Anxiety symptoms are common among adolescents. A well-validated and easy-to-use tool is indispensable to measure and detect anxiety for timely interventions. The Generalized Anxiety Disorder Scale-7 item (GAD-7) is a self-report scale used to measure the severity of anxiety and has been validated in adult populations, but psychometric properties of the GAD-7 remained rarely tested in adolescents. The study aimed to investigate the reliability and validity of the GAD-7 in Chinese adolescents. Sex- and age-specific analyses were conducted in a large sample of adolescents (n = 67,281, aged 10–17 years). Our results showed that the GAD-7 scores were higher in female and older adolescents. The GAD-7 presented good internal consistency and a unidimensional structure across sex- and age-specific groups. The GAD-7 scores were significantly correlated with the scores of the Patient Health Questionnaire-9 item (PHQ-9, a self-reported scale to measure depression symptoms) in all subgroups, indicating acceptable criterion validity. In conclusion, the GAD-7 is a scale with good psychometrics and can serve as a tool for anxiety screening in Chinese adolescents at the populational level.

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With the continuous improvement of living standards, the level of physical development of adolescents has improved significantly. The physical functions and healthy development of adolescents are relatively slow and even appear to decline. This paper proposes a novel data mining algorithm based on big data for monitoring of adolescent student’s physical health to overcome this problem and enhance young people’s physical fitness and mental health. Since big data technology has positive practical significance in promoting young people’s healthy development and promoting individual health rights, this article will implement commonly used data mining algorithms and Hadoop/Spark big data processing. The algorithm on different platforms verified that the big data platform has good computing performance for the data mining algorithm by comparing the running time. The current work will prove to be a complete physical health data management system and effectively save, process, and analyze adolescents’ physical test data.

Large-scale sports events with high-level competition as the main content will have a great impact on the host city whether from the economic level or from the social level. With the improvement of human civilization, people realize that the holding of large-scale sports events not only has a positive impact on the economy and society but also brings some negative effects, such as waste of resources and environmental pollution, which have attracted the attention of the government and investors. Therefore, how to scientifically, comprehensively, and reasonably evaluate large-scale sports events, especially the accurate evaluation of their economic and social effects, has become the focus of attention. The evaluation of large-scale sports events mainly includes two levels: economic and social. Through the specific analysis of the evaluation content and the weight calculation of the evaluation index, the overall optimization of the evaluation of large-scale sports events is realized, and the reference experience is provided for the holding and evaluation of large-scale sports events in the future. Based on this, this article proposes a sports event evaluation and classification method based on the deep neural network. Firstly, on the basis of literature review and field investigation, the evaluation index system of sports events is established. Deep learning models have strong fitting power and robustness and have been applied to many real-world tasks. Then the deep neural network is used to evaluate the holding effect of sports events. The experimental results show that the model has high evaluation accuracy and is of great significance to the supervision and guidance of sports events.