COVID-19: Exercise Behavior of College Students Communicating at Home

Abstract

Objectives: During the outbreak and spread of COVID-19, the extension of college students' time spent studying at home changed their physical exercise behavior and affected the physical activity behavior of the whole family. Methods: A questionnaire survey was conducted among 1,582 college students using a specific measurement scale. A total of 305 urban college students were selected as research subjects. SPSS24.0 and AMOS24.0 were used for statistical analysis. Results: During the COVID-19 transmission period, the pair correlation coefficients of exercise behavior, exercise attitude, and family exercise conditions were 0.63, 0.36, and 0.25, respectively. The influence on family exercise behavior is as follows: college students' exercise behavior (0.403), family exercise support (0.329), and college students' exercise attitude (0.257). The most significant influence on family exercise support is college students' exercise attitude (0.509). The regression model of family exercise behavior standardization had 0.74 and 0.44 explanatory power to family exercise behavior and family exercise support, respectively. Conclusion: The individual-level interventions were assessed by considering the interaction between individual exercise behavior and individual factors. In addition, the exercise environment exhibited a regulatory role and should be controlled. At the interpersonal level, the communication of the college students regarding exercise behavior was bidirectional. Exercise support for family members is an important factor affecting two-way communication and has a significant effect. With the development of the exercise behavior theory, the interaction between individuals is the origin of the spread of group behavior. The data suggest that instead of one-way influence two-way influence mechanisms should be proposed to assess the transformation from the individual to group exercise behavior. Strengths and Limitations of this Study: Citation impact metrics, WeChat and QQ followers can be measured with relatively high accuracy. Structural equation model analysis can effectively estimate hypotheses and test causality. The analysis focused mainly on cities in the major signatories and did not cover rural areas.