To understand human behaviors, action recognition based on videos is a common approach. Compared with image-based action recognition, videos provide much more information, reducing the ambiguity of actions. In the last decade, many works focus on datasets, novel models and learning approaches have improved video action recognition to a higher level. However, there are challenges and unsolved problems, in particular in sports analytics where data collection and labeling are more sophisticated, requiring people with domain knowledge and even sport professionals to annotate data. In addition, the actions could be extremely fast and it becomes difficult to recognize them. Moreover, in team sports like football and basketball, one action could involve multiple players, and to correctly recognize them, we need to analyze all players, which is relatively complicated. In this paper, we present a survey on video action recognition for sports analytics. We introduce more than ten types of sports, including team sports, such as football, basketball, volleyball, hockey and individual sports, such as figure skating, gymnastics, table tennis, tennis, diving and badminton. Then we compare numerous existing frameworks for sports analysis to present status quo of video action recognition in both team sports and individual sports. Finally, we discuss the challenges and unsolved problems in this area and to facilitate sports analytics, we develop a toolbox using PaddlePaddle, which supports football, basketball, table tennis and figure skating action recognition.

What is already known about this topic? Injury is a significant public health issue, particularly among the elderly population. However, the extent of this problem varies significantly based on age, gender, and geographic location. What is added by this report? This study aims to examine the changing patterns of injury mortality rates in China over a 35-year period and assess the age-period-cohort effects on mortality trends. What are the implications for public health practice? This study examines the evolving patterns of injury mortality in the elderly population and identifies potential high-risk groups. The findings offer valuable insights for informing injury prevention policies.

Aims: To make time series analysis on the 29-year (1985−2014) trends in height among Han Students Aged 7-18 in China and predict their future height from the perspective of interdisciplinary studies of demography and physical education. Methods: The data were from the findings of seven cross-sectional surveys from the Chinese National Survey on Students’ Constitution and Health (CNSSCH). The mean, standard error, variance analysis, trend test, F test, T test, development growth and growth rate were used to make a descriptive analysis of height trends. The ARIMA model in the time series analysis was applied to predict the height development of Han students in 2024. Results: The height of students aged 7-18 substantially increased from 1985 to2014. The mean of the height of boys increased by 3.99 cm, 8.01 cm, 8.31 cm, 8.63 cm, 9.81 cm, 11.62 cm, 10.38 cm, 9.22 cm, 7.50 cm, 5.59 cm, 4.51 cm, and 3.79 cm, respectively. The mean of the height of girls increased by 6.66 cm, 7.36 cm, 8.00 cm, 8.84 cm, 9.60 cm, 8.66 cm, 5.57 cm, 4.65 cm, 3.95 cm, 3.32 cm, 2.87 cm and 3.32 cm, respectively. The increasing disparity of the sex differential in the mean height was also observed. From 1985 to 2014, the mean of height difference between boys and girls aged 7-18 had been increased constantly. Their mean height differences were 3.27 cm, 4.03 cm, 4.64 cm, and 5.13 cm, respectively in 1985, 1995, 2005, and 2014. A narrowing of the urban−rural differential in the mean height was observed. In 1985, on average, urban boys aged 7-18 were 4.18cm higher than rural boys in the same age group. And the height differences between urban and rural boys in the same age group were 3.58 cm, 3.18 cm and 2.41 cm, respectively in 1995, 2005 and 2014. According to comparison results, the mean height of urban girls was greater than that of rural girls, and the height difference between urban and rural girls had been constantly narrowed as well. The results of 4 comparisons showed that the height differences between urban and rural girls were 3.68 cm, 3.12 cm, 2.62 cm, and 1.98 cm, respectively. Conclusions: There was a general increase in the height of Chinese Han students aged 7-18 in the past 29 years and difference between sex, rural-urban and age have been observed. In 2024, the height of students will continue to grow.

Becoming more and more serious of such problems as acceleration of aging and high medical burden of the elderly in China, research on the influence of physical activity on medical expenditure in the elderly has attracted a lot of attention. In this work, Tobit model is established based on the data from China Health and Retirement Longitudinal Study in 2015 and Behavioral Model of Health Services Use theoretical framework. Compared with the inactive elderly, the medical expenditure of the elderly who participate in the level 4, 3, 2 physical activity decreasing step by step. After involving need factors in the model, the influence of physical activity on medical expenditure varies from significant to insignificant. The medical expenditure of the elderly who meet the physical activity amount is negatively correlated with the physical activity amount, and is lower than that of the elderly who don’t meet physical activity amount. Physical activity will not directly affect medical expenditure, but will affect medical expenditure through chronic diseases and self-rated health status. Our results provide theoretical analysis and empirical test for enriching physical activity guidelines and Behavioral Model of Health Services Use in the elderly in China.

This study investigates how elite coaching cognition in Chinese table tennis has been reshaped by the ABS plastic ball era, not merely in technical outcomes but in the deeper logic of tactical and developmental practice. Using interpretive phenomenological analysis (IPA), we conducted an in-depth interview with Liu Guozheng, head coach of the Chinese men's national team, to capture how systemic disruption was experienced and acted upon. Findings reveal four interrelated domains of adaptation. First, tactical emphasis shifted from early-stroke aggression toward sustained rally control, with athletes trained to delay pressure and cultivate “silent control” across extended rallies. Second, psychological preparation was reframed as emotional regulation and decision resilience, illustrated by resetting routines that enabled clarity under prolonged rallies. Third, ecological training design was used to simulate uncertainty and cognitive load, embedding perception-driven drills where the “right moment to act” was intentionally obscured. Finally, talent selection criteria evolved to emphasize cognitive elasticity, reflecting a broader shift from fixed traits toward developmental potential. These domains did not emerge as abstract categories but as lived responses to systemic change. Together, they highlight adaptive expertise as a multi-layered process—spanning tactical reasoning, psychological stability, ecological training design, and future-oriented talent logic. By grounding theoretical insight in practitioner judgment, this study offers an ecologically valid account of how elite coaching negotiates performance disruption, with implications for broader high-performance contexts undergoing structural transitions.