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Interpretable analysis of smartphone addiction status and its associated factors among college students
Yuanning Li1, Najie Zhao2, Yanyan Wang3
1School of Physical Education, Yanshan University, Qinhuangdao, Hebei, China.
Background:
This study aimed to develop and validate a risk prediction model for smartphone addiction among college students using an extreme gradient boosting (XGBoost) algorithm, and to identify key factors associated with this behavioral pattern.
Methods:
A cross-sectional survey was conducted among 2,761 college students. The XGBoost machine learning algorithm was applied to analyze the dataset, enabling the identification of variables associated with smartphone addiction and the ranking of their relative contributions based on feature importance scores.
Results:
The prevalence of smartphone addiction in this sample was approximately 22.24%. According to the XGBoost model, predictors ranked by descending feature importance scores were as follows: Loneliness (0.437), Monthly household income (0.067), Age (0.056), Place of residence (0.056).
Conclusions:
Smartphone addiction among college students represents a notable public health concern requiring sustained attention. University educators and administrators are encouraged to prioritize factors showing robust statistical associations with smartphone addiction-including loneliness, monthly household income, age and place of residence-implement routine screening for problematic smartphone use, and develop personalized intervention strategies aligned with individual risk profiles.
