Correlation between music APP listening habits and depression tendency in college students based on SMOTEENN algorithm
10.16016/j.2097-0927.202312027
- VernacularTitle:基于SMOTEENN算法的音乐APP听歌习惯与大学生抑郁倾向的相关性研究
- Author:
Xinqiao HUANG
1
;
Hui ZHU
;
Hao QU
;
Yazhou WU
;
Qiuyue SONG
Author Information
1. 400038 重庆,陆军军医大学(第三军医大学)军事预防医学系军队卫生统计学教研室
- Keywords:
college students;
music APP;
listening habits;
depression tendency;
early warning model
- From:
Journal of Army Medical University
2024;46(23):2670-2680
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the influencing factors for tendency towards depression in college students having music listening habits with music APP,and develop a prediction model and further optimize it.Methods A total of 1 157 college students were subjected with convenient sampling and surveyed with questionaires between April and May 2023.Univariate analysis and logistic regression analysis were employed to identify the influencing factors.Then a prediction model was constructed based on these factors.SMOTEENN over-sampling algorithm was utilized to enhance the dataset and construct the prediction model.Results Logistic regression analysis revealed that female(OR=1.730,95％CI:1.257～2.396),senior grade(OR=2.649,95％CI:1.198～7.506),postgraduate grade(OR=2.041,95％CI:1.231～3.885),major in Science(OR=1.573,95％CI:1.052～2.350),listening for a duration of 0.5～2 h(OR=1.661,95％CI:1.011～2.695),music style of melancholy(OR=2.668,95％CI:1.701～4.226)and of nostalgia(OR=1.751,95％CI:1.086～2.837),and frequency of comments on 0～5％of songs(OR=2.938,95％CI:1.018～8.417)were independent risk factors for depressive tendency.Time since listening to music for 1～3 years(OR=0.547,95％CI:0.347～0.872),listening to music from 14:00 to 18:00(OR=0.375,95％CI:0.167～0.845)and 18:00 to 21:00(OR=0.313,95％CI:0.148～0.671),and preference for Chinese style songs(OR=0.711,95％CI:0.541～0.941)were independent protective factors.The logistic early warning model based on SMOTEENN algorithm demonstrated optimal predictive performance with an AUC value of 0.923.Conclusion Our constructed logistic regression model has identified 9 independent influencing factors associated with depression tendency among college students.The early warning model based on SMOTEENN algorithm can predict the depression tendency more accurately for college students.
