences observed in the present study cannot be explained exclusively by sex.

The present results should therefore not be interpreted as evidence of inherent psychological differences between male and female players. Differences in self-reported distress may reflect a combination of developmental, social, organizational, and sport-specific conditions, together with possible differences in the recognition, interpretation, and expression of psychological difficulties. More generally, research on psychological sex differences indicates substantial overlap between males and females and emphasizes that many observed differences are small and dependent on contextual and measurement conditions (Hyde, 2014). Within elite sport, female athletes may also experience specific organizational and psychosocial demands, including differences in resources, professional conditions, institutional support, visibility, and opportunities for career development (Pascoe et al., 2022). Accordingly, the present findings should be interpreted as patterns observed within this particular academy and sample rather than as general characteristics of male and female football players.

Differences according to competitive level were also identified, although they did not follow a completely linear pattern. Less favorable profiles across several indicators were observed in some lower-level female teams, particularly the U16 team. These findings are consistent with the view that developmental stages and transitions within elite football may expose young players to multiple simultaneous demands. Academy players must manage selection and deselection processes, uncertainty regarding progression, increasing performance expectations, changes in team status, and, in many cases, academic and family demands (Mills et al., 2012; Saward et al., 2020; Swainston et al., 2020). Longitudinal research on the junior-to-senior transition has similarly shown that young football players experience changing expectations, uncertainty, and adjustment demands as they attempt to establish themselves within professional environments (Swainston et al., 2020). Research involving young talented football players has also indicated that mental health profiles are heterogeneous and may be shaped by the interaction between sport-related demands and athletes’ broader developmental circumstances (Tachom Waffo & Hauw, 2024).

Nevertheless, the expectation that increasing competitive level would consistently correspond to a more favorable psychological profile was only partially supported. Higher-level players may have accumulated greater competitive experience, developed more effective coping resources, or been selected partly because of their ability to adapt to demanding sporting environments. Previous research in women’s football has reported a tendency for higher-level players to display more favorable psychological characteristics, although the available evidence remains heterogeneous (Pettersen et al., 2022; Ruiz-Esteban et al., 2020). At the same time, progression to higher competitive levels may involve greater performance expectations, more intensive evaluation, increased public or organizational visibility, and greater uncertainty regarding selection or sporting status. Competitive level may therefore represent both successful adaptation to the sporting environment and exposure to additional psychological demands. This dual role may explain why some higher-level teams showed more favorable profiles, whereas others did not differ consistently across all psychological variables. The findings consequently support a team- and variable-specific interpretation rather than the assumption of a simple linear relationship between competitive level and psychological functioning.

The analysis of psychological risk categories provided information that complemented the comparison of median scores. Whereas measures of central tendency describe the typical profile of each group, the classification of players into low-, medium-, and high-risk categories helps determine whether less favorable psychological indicators are concentrated within particular subgroups. This distinction is relevant because similar group averages may coexist with markedly different proportions of players showing elevated psychological vulnerability. Previous systematic reviews and meta-analyses have shown that symptoms of anxiety, depression, psychological distress, and sleep disturbance are relatively common among elite athletes, although prevalence estimates vary substantially according to the instruments, thresholds, sports, and populations examined (Gouttebarge et al., 2019; Rice et al., 2016). In addition, Morelló et al. (2026) previously applied the Psycholight framework to combine several psychological dimensions into a cumulative risk indicator in football players, supporting the potential value of integrated assessment rather than the isolated interpretation of individual variables.

The risk classifications used in the present study should not be interpreted as clinical diagnoses or deterministic predictions of injury, impaired performance, or other adverse outcomes. Rather, they represent operational indicators of cumulative psychological vulnerability at the time of assessment. From an applied perspective, these classifications may help identify players who require closer assessment, continued monitoring, or individualized psychological support. The risk index should therefore be understood as a screening and decision-support measure rather than as a diagnostic instrument.

This distinction is also relevant from an intervention perspective. A recent systematic review and meta-analysis indicated that psychological interventions can reduce performance anxiety in athletes and performing artists, with particularly promising findings for cognitive-behavioral approaches, although the available evidence remains heterogeneous and effects may differ according to the type of anxiety assessed (Niering et al., 2023). Thus, identifying elevated anxiety-related indicators through structured monitoring may help guide further individualized assessment and, when appropriate, evidence-informed psychological intervention.

Sleep quality also contributed to the characterization of psychological profiles and to the cumulative psychological risk index. Poorer sleep was particularly evident in several female and lower-level groups. This finding is relevant because sleep and psychological functioning are closely interrelated. Sleep contributes to emotional regulation, cognitive functioning, recovery, and adaptation to training and competition, whereas psychological distress may itself interfere with sleep onset, continuity, and perceived restfulness (Walsh et al., 2021). Research involving elite athletes has shown that poorer sleep is associated with greater symptoms of depression, anxiety, and stress, supporting the inclusion of sleep within broader psychological monitoring systems rather than considering it exclusively as an indicator of physical recovery (Facer-Childs et al., 2022). Competition schedules, late matches, travel, early training sessions, pre-competition arousal, and academic or personal demands may all contribute to the disruption of athletes’ sleep. However, because the present study was cross-sectional, it cannot determine whether poorer sleep preceded greater psychological distress, resulted from it, or reflected a reciprocal relationship.

In relation to the second specific objective, competitive participation was associated with psychological risk primarily among female players. Lower participation was related to less favorable risk distributions across several psychological indicators. Playing time may be associated with perceived competence, team status, opportunities for progression, coach feedback, and feelings of belonging or recognition. Players receiving fewer competitive opportunities may experience frustration, uncertainty regarding their role, or concerns about their future within the team. Conversely, players experiencing greater psychological distress may also be less likely to be selected or receive substantial playing time. The direction of this relationship cannot be established from the present cross-sectional data.

Emerging longitudinal evidence supports the relevance of considering participation and well-being as dynamically related variables. Madsen et al. (2025) examined Danish U17 elite football players across a competitive season and identified temporal associations between match involvement and psychological well-being. Their findings reinforce the importance of repeated monitoring and suggest that playing time may be meaningfully related to players’ psychological experiences, although causal direction remains uncertain.

Psychological risk distributions also varied according to playing position, particularly among female players, with forwards showing less favorable distributions across several indicators. Playing positions may involve different technical, tactical, interpersonal, and evaluative demands. One possible explanation is that positional roles differ in the visibility and criteria of performance evaluation. Forwards may be evaluated more directly through goals and other attacking outcomes, whereas goalkeepers and defenders may experience greater salience of individual errors and decisive events. Nevertheless, this interpretation remains speculative. The positional differences were not consistent across all psychological variables, and some positional subgroups were likely small. These findings should therefore be interpreted as exploratory patterns rather than as evidence that a particular playing position inherently entails greater psychological risk.

Differences in several psychological risk indicators were also observed according to living arrangements among female players. Importantly, the direction of the findings did not support the assumption that living away from the family home was necessarily associated with a less favorable profile. Instead, players living in the family home showed higher odds of being classified in the medium- or high-risk categories for several outcomes. This pattern may reflect differences in age, competitive level, personal autonomy, family relationships, or the type and perceived quality of social support available rather than a direct effect of residence itself. Social support has been associated with lower stress and more favorable mental health outcomes among athletes, including adolescent football players (Delfin et al., 2024). However, the present results cannot establish whether the observed differences were attributable to residence, family functioning, autonomy, or the quality of the support received.

Moreover, the category of other residence may include heterogeneous circumstances, such as living independently, sharing accommodation with teammates, or residing in housing organized by the club. These situations may involve very different levels of autonomy, social connection, and organizational support. Living arrangements should therefore not be interpreted causally and should be understood as a contextual marker that may interact with developmental, interpersonal, and sporting conditions.

Among male players, the associations between competitive participation, playing position, living arrangements, and psychological risk did not form a consistent pattern across the variables examined. This does not necessarily imply that contextual conditions are unimportant for male players. The absence of stable patterns may reflect differences in group composition, restricted variability, subgroup size, or the particular contextual indicators included. It may also be influenced by sex-related differences in the recognition and reporting of psychological distress. In elite sport, perceived stigma and pressure to maintain an image of strength, resilience, or emotional control may reduce the disclosure of psychological difficulties and affect self-report results (Gulliver et al., 2012; Rice et al., 2016). Comparisons between male and female players should therefore consider not only possible differences in psychological functioning, but also potential differences in symptom recognition, interpretation, and reporting.

Regarding the third, exploratory objective, some differences in psychological risk indicators were observed according to years at the club and body-composition status, operationalized through team-specific tertiles of the sum of six skinfolds. However, these differences were not consistent across psychological indicators or between male and female players. Time at the club may reflect several overlapping processes, including familiarity with the sporting environment, adaptation to organizational demands, accumulated competitive experience, perceived stability, and prolonged exposure to selection and evaluation processes. Nevertheless, years at the club are also likely to be associated with age, competitive category, selection history, and sporting trajectory, making their independent contribution difficult to determine.

Similarly, the observed differences according to skinfold tertiles should not be interpreted as evidence of a direct relationship between body composition and psychological risk. Body-composition indicators may overlap with age, biological maturation, playing position, training status, nutritional practices, injury history, and perceived pressure related to physical appearance or sporting performance. Because these factors were not examined simultaneously, the present findings should be considered hypothesis-generating rather than confirmatory. Overall, the results concerning years at the club and body-composition status support cautious further investigation but do not provide evidence that either factor independently determines psychological functioning or psychological risk.

4.1. Practical Implications

From an applied perspective, the findings support the value of systematic psychological assessment within elite football organizations. The Psycholight protocol integrates performance-related psychological characteristics, perfectionism, anxiety, depressive symptoms, stress, and sleep quality, thereby avoiding an exclusively symptom-focused or performance-focused interpretation of the player. Its cumulative psychological risk index may facilitate the organization of information from several instruments and help identify players or teams that could benefit from additional assessment, follow-up, or preventive support. This approach is consistent with recommendations promoting early identification and the integration of mental health assessment into routine athlete-support systems (Gouttebarge et al., 2021; Olmedilla-Zafra & García-Mas, 2023).

Nevertheless, a cumulative score necessarily simplifies multidimensional information. The index should therefore be interpreted together with the scores of its individual components, the athlete’s circumstances, and professional judgment. It should not be used as a diagnostic instrument or as a direct predictor of performance, injury, or clinical disorder. Its primary value lies in supporting structured screening, identifying potentially relevant patterns, and guiding decisions about whether further individualized assessment is warranted.

The results also suggest that psychological monitoring should not rely exclusively on comparisons between male and female teams or between competitive levels. Team-specific and subgroup-specific patterns may be more informative for applied decision-making. Particular attention may be warranted when unfavorable indicators converge across psychological distress, sleep quality, competitive participation, and contextual conditions. In such cases, multidisciplinary interpretation involving psychologists, medical staff, coaches, and performance professionals may improve the interpretation of the findings within the athlete’s specific context while preserving confidentiality.

4.2. Limitations

Several limitations should be considered. First, the cross-sectional design precludes conclusions regarding temporal sequence or causality. The differences observed between teams may reflect age, selection processes, competitive experience, current sporting circumstances, or other unmeasured factors.

Second, all participants belonged to a single elite football academy. This provides a relatively consistent organizational context but limits generalization to other clubs, competitions, countries, or sporting structures.

Third, the broad age range and close relationship between age and competitive level make it difficult to separate developmental effects from those associated with competitive status. Some of the differences attributed to competitive level may therefore also reflect age-related or developmental processes.

Fourth, the instruments were based on self-report and may have been influenced by recall processes, response interpretation, perceived confidentiality, and social desirability. Socially desirable responding may be particularly relevant when athletes believe that acknowledging psychological distress could influence how they are perceived within the organization (van de Mortel, 2008). The results should therefore be interpreted as athletes’ reported perceptions and symptoms rather than as objective or diagnostic assessments of psychological functioning.

Fifth, several contextual comparisons involved subdividing the sample according to sex, position, team, and other characteristics, which may have resulted in small subgroup sizes and imprecise estimates. Although the Benjamini–Hochberg procedure was applied to reduce the false discovery rate, the number of comparisons and the exploratory nature of these analyses should still be considered when interpreting isolated significant findings.

Sixth, relevant explanatory variables such as injury status, accumulated training and competition load, academic demands, contract status, perceived coach support, family support, previous mental health history, and access to psychological services were not included.

Finally, the psychological risk index combines scores from instruments assessing different constructs and using different scoring systems. Its use in applied settings requires further examination of its construct validity, classification accuracy, sensitivity to change, and prospective association with relevant psychological and sporting outcomes.

4.3. Future Research

Longitudinal designs involving repeated assessments across the season are needed to determine how psychological profiles and sleep quality change in response to selection decisions, competitive participation, transitions between teams, injuries, and variations in training demands. Such designs would also help clarify the direction of the associations observed between playing time, sleep quality, and psychological distress.

Research involving multiple academies, clubs, competitions, and countries would help determine whether the patterns observed in the present study are specific to this organization or can be replicated across different football systems. Multilevel analyses could further distinguish variability attributable to individual players, teams, competitive levels, and organizational environments.

More detailed contextual measures should also be incorporated, including perceived coach support, social support, family functioning, satisfaction with living arrangements, academic or occupational demands, contract security, injury history, and access to psychological care. Among female players, it may also be relevant to examine additional health and contextual factors that could contribute to psychological well-being without assuming that sex itself is the primary explanatory variable.

The inclusion of clinical interviews, objective or behavioral measures of sleep, and information collected by multidisciplinary support teams could strengthen the interpretation of self-report findings. Further research should also evaluate the psychometric and practical performance of the psychological risk index, including its reliability, validity, sensitivity to change, and utility for identifying athletes who may benefit from additional assessment or support.

5. Conclusions

Psychological profiles, sleep quality, and psychological risk were not uniformly distributed across teams within the elite football academy examined. The hypothesis that these variables would differ according to sex and competitive level was generally supported, whereas the expectation that higher-level teams would consistently display more favorable psychological profiles was only partially confirmed.

Some female and lower-level teams showed a greater concentration of less favorable psychological indicators, although the observed patterns were heterogeneous and context-dependent. Competitive participation, playing position, and living arrangements were also associated with psychological risk, particularly among female players, whereas findings concerning years at the club and body-composition status were less consistent.

Overall, the findings support systematic and multidimensional psychological monitoring in elite football, provided that risk classifications are interpreted as non-diagnostic indicators of potential psychological vulnerability and considered alongside each athlete’s developmental, competitive, and personal context.