Can psychological characteristics, football experience, and player status predict state anxiety before important matches in Danish elite-level female football players?

Monitoring Readiness to Train and Perform in Female Football: Current Evidence and Recommendations for Practitioners

PURPOSE

Monitoring player readiness to train and perform is an important practical concept in football. Despite an abundance of research in this area in the male game, to date, research is limited in female football. The aims of this study were, first, to summarize the current literature on the monitoring of readiness in female football; second, to summarize the current evidence regarding the monitoring of the menstrual cycle and its potential impact on physical preparation and performance in female footballers; and third, to offer practical recommendations based on the current evidence for practitioners working with female football players.

CONCLUSIONS

Practitioners should include both objective (eg, heart rate and countermovement jump) and subjective measures (eg, athlete-reported outcome measures) in their monitoring practices. This would allow them to have a better picture of female players' readiness. Practitioners should assess the reliability of their monitoring (objective and subjective) tools before adopting them with their players. The use of athlete-reported outcome measures could play a key role in contexts where technology is not available (eg, in semiprofessional and amateur clubs); however, practitioners need to be aware that many single-item athlete-reported outcome measures instruments have not been properly validated. Finally, tracking the menstrual cycle can identify menstrual dysfunction (eg, infrequent or irregular menstruation) that can indicate a state of low energy availability or an underlying gynecological issue, both of which warrant further investigation by medical practitioners.

Recognition and Analysis of Sports on Mental Health Based on Deep Learning

This paper presents the purpose of sport recognition of mental health for users and analyzes and studies the recognition of mental health by sports based on deep learning. The recognition model of sport mental health state composed of data layer, logic layer and display layer is built. After fusing human health data with deep learning algorithm, the feature of human health mutual information is extracted, the feature into the recognition model of mental health state is inputted, and the recognition results of sport mental health mode after forward and reverse operation are outputted. The recognition data of sports on mental health status are obtained, which correspond to the link flowing through during multi-level transmission, calibrate the multi-level transmission point, and fuse and process the recognition information of sports on mental health status. The experimental results show that the loss value of the research method when analyzing the effect of sports on mental health enhancement is the smallest, the output result is reliable, can effectively improve the body mass index (BMI) of the human body, has the most controllable amount of data, and has good performance.