To explore the reasons for low levels of physical activity in obese/overweight children and adolescents and to propose appropriate strategies to promote their physical activity (PA). This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines by searching and analyzing the literature of studies related to physical activity in obese/overweight children and adolescents published between January 2003 and January 2023 in Web of Science, Scopus, and PubMed databases. A total of 31 relevant studies were included for analysis, of which 16 were quantitative and 15 qualitative. According to these studies, the PA of obese/overweight children and adolescents is mainly constrained by negative factors: Individual, interpersonal, and environmental. Among these factors, low levels of individual motivation and psychological sensitivity and vulnerability, lack of family support, negative social feedback, insufficient protection from government policies, and inadequate support from the built environment are the main reasons that constrain their PA. The promotion of PA in obese/overweight children and adolescents, who are subject to more constraints at all levels, requires a system of security that involves the government, the community, the school, and the family to address the problems they encounter and enhance the sustainability of engagement in PA.

To solve the problem of real-time arrhythmia classification, this paper proposes a real-time arrhythmia classification algorithm using deep learning with low latency, high practicality, and high reliability, which can be easily applied to a real-time arrhythmia classification system. In the algorithm, a classifier detects the QRS complex position in real time for heartbeat segmentation. Then, the ECG_RRR feature is constructed according to the heartbeat segmentation result. Finally, another classifier classifies the arrhythmia in real time using the ECG_RRR feature. This article uses the MIT-BIH arrhythmia database and divides the 44 qualified records into two groups (DS1 and DS2) for training and evaluation, respectively. The result shows that the recall rate, precision rate, and overall accuracy of the algorithm’s interpatient QRS complex position prediction are 98.0%, 99.5%, and 97.6%, respectively. The overall accuracy for 5-class and 13-class interpatient arrhythmia classification is 91.5% and 75.6%, respectively. Furthermore, the real-time arrhythmia classification algorithm proposed in this paper has the advantages of practicability and low latency. It is easy to deploy the algorithm since the input is the original ECG signal with no feature processing required. And, the latency of the arrhythmia classification is only the duration of one heartbeat cycle.

Pulse signals are widely used to evaluate the status of the human cardiovascular, respiratory, and circulatory systems. In the process of being collected, the signals are usually interfered by some factors, such as the spike noise and the poor-sensor-contact noise, which have severely affected the accuracy of the subsequent detection models. In recent years, some methods have been applied to processing the above noisy signals, such as dynamic time warping, empirical mode decomposition, autocorrelation, and cross-correlation. Effective as they are, those methods are complex and difficult to implement. It is also found that the noisy signals are tightly related to gross errors. The Chauvenet criterion, one of the gross error discrimination criterions, is highly efficient and widely applicable for being without the complex calculations like decomposition and reconstruction. Therefore, in this study, based on the Chauvenet criterion, a new pulse signal preprocessing method is proposed, in which adaptive thresholds are designed, respectively, to discriminate the abnormal signals caused by spike noise and poor-sensor-contact noise. 81 hours of pulse signals (with a sleep apnea annotated every 30 seconds and 9,720 segments in total) from the MIT-BIH Polysomnographic Database are used in the study, including 35 minutes of poor-sensor-contact noises and 25 minutes of spike noises. The proposed method was used to preprocess the pulse signals, in which 9,684 segments out of a total of 9,720 were correctly discriminated, and the accuracy of the method reached 99.63%. To quantitatively evaluate the noise removal effect, a simulation experiment is conducted to compare the Jaccard Similarity Coefficient (JSC) calculated before and after the noise removal, respectively, and the results show that the preprocessed signal obtains higher JSC, closer to the reference signal, which indicates that the proposed method can effectively improve the signal quality. In order to evaluate the method, three back-propagation (BP) sleep apnea detection models with the same network structure and parameters were established, respectively. Through comparing the recognition rate and the prediction rate of the models, higher rates were obtained by using the proposed method. To prove the efficiency, the comparison experiment between the proposed Chauvenet-based method and a Romanovsky-based method was conducted, and the execution time of the proposed method is much shorter than that of the Romanovsky method. The results suggest that the superiority in execution time of the Chauvenet-based method becomes more significant as the date size increases.

Objectives: While many studies have shown that student-athletes are a stigmatized group and topics related to them have received increasing attention, few studies have focused on the current situation of Chinese sports students (tiyu sheng) with the same dual identity as student-athletes. Thus, this study aims to understand and recognize the negative perceptions that students with sports identities experience in the Chinese educational and cultural context and the impact of these perceptions on them.Methods: This qualitative study used an interpretive description methodology to collect data through semi-structured, in-depth interviews conducted by the researcher with 11 sports students, 4 regular students, 3 teachers, and 2 parents. Then, the interview data was analyzed using reflective thematic analysis.Results: Three themes were eventually extracted: (1) The onset and development of stigma, (2) Stigma factors in the spotlight, and (3) the Response to stigmatization by sports students.Conclusion: Findings indicated that sports students are subjected to various stigmas under the negative filters of society. The stigmas suffered by sports students vary across school years and levels, and their perceptions of stigmas may also change over time and in different environments. In addition, being subjected to stereotype threat and self-stigma may have hurt some participants. The study’s results also provide evidence that the Internet plays a vital role in exacerbating the development of stigmatization among sports students, which needs to be determined by further research. These findings will contribute to subsequent research on the de-stigmatization of sports students.

The rating of perceived exertion (RPE) and surface electromyography (sEMG) describe exercise intensity subjectively and objectively, while there has been a lack of research on the relationship between them during dynamic contractions to predict exercise intensity, comprehensively. The purpose of this study was to establish a model of the relationship between sEMG and RPE during dynamic exercises. Therefore, 20 healthy male subjects were organized to perform an incremental load test on a cycle ergometer, and the subjects’ RPEs (Borg Scale 6–20) were collected every minute. Additionally, the sEMGs of the subjects’ eight lower limb muscles were collected. The sEMG features based on time domain, frequency domain and time–frequency domain methods were extracted, and the relationship model was established using Gaussian process regression (GPR). The results show that the sEMG and RPE of the selected lower limb muscles are significantly correlated (p < 0.05) but that they have different monotonic correlation degrees. The model that was established with all three domain features displayed optimal performance and when the RPE was 13, the prediction error was the smallest. The study is significant for lower limb muscle training strategy and quantification of training intensity from both subjective and objective aspects, and lays a foundation for sEMG further applications in rehabilitation medicine and sports training.