Nowadays, the applications of the triboelectric nanogenerator in sensing and monitoring sports experience a blooming prosperity. Here, we report a flexible and lightweight triboelectric nanogenerator (FL-TENG) made of hydrogel electrodes, polytetrafluoroethylene (PTFE), PDMS, and polyurethane (PU). Based on the triboelectric effect, the FL-TENG can work as a self-powered sensor attaching to taekwondo protective gear, which can be used to monitor athletes’ competition performance and improve the fairness of the competition. In addition, the FL-TENG can drive micro-wireless devices for wireless transmitting sports data during the competition in real time. This kind of sustainable green self-powered sensor provides a new path for the field of sports competition monitoring.

A portable and flexible self-powered biosensor based on ZnO nanowire arrays (ZnO NWs) and flexible PET substrate has been designed and fabricated for real-time monitoring in swimming. Based on the piezoelectric effect of polar ZnO NWs, the fabricated biosensor can work in both air and water without any external power supply. In addition, the biosensor can be easily attached to the surface of the skin to precisely monitor the motion state such as joint moving angle and frequency during swimming. The constant output piezoelectric signal in different relative humidity levels enables actual application in different sports, including swimming. Therefore, the biosensor can be utilized to monitor swimming strokes by attaching it on the surface of the skin. Finally, a wireless transmitting application is demonstrated by implanting the biosensor in vivo to detect angiogenesis. This portable and flexible self-powered biosensor system exhibits broad application prospects in sport monitoring, human–computer interaction and wireless sport big data.

Background: Regular physical activity is an effective means to enhance university students’ subjective well-being. However, current research needs to understand how physical activity enhances the subjective well-being of Chinese university students. Therefore, the study investigated the mechanism of physical activity’s impact on university students’ subjective well-being and the mediating roles of cognitive reappraisal and resilience in this mechanism. Methods: The physical activity scale, subjective well-being scale, cognitive reappraisal scale and resilience scale were used to investigate 1350 university students, and the relationship between physical activity, cognitive reappraisal, resilience and university students’ subjective well-being was verified through correlation analysis, regression analysis and a Bootstrap method. Results: (1) There is a significant positive correlation between physical activity, cognitive reappraisal, resilience and university students’ subjective well-being (p < 0.01); (2) physical activity, cognitive reappraisal and resilience all have a significant positive effects on university students’ subjective well-being (p < 0.01); (3) cognitive reappraisal and resilience have significant mediating roles in the process of physical activity affecting university students’ well-being, with mediating-effect values of 0.052 and 0.285; (4) the chain-mediating role of cognitive reappraisal and resilience in the process of physical activity affecting university students’ well-being is significant, with the chain-mediating effect value of 0.062. Conclusion: Promoting university students’ participation in physical activity not only directly enhances university students’ subjective well-being but also indirectly improves university students’ subjective well-being through cognitive reappraisal and resilience.

With the rapid development of the Internet of Things (IoTs), big data analytics has been widely used in the sport field. In this paper, a light-weight, self-powered sensor based on a triboelectric nanogenerator for big data analytics in sports has been demonstrated. The weight of each sensing unit is ~0.4 g. The friction material consists of polyaniline (PANI) and polytetrafluoroethylene (PTFE). Based on the triboelectric nanogenerator (TENG), the device can convert small amounts of mechanical energy into the electrical signal, which contains information about the hitting position and hitting velocity of table tennis balls. By collecting data from daily table tennis training in real time, the personalized training program can be adjusted. A practical application has been exhibited for collecting table tennis information in real time and, according to these data, coaches can develop personalized training for an amateur to enhance the ability of hand control, which can improve their table tennis skills. This work opens up a new direction in intelligent athletic facilities and big data analytics.

Purpose: The purpose of this study was to investigate the association between moderate to vigorous physical activity (MVPA), sedentary behavior, fruit and vegetable intake, and the risk of sarcopenia among older Chinese adults. Methods: This cross-sectional study enrolled 5418 older Chinese adults who participated in the Study on Global Aging and Adult Health (SAGE). Participants reported information about their physical activity, sedentary behavior, and dietary habits (fruit and vegetable intake). Sarcopenia was defined as the presence of low skeletal muscle mass and either a slow gait speed or weak handgrip strength. A multiple logistic regression model was employed to determine the relationship between MVPA, sedentary behavior, fruit and vegetable intake, and the risk of sarcopenia. Results: Only 32.63% of participants met all three recommendations (for MVPA, sedentary behavior, and fruit and vegetable intake). Compared with meeting none of the recommendations, meeting all three recommendations was associated with a lower risk of sarcopenia (OR = 0.63, 95% CI = 0.41–0.98). Moreover, meeting the recommendation for sufficient fruit and vegetable intake (OR = 0.69, 95% CI = 0.58–0.83), MVPA and fruit/vegetable intake (OR = 0.67, 95% CI = 0.52–0.86), and sedentary behavior and fruit/vegetable intake (OR = 0.69, 95% CI = 0.48–0.98) was associated with a lower risk of sarcopenia. Conclusion: Our findings indicate that in this large representative sample of older Chinese adults, meeting lifestyle recommendations for MVPA, sedentary behavior, and fruit and vegetable intake protected against sarcopenia.

Sarcopenic obesity (SO) is characterized by concomitant sarcopenia and obesity and presents a high risk of disability, morbidity, and mortality among older adults. However, predictions based on sequential neural network SO studies and the relationship between physical fitness factors and SO are lacking. This study aimed to develop a predictive model for SO in older adults by focusing on physical fitness factors. A comprehensive dataset of older Korean adults participating in national fitness programs was analyzed using sequential neural networks. Appendicular skeletal muscle/body weight was defined as SO using an anthropometric equation. Independent variables included body fat (BF, %), waist circumference, systolic and diastolic blood pressure, and various physical fitness factors. The dependent variable was a binary outcome (possible SO vs normal). We analyzed hyperparameter tuning and stratified K-fold validation to optimize a predictive model. The prevalence of SO was significantly higher in women (13.81%) than in men, highlighting sex-specific differences. The optimized neural network model and Shapley Additive Explanations analysis demonstrated a high validation accuracy of 93.1%, with BF% and absolute grip strength emerging as the most influential predictors of SO. This study presents a highly accurate predictive model for SO in older adults, emphasizing the critical roles of BF% and absolute grip strength. We identified BF, absolute grip strength, and sit-and-reach as key SO predictors. Our findings underscore the sex-specific nature of SO and the importance of physical fitness factors in its prediction.