The recent curriculum reform in China puts forward higher requirements for the development of physical education. In order to further improve students’ physical quality and motor skills, the traditional model was improved to address the lack of accuracy in motion recognition and detection of physical condition so as to assist teachers to improve students’ physical quality. First, the physical education teaching activities required by the new curriculum reform were studied with regard to the actual needs of China’s current social, political, and economic development; next, the application of artificial intelligence technology to physical education teaching activities was proposed; and finally, deep learning technology was studied and a human movement recognition model based on a long short-term memory (LSTM) neural network was established to identify the movement state of students in physical education teaching activities. The designed model includes three components: data acquisition, data calculation, and data visualization. The functions of each layer were introduced; then, the intelligent wearable system was adopted to detect the status of students and a feedback system was established to assist teaching; and finally, the dataset was constructed to train and test the designed model. The experimental results demonstrate that the recognition accuracy and loss value of the training model meet the practical requirements; in the algorithm test, the motion recognition accuracy of the designed model for different subjects was greater than 97.5%. Compared with the traditional human motion recognition algorithm, the designed model had a better recognition effect. Hence, the designed model can meet the actual needs of physical education. This exploration provides a new perspective for promoting the intelligent development of physical education.

Background: Mental fatigue largely influences technical performance in soccer, including offensive and defensive skills. However, these effects on technical performance among the soccer players have not yet been aggregated to be assessed systematically.Objective: The purpose of the review was to evaluate the impact of mental fatigue on soccer players' overall technical skills.Methods: Drawing on Web of Science, PubMed, Scopus, and EBSCOhost (CENTRAL and SPORTDicus), an in-depth search was conducted. PICOS established the eligibility criteria to select the studies as follows: (i) population—healthy soccer players; (ii) intervention—involving any mental-fatigue-prompted protocol; (iii) comparison—control conditions (active or passive without inducing mental fatigue); (iv) outcomes—technical performance (offensive and defensive skill); and (v) study design—randomized controlled trials.Results: A total of eight studies were qualified for inclusion in the systematic literature review. Overall, the results indicate that mental fatigue had significant effects on technical skills, including offensive and defensive skills. Specifically, there were significant effects on errors (ES = 0.977; p < 0.001), number of tackles (ES = −0.739; p = 0.005), and the percentage of successful tackles (ES = −0.628; p = 0.022), while there were no significant effects on the number of passes (ES = 0.240; p = 0.328), the percentage of accurate passing (ES = −0.008; p = 0.985), and the number of successful passes (ES = −0.322; p = 0.217).Conclusion: Overall, a significant effect of mental fatigue on the technical performance (e.g., tackles and errors) of soccer players was detected, while no significant effects on passing skills were detected. Future studies may consider investigating technical performance together with other important results (e.g., decision-making skills or internal load).Systematic Review Registration: https://inplasy.com/inplasy-2022-2-0008/, Inplasy protocol 202220008.

Postmenopausal women affected by overweight and obesity are susceptible to a variety of diseases due to inflammation. Exercise may reduce the risk of disease by attenuating low-grade chronic inflammation. We conducted a systematic review and meta-analysis to investigate the effects of exercise on inflammatory markers in postmenopausal women struggling with overweight and obesity. Literature as of May 2023 was searched from databases such as Cochrane, Embase, Pubmed, Web of Science, and EBSCO and English-language randomized controlled trials (RCTs) that meet the inclusion criteria were selected. Studies were included based on the following criteria: (A) Written in English; (B) RCTs; (C) Postmenopausal women impacted by overweight and obesity as research objects; (D) Outcome measurements include CRP, TNF-α, IL-6, and adiponectin; (E) Duration of the exercise intervention is eight weeks. A total of 34 articles and 2229 participants were included. Exercise can significantly reduce the level of C-reactive protein (CRP) (MD: -0.59, 95 % CI: -0.87 to -0.31, p < 0.00001), tumor necrosis factor-α (TNF-α) (MD: -0.65, 95 % CI: -0.94 to -0.35, p < 0.00001), interleukin-6 (IL-6) (MD: -0.48, 95 % CI: -0.75 to -0.21, p < 0.00001), and exercise can significantly increase the level of adiponectin (MD: 0.33, 95 % CI: 0.02 to 0.65, p = 0.04) in women impacted by overweight and obesity. These results suggest that exercise may be an effective intervention for reducing pro-inflammatory markers and increasing adiponectin in postmenopausal women impacted by overweight and obesity. The findings may provide clinicians and healthcare professionals with insights into the implementation of exercise programs for postmenopausal women living with overweight and obesity.

In this paper, the forest algorithm and the decision tree algorithm are mainly used to analyze students’ physical education information, course exam results, and student learning data and relevant feature attributes from the online teaching platform. We aim to generate decision trees using the decision tree algorithm for the purpose of generating classification rules, based on which we can find factors that are important to students’ physical education performance and form data basis for improving teaching quality to help teaching management and teachers improve teaching methods and adjust teaching strategies. We specifically achieved this objective by constructing a model for assessing the effectiveness of student teaching, the steps of which include data collection and preparation, data preprocessing (data cleaning, conversion, integration), model construction (algorithm training), and algorithm optimization, as well as realizing the simulation results of the model. At the same time, the importance of the relevant attributes of the model is analyzed, and some measures are proposed to improve the universities: the standard of physical education teaching and the corresponding strategies for improving teaching methods. The mainstream development environment is chosen to ensure the complete operation of the project system that integrates learning, operation, and evaluation. The sports virtual simulation experimental teaching system realized in this paper has good functionality, stability, and application benefits in operation and use.

The aim of this study was to compare the effects of jumping interval training (JIT) and running high-intensity interval training (HIIT) on the aerobic, anaerobic and jumping performances of youth female aerobic gymnasts. A randomized controlled study was conducted over an 8-week period, involving 73 youth female athletes (16.2 ± 1.3 years old) of aerobic gymnastics. The study comprised two experimental groups (JIT and HIIT) and a control group. Participants in the experimental groups engaged in two additional training sessions per week alongside their regular training regimen, while the control group followed their usual training routine. Before and after the intervention period, gymnasts were assessed for their performance in the countermovement jump test (CMJ), the specific aerobic gymnastics anaerobic test (SAGAT) and the 20-m multistage fitness test. Significant interactions time × group were found in SAGAT (p < 0.001; = 0.495), CMJ (p < 0.001; = 0.338) and 20-m multistage fitness test (p < 0.001; = 0.500). The time × group analysis post-intervention revealed significantly lower scores in SAGAT for the control group compared to the JIT (p = 0.003) and HIIT (p = 0.034). Additionally, significantly higher scores were observed for the JIT group in the CMJ test compared to the HIIT (p = 0.020) and control (p = 0.028) groups following the intervention. Finally, the 20 m multistage fitness test post-intervention revealed significantly lower scores for the control group compared to JIT (p < 0.001) and HIIT (p < 0.001). Both JIT and HIIT are recommended training strategies to adopt in aerobic gymnastics for significantly improving the aerobic and anaerobic performances of athletes. However, JIT may be particularly relevant to use as it offers additional benefits in improving vertical jumping performances.