The correlation between sleep quality and performance in football athlete

The relationship between perfectionism and sleep quality in athletes: the mediating role of mental toughness

Recent research suggested that mental toughness might play a mediating role in the relationship between multidimensional perfectionism and sleep quality. This study sought to examine the associations between multidimensional perfectionism, mental toughness, and sleep quality in young Chinese athletes, with a particular focus on investigating the potential mediating role of mental toughness in the relationship between multidimensional perfectionism and sleep quality. A total of 208 Chinese athletes (129 females and 79 males) aged from 15 to 29 (Mage = 19.9, SD = 2.52) were included. Structural equation modeling (SEM) was used to analyze the potential mediating effect. The results revealed that perfectionism striving was not associated with sleep quality, whereas perfectionism concerning emerged as a negative predictor of sleep quality. Mental toughness could positively predict sleep quality. The mediation analysis demonstrated that mental toughness partially mediated the relationship between perfectionism concerning and sleep quality. This study revealed that perfectionism concerning can reduce sleep quality, and mental toughness played a partial mediating role in the relationship between perfectionism concerning and sleep quality among Chinese young athletes. These findings suggest that enhancing athletes' mental toughness and reducing perfectionism concerning may be effective strategies for improving sleep quality.

The Influence of Employee Emotion Fluctuation on Service Performance: An Experience Sampling Data Analysis

Research on the relationship between emotions and job performance is ubiquitous, yet few scholars have examined the combined effects of different emotions. Drawing on the broaden-and-build theory and conservation of resources (COR) theory, we propose that employees’ daily emotion fluctuations (positive emotions vs. negative emotions) will affect their service performance in opposite directions. Furthermore, we propose these effects will be moderated by psychological [i.e., regulatory emotional self-efficacy (RESE)] and physiological (i.e., sleep quality) characteristics of the employees. Based on the experience sampling method (ESM), data (N = 810) obtained from 187 frontline employees of 35 bank branches over 18 consecutive days supports our hypotheses.

GI-SleepNet: A Highly Versatile Image-Based Sleep Classification Using a Deep Learning Algorithm

Sleep-stage classification is essential for sleep research. Various automatic judgment programs, including deep learning algorithms using artificial intelligence (AI), have been developed, but have limitations with regard to data format compatibility, human interpretability, cost, and technical requirements. We developed a novel program called GI-SleepNet, generative adversarial network (GAN)-assisted image-based sleep staging for mice that is accurate, versatile, compact, and easy to use. In this program, electroencephalogram and electromyography data are first visualized as images, and then classified into three stages (wake, NREM, and REM) by a supervised image learning algorithm. To increase its accuracy, we adopted GAN and artificially generated fake REM sleep data to equalize the number of stages. This resulted in improved accuracy, and as little as one mouse’s data yielded significant accuracy. Due to its image-based nature, the program is easy to apply to data of different formats, different species of animals, and even outside sleep research. Image data can be easily understood; thus, confirmation by experts is easily obtained, even when there are prediction anomalies. As deep learning in image processing is one of the leading fields in AI, numerous algorithms are also available.