Correlation between music APP listening habits and depression tendency in college students based on SMOTEENN algorithm

Xinqiao HUANG; Hui ZHU; Hao QU; Yazhou WU; Qiuyue SONG

doi:10.16016/j.2097-0927.202312027

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Correlation between music APP listening habits and depression tendency in college students based on SMOTEENN algorithm

Public Health and Preventive Medicine | 更新时间：2026-01-09

- Correlation between music APP listening habits and depression tendency in college students based on SMOTEENN algorithm
- Journal of Army Medical University Vol. 46, Issue 23, Pages: 2670-2680(2024)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
  
  the National Natural Science Foundation of China(82173621)
- DOI：10.16016/j.2097-0927.202312027
  CLC： G645.5;R195.1;R395.6
- Received：06 December 2023，
  
  Revised：2024-09-13，
  
  Published：15 December 2024
- 稿件说明：
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Xinqiao HUANG, Hui ZHU, Hao QU, et al. Correlation between music APP listening habits and depression tendency in college students based on SMOTEENN algorithm[J]. Journal of Army Medical University, 2024, 46(23): 2670-2680.
DOI：

Xinqiao HUANG, Hui ZHU, Hao QU, et al. Correlation between music APP listening habits and depression tendency in college students based on SMOTEENN algorithm[J]. Journal of Army Medical University, 2024, 46(23): 2670-2680. DOI： 10.16016/j.2097-0927.202312027.

摘要

目的

探究基于音乐APP听歌习惯的大学生抑郁倾向影响因素，构建预测模型并进一步优化。

方法

2023年4~5月采用方便抽样对1 157份在校大学生进行问卷调查，利用单因素分析和Logistic回归分析筛选影响因素，在此基础上构建预测模型；采用SMOTEENN过抽样算法改进数据集，构建预测模型。

结果

Logistic回归分析发现性别: 女性(

=1.730，95%

：1.257~2.396)，年级：大四和研究生(

=2.649，95%

：1.198~7.506)、(

=2.041，95%

：1.231~3.885)，专业：理科(

=1.573，95%

：1.052~2.350)，每天听歌时长：0.5~2 h(

=1.661，95%

：1.011~2.695)，听歌曲风: “伤情”和“怀旧”(

=2.668，95%

：1.701~4.226)、(

=1.751

95%

: 1.086~2.837)，留言频率：“0~5%”的歌曲留言(

=2.938，95%

：1.018~8.417)是抑郁倾向的独立危险因素。开始听歌的年限：1~3年内(

=0.547，95%

：0.347~0.872)，听歌时间段：14：00~18：00和18：00~21：00(

=0.375，95%

：0.167~0.845)、(

=0.313，95%

：0.148~0.671)，“国风”类歌曲：喜欢(

=0.711，95%

：0.541~0.941)是抑郁倾向的独立保护因素。预测效果最优的模型为基于SMOTEENN算法的Logistic预警模型，AUC为0.923。

结论

构建Logistic回归模型得到大学生抑郁倾向的9个独立影响因素。基于SMOTEENN算法所构建的预警模型能更准确地预测大学生的抑郁倾向。

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 (

=1.730

95%

: 1.257~2.396)

senior grade (

=2.649

95%

: 1.198~7.506)

postgraduate grade (

=2.041

95%

: 1.231~ 3.885)

major in Science(

=1.573

95%

: 1.052~2.350)

listening for a duration of 0.5~2 h (

=1.661

95%

: 1.011~2.695)

music style of melancholy (

=2.668

95%

: 1.701~4.226) and of nostalgia (

=1.751

95%

: 1.086~2.837)

and frequency of comments on 0~5% of songs (

=2.938

95%

: 1.018~8.417) were independent risk factors for depressive tendency. Time since listening to music for 1~3 years (

=0.547

95%

: 0.347~0.872)

listening to music from 14:00 to 18:00

(

= 0.375

95%

: 0.167~0.845) and 18:00 to 21:00 (

=0.313

95%

: 0.148~0.671)

and preference for Chinese style songs (

=0.711

95%

: 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.

关键词

Keywords

references

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