Purpose: This study aims to identify potential risk factors associated with anxiety or depression and propose algorithms to predict anxiety and depression especially among university students.
Methods: We included and analyzed 881 university students from eight colleges in China in November 2020. Student’s basic information, lifestyles, sport habits, comorbidities, and mental health conditions were collected. Anxiety and depression were measured using the generalized anxiety disorder 7 (GAD-7) and the patient health questionnaire 9 (PHQ-9), respectively. A multiple linear regression analysis was used to assess the ability of 25 potential risk factors for predicting anxiety and depression, and significant risk factors were included in the algorithms.
Results: Of all the included students, 44.27% lived with mild or above anxious symptoms and 50.62% had mild or above depressive symptoms. According to the multiple linear regression model, grade levels (P< 0.01), member of college sports dance team (P=0.05), sedentary time (P=0.02), exercise frequency (P< 0.01), only child status (P=0.05), addiction of drinking (P< 0.01), and prefer eating vegetable (P< 0.01) were significantly associated with anxiety; grade levels (P< 0.01), member of college sports dance team (P< 0.01), sedentary time (P< 0.01), exercise frequency (P< 0.01), academic study period during free time (P=0.03), only child status (P< 0.01), addiction of drinking (P< 0.01), prefer eating vegetables (P< 0.01), and main types of drinking water (P< 0.01) were significantly associated with depression. Based on these significant factors, two algorithms were successfully developed, and two risk groups were created according to the algorithms.
Conclusion: The study proposed two algorithms to calculate anxiety and depression, respectively, which can be useful tools to identify students with different risk of anxiety or depression. Effective measures are warranted to improve student’s sport habits and healthy lifestyles in order to mitigate anxiety and depression, especially among students in the high risk group.
Keywords: students, public health, anxiety, depression, risk factors, algorithms