In sports, elite athletes must make considerable sacrifices in the quest to improve their physical, technical, tactical, and psychological conditions, all of which help them achieve better performance (1). Even highly talented athletes do not deprive themselves of hard training and considerable preparation (5,7,24). Although sports performance is a multifactorial construct, historically it has been investigated from the perspective of a single discipline of sports science, such as sports physiology, the biomechanics of sport, and sports psychology (14).
In recent years, the psychological factors relating to sports performance have received particular attention, especially athletes' mood states (1,16,28). Mood states are regarded as one of the most important precompetitive predictive factors of sport performance (3,26), as certain mood states are believed to optimize athletes' performance, enabling them better management of the thoughts, feelings, and behaviors required of them before and during a competition (35). Indeed, precompetitive vigor and fatigue are 2 main psychological factors governing sports performance (47). More specifically, researchers have determined that a mood comprising high vigor and lower levels of tension, depression, anger, fatigue, and confusion is associated with optimal performance. This mood profile is called the “iceberg profile” (1). Precompetitive mood states are believed to be influenced by a variety of factors, such as the characteristics of the precompetitive period (e.g., the surrounding environment), whether athletes are going to be competing with strangers that are better ranked than them, any personal concerns that the athlete might have, contextual factors, and how important the competition is to athletes (8).
Sleep is another factor believed to be highly important for sports performance (34,46,48), as better sleep quality has strong physiological and emotional effects (20,23,32,37,48). Specifically, it appears to be an essential component of athletes' preparation and recovery (6,17,49) and influences their homeostasis and immune and neuroendocrine regulation (27,36).
Athletes who participate in competitions experience high levels of stress, which can alter mood and sleep patterns and compromise performance (11,44). Many athletes have reported poor sleep before important competitions (19), which can lead to higher stress levels, anger, and tension the following day; increased daytime sleepiness; and worse performance in the competition or game (6). Mood changes can also interfere with sports performance and negatively influence processes such as decision making (13,20), which are important for competitions. Even though there is widespread recognition of the importance of good sleep quality for sports performance and overall health, relatively little is known about the sleep patterns of athletes during sport competitions (25). Similarly, although there has been some research on differences in sleep patterns and mood between athletes of individual sports (6) and those of team sports (47), these differences have never been determined in Brazilian athletes.
Although, understandably, sleep and mood are interrelated, many questions about the direction and causation effects between sleep and mood remained unanswered (19). Therefore, the present study hypothesized that the quality of sleep is positively associated with moods, which in turn are associated with sporting performance in different sports modalities. By identifying this relationship, it may be possible to help coaches plan technical interventions or develop strategies to improve sleep quality in precompetitive athletes.
Experimental Approach to the Problem
We chose the study variables of sleep quality and precompetitive mood state to define the impact of these variables among athletes immediately before they begin competing; we considered this study design as highly ecologically valid and as having the potential to offer substantial contributions to the development of interventions for use by technicians. To this end, we chose rather brief measures of the 2 variables, which has been done in previous studies (33,52).
To investigate the influence of self-reported sleep quality and mood states on the sport performance of Brazilian athletes, we used the Brunel Mood Scale (BRUMS) and a question about the perceived quality of sleep. The question on self-reported sleep quality was “How would you evaluate the quality of your sleep in the past few days?” Participants rated their sleep quality on a Likert-type scale as follows: 1 = very bad, 2 = bad, 3 = normal, 4 = good, and 5 = excellent. We also recorded participants' age, sport modality (individual or team), and years of practice in their sports. Our use of self-reported sleep quality was based on previous studies, which also investigated the quality of sleep in this manner (22,33).
Moods can be used to predict the performance of athletes in different sports (6,28). In the present study, we used the BRUMS to evaluate mood states (46). The BRUMS was translated into Portuguese and validated in a Brazilian population by Brandt et al. (4). The instrument comprises 24 items rated on a 5-point scale (ranging from 0 = “no” to 4 = “extremely”); ratings reflect participants' mood at the time of evaluation. The BRUMS comprises 6 dimensions (Table 1), each consisting of 4 items. The total score of each dimension ranges from 0 to 16. The internal consistency values (Cronbach's alpha) of all 6 dimensions and the total scale were all greater than 0.85 in Brandt et al. (4). For the present study, the internal consistency of the total scale was 0.841, whereas those of the factors were as follows: anger, α = 0.65; confusion, α = 0.63; depression, α = 0.66; fatigue, α = 0.60; tension, α = 0.65; and vigor, α = 0.81.
Performance was determined by the summary or official report provided by the competition organizer; we classified it as a dichotomous variable (i.e., won or lost). We opted for the dichotomized variable of performance because previous studies had used this model (6,43,52).
Participants were 576 Brazilian competitive elite (45) athletes (mean age 22 years, SD = 6.8; range 13–47 years), of whom 404 were men (mean age 22 years, SD = 7) and 172 were women (mean age 21 years, SD = 6). A number of sports were represented in the sample (Table 2). All athletes were competing at the national or international level, with more than 5 years of accumulated practice. This study received the approval of the ethics committee of the State University of Santa Catarina. The athletes aged 18 or older and the parents or guardians of athletes under 18 years signed a consent form.
Before data collection, the researchers contacted the coaches to provide information on the objectives and research procedures and to request authorization to perform data collection during a competitive period. After obtaining authorization, all athletes were informed of the research. During data collection, which took place between February and September, 2011 up to 60 minutes before a participant's first day in the competition, athletes were approached by researchers and referred to a specific, quiet location inside the site of the competition wherein they completed the above instruments while under the supervision of 2 researchers. All data collection was standardized in terms of privacy conditions and supervision. Participants were asked to be as truthful as possible when completing the instruments. The athletes were instructed to answer the mood instrument in terms of their current feeling. The total data collection time (travel time to the place of collection and completion of instruments) was approximately 10 minutes for each participant.
The athletes completed the instruments at different times of the day, depending on the characteristics of their sport modality; for instance, the beginning of the sailing competition depended on weather conditions, and usually has its onset in the afternoon. In contrast, other individual or team sports started in the morning. To avoid possible influences of time of day, we attempted to standardize it.
The data are presented as means and standard deviations and in frequency tables. Before the data analysis, the normality of all variables was evaluated according to skewness and kurtosis. To compare participants' mood states by sex, sleep quality, practice time, sport modality, and performance, we used the Kruskal–Wallis test and Mann-Whitney U-test. Furthermore, we used logistic regression analysis to confirm the effects of mood states and sleep quality on the dichotomous performance variable. All statistical analyses were performed using SPSS version 21 (IBM Corporation, Armonk, NY, USA). The significance level was set at p ≤ 0.05.
Of the 576 athletes studied, the majority (294 [51%]) reported having good sleep quality (team sports, n = 171 [52.6%]; individual sports, n = 123 [49%]). There were no significant differences between men and women or between sports modalities (Table 3) in terms of sleep quality.
Regarding mood states, men significantly differed from women in scores on mental confusion, fatigue, and vigor (Table 4). Notably, individual sports athletes had significantly higher levels of stress and reduced vigor compared with those in team sports. Furthermore, participants with less practice time in their sport tended to have higher tension and mental confusion as well as lower vigor. Athletes who had won their competition bout tended to have lower tension and vigor compared with those who had lost. Perceived sleep quality had a direct relationship with mood state: higher vigor and lower tension, depression, anger, fatigue, and mental confusion were found among those with better perceived sleep quality (i.e., reported “good” or “great” on the sleep measure).
Logistic regression analysis (Table 5) was conducted to determine the relationships of performance with perceived sleep quality and mood states among the Brazilian elite athletes. The model containing both perceived sleep quality and mood states, compared with the model containing only one of these variables, had a significantly better fit to the data (χ2 = 24,325, degrees of freedom = 10, p ≤ 0.05). This suggests that these 2 variables have additive effects on performance. Nagelkerke's R2 was 0.056, indicating a moderate-to-weak relationship to the model. Notably, the overall variance in performance explained by the model was 60.8% (19.2% for winning and 88.2% for losing). The Wald test demonstrated (p ≤ 0.05) that poor perceived sleep quality and anger, tension, and vigor significantly contributed to the prediction of athletes' performance during the competition.
We used the formula [Exp(B) − 1] × 100 (14) to assist us in calculating the variance explained by the predictor variables. The results showed that athletes with poor sleep quality were almost thrice as likely (i.e., 173% as likely) to have poor performance as were those with good sleep quality. Furthermore, for every increase in tension, there was a corresponding increase of 10% in the chances of winning. On the other hand, decreasing vigor led to a 6% decrease in the chance of winning, whereas decreasing anger led to an 11% decrease.
Finally, the results of a Hosmer–Lemeshow test indicated that the model accurately predicted the values and was capable of generalization, as the p value was not significant (p = 0.382) (10).
This study discussed the relationship of 2 important factors in the field of sports psychology—perceive sleep quality and mood states—with performance among elite athletes (30,40). Sleep is an important predictor of various emotional constructs and physiological well being (9,12). For example, from a neurobiological perspective, sleep is important for the development and maintenance of healthy brain function (36). In sports, it is helpful for recovery during training periods and contributes to optimal physical and emotional condition among athletes (27,32).
Most of the athletes in this study showed good sleep quality. Sleep is widely recognized in the literature as being an important factor for athletes, especially with regard to performance (46). For instance, Poussel et al. (38) investigated subjective sleep quality among 137 elite athletes using the dichotomous variable of poor or sufficient sleep. Furthermore, Juliff et al. (24) found that 64% of studied athletes reported poor sleep quality on nights before a major competition. Notably, one of the first symptoms arising from poor sleep quality is reduced ability to deal with one's emotions; indeed, sleep-deprived individuals consistently show increased levels of depression, stress, anxiety, worry, frustration, irritability, diminished vigor, and lower confidence (50).
We found no significant differences in sleep quality by gender or sport modality (i.e., individual or team). This is in contrast to Leeder et al. (32), who showed that sleep quality differed among men and women—specifically, men had shorter sleep times (which they used as an indicator of sleep quality) than did women. Regarding sport modalities, however, Erlacher et al. (9), in investigating 632 German athletes from various sports, found that athletes from individual sports reported more sleep difficulties compared with team sports athletes. Nervousness and thoughts about the competition were the main causes of these sleep problems. Additionally, Lastela et al. (31), who found that athletes of individual sports tended to go to bed and wake up earlier when compared with athletes of team sports.
In relation to mood states, however, we noted differences between men and women in mental confusion, fatigue, and vigor, which corroborate the findings of other studies (5,52). There were also differences in mood states by sport modality and practice time. Considering our results and those of previous studies, we suggest that care for athletes suffering from stress before competitions should consider gender and sport modality.
As for the relationship between mood state and performance, we noted a significant difference only in mental confusion and tension between athletes who won their bout and those who lost it. This is in contrast to Zandi and Rad (53), wherein there were considerable differences in mood profiles between athletes who won and those who lost, particularly in mental confusion, vigor, tension, fatigue, and anger. Our results indicated that Brazilian elite athletes overall tend to have a profile similar to the renowned Iceberg profile proposed by Morgan. Furthermore, our results suggest that small changes in moods relate to better or worse performance among athletes.
Sleep quality has been shown to have a direct relationship with mood states (30) and sports performance (41). Our results support these previous findings: better sleep quality is associated with greater vigor and less tension, depression, anger, fatigue, and mental confusion. The effect of sleep loss before exercise may be detrimental to team-sport exercise. Sleep disruption has been found to be associated with an increased perception of negative mood states (42). Mah et al. (34) requested that the basketball players have as much extra sleep as possible, beyond the usual; after this, they found that players with extra sleep had faster sprint times, improved free-throw accuracy, greater vigor, and decreased fatigue.
Our regression analysis revealed that perceived sleep quality and 3 subscales of the BRUMS (vigor, tension, and anger) best predicted performance—specifically, around 88.2% of losing cases and 19.2% of winning cases could be predicted by these variables. Keikha et al. (26), using similar statistics, found that their model could predict up to 61% of winning cases and 26% of losing cases. Based on this, our model seems to be good for predicting negative results but seems ineffective in predicting the likelihood of winning.
In this study, we found that poor perceived sleep quality was associated with a 3-fold greater likelihood of the athlete losing their competition bout. These findings indicate the importance of sleep quality for sports performance and reaffirm the findings of previous studies (46). In examining the effects of sleep deprivation in weightlifters, Blumert et al. (2) observed no significant differences in tasks carried out among athletes; however, when analyzing the moods through the Profile of Mood States, they found significant differences in confusion, vigor, and fatigue, which are negatively influenced by sleep deprivation. A possible explanation for this effect is that reduced quantity and quality of sleep can disrupt the autonomic nervous system, thereby causing the athlete to present symptoms similar to overtraining as well as decreased immune system (12) and cognitive functioning (18). Juliff et al. (24) reported that most athletes are unaware of strategies to overcome poor sleep quality. This suggests the need for guidance for athletes and coaches in handling this type of situation.
As for mood states, athletes should find ways to maintain high levels of vigor, as lower levels of this mood state are associated with a decreased chance of victory. Furthermore, both higher levels of tension and anger seem to be beneficial to sports performance. These results accord with those of Keihka et al. (26). It is noteworthy that anger and tension have curvilinear effects on performance (28)—that is, very low and very high levels of these mood states can compromise performance, whereas moderate levels have a facilitating effect (39).
The results are relatively conclusive in highlighting that poor quality of sleep is associated with an increased probability of the athlete losing their competition bout. Thus, coaches, medics, psychologists, physiotherapists, and athletes might make use of various tools and techniques to identify and maintain these factors at levels optimal for each athlete (i.e., according to their needs, individual characteristics, and sport modality). Our results can be combined with recent findings in the same field: for instance, athletes who suffer from decreased or a loss of sleep, or who wake up too early to compete effectively, might benefit from a brief nap. This was noted by Waterhouse et al. (51), who found that a 30-minute nap improved athletes' performance. Coaches and fitness coaches should therefore modify their training plans to include practicing techniques for improving sleep during noncompetitive periods. This would therefore ensure that athletes could benefit from such techniques during competitive periods.
It must be mentioned, however, that competition outcomes are determined by multiple factors; as such, perceived sleep quality and mood states cannot fully explain competition outcomes. Furthermore, factors inherent in the competition itself might influence the results. Future studies should investigate psychological factors in a single competition and make use of more robust and detailed sleep instruments, as well as include sociocultural factors, to better explain the results.
No grant aid was received in conjunction with the present study, and no conflicts of interest are declared.
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