Objective:To explore the impact of adolescent mental health status on smartphone addiction, and construct a predictive model for smartphone addiction based on the eXtreme Gradient Boosting(XGBoost) algorithm and multivariate Logistic regression.Methods:In April 2023, a cross-sectional survey was conducted among 14 666 adolescents.All participants were systematically evaluated using a self-developed general information questionnaire, the middle school student mental health scale(MSSMHS), the adolescents self-harm scale(ASHS), the interaction anxiousness scale(IAS), the mobile phone addiction index(MPAI), the middle school students shame scale(MSSS), the UCLA loneliness scale(UCLA-LS), the multidimensional peer victimization scale(MPVS), and the basic psychological needs scale(BPNS).R software version 4.3.2 was used for data analysis. Participants were randomly divided into training set and validation set at the ratio of 7∶3.The XGBoost model and multivariate logistic regression model were constructed to predict the risk of smartphone addiction, and a nomogram was plotted.Model performance was evaluated using the Hosmer-Lemeshow test, area under the curve(AUC), and accuracy(ACC).Results:(1) A total of 14 036 high school students were included in the study, with 5 069(36.1%) exhibited smartphone addiction.The training set comprised 9 826 students, with 3 549(36.1%) being smartphone addicts.The validation set included 4 210 students, with 1 520(36.1%) being smartphone addicts.(2) The XGBoost model identified shame-proneness and social anxiety as the two main predictors of smartphone addiction.(3) Multivariate Logistic regression analysis revealed that anxiety( B=0.328, OR(95% CI)=1.39(1.07-1.81), P=0.015), interpersonal sensitivity( B=0.311, OR(95% CI)=1.36(1.05-1.77), P=0.018), learning pressure( B=0.606, OR(95% CI)=1.83(1.46-2.31), P<0.001), mood swings( B=0.775, OR(95% CI)=2.17(1.70-2.78), P<0.001), social anxiety( B=0.024, OR(95% CI)=1.02(1.01-1.04), P<0.001), shame-proneness( B=0.049, OR(95% CI)=1.05(1.04-1.06), P<0.001), and peer victimization( B=0.037, OR(95% CI)=1.04(1.02-1.06), P<0.001) were significant predictors of smartphone addiction.(4) The ACC and AUC values of the XGBoost model were 0.890 and 0.929 in the training set, and 0.865 and 0.864 in the validation set, respectively.The multivariate Logistic regression model achieved ACC and AUC values of 0.870 and 0.854 in the training set, and 0.867 and 0.859 in the validation set, respectively. Conclusion:Anxiety, interpersonal sensitivity, learning pressure, mood swings, social anxiety, shame-proneness, and peer victimization are identified risk predictors of smartphone addiction in high school adolescents.

Objective:To explore the impact of adolescent mental health status on smartphone addiction, and construct a predictive model for smartphone addiction based on the eXtreme Gradient Boosting(XGBoost) algorithm and multivariate Logistic regression.Methods:In April 2023, a cross-sectional survey was conducted among 14 666 adolescents.All participants were systematically evaluated using a self-developed general information questionnaire, the middle school student mental health scale(MSSMHS), the adolescents self-harm scale(ASHS), the interaction anxiousness scale(IAS), the mobile phone addiction index(MPAI), the middle school students shame scale(MSSS), the UCLA loneliness scale(UCLA-LS), the multidimensional peer victimization scale(MPVS), and the basic psychological needs scale(BPNS).R software version 4.3.2 was used for data analysis. Participants were randomly divided into training set and validation set at the ratio of 7∶3.The XGBoost model and multivariate logistic regression model were constructed to predict the risk of smartphone addiction, and a nomogram was plotted.Model performance was evaluated using the Hosmer-Lemeshow test, area under the curve(AUC), and accuracy(ACC).Results:(1) A total of 14 036 high school students were included in the study, with 5 069(36.1%) exhibited smartphone addiction.The training set comprised 9 826 students, with 3 549(36.1%) being smartphone addicts.The validation set included 4 210 students, with 1 520(36.1%) being smartphone addicts.(2) The XGBoost model identified shame-proneness and social anxiety as the two main predictors of smartphone addiction.(3) Multivariate Logistic regression analysis revealed that anxiety( B=0.328, OR(95% CI)=1.39(1.07-1.81), P=0.015), interpersonal sensitivity( B=0.311, OR(95% CI)=1.36(1.05-1.77), P=0.018), learning pressure( B=0.606, OR(95% CI)=1.83(1.46-2.31), P<0.001), mood swings( B=0.775, OR(95% CI)=2.17(1.70-2.78), P<0.001), social anxiety( B=0.024, OR(95% CI)=1.02(1.01-1.04), P<0.001), shame-proneness( B=0.049, OR(95% CI)=1.05(1.04-1.06), P<0.001), and peer victimization( B=0.037, OR(95% CI)=1.04(1.02-1.06), P<0.001) were significant predictors of smartphone addiction.(4) The ACC and AUC values of the XGBoost model were 0.890 and 0.929 in the training set, and 0.865 and 0.864 in the validation set, respectively.The multivariate Logistic regression model achieved ACC and AUC values of 0.870 and 0.854 in the training set, and 0.867 and 0.859 in the validation set, respectively. Conclusion:Anxiety, interpersonal sensitivity, learning pressure, mood swings, social anxiety, shame-proneness, and peer victimization are identified risk predictors of smartphone addiction in high school adolescents.

With the widespread adoption of smartphones， mobile phone addiction among adolescents has emerged as a challenging public health concern. This paper aims to undertake a comprehensive literature review on mobile phone addiction among high school students， both domestically and internationally. It primarily focuses on exploring the conceptual framework， measurement tools， epidemic status， influencing factors and intervention strategies associated with mobile phone addiction in this specific population， so as to provide references for interventions targeting mobile phone addiction among high school students， with the ultimate goal of reducing the incidence rate within this population. ［Funded by Sichuan Provincial Primary Health Development Research Center in 2022， North Sichuan Medical College （number， SWFZ22-C-89）， Mianyang City Social Science Research Key Base-Sichuan Mianyang Minor Psychological Growth Guidance and Research Center 2022 Annual Funding Project （number， SCWCN 2022YB07）］

Objective:To investigate the mediating roles of the fear of missing out and mobile phone addiction between the basic psychological needs satisfaction and phubbing behavior among high school students.Methods:In April 2022, a cross-sectional design survey was conducted on 14 666 high school students. All participants were evaluated by the basic psychological needs scales(BPNS), generic scale of phubbing(GSP), trait-state fear of missing out scale(T-S FOMOS) and mobile phone addiction index(MPAI). The SPSS 26.0 software was used to conduct common method deviation test, descriptive statistics, and correlation analysis.PROCESS 4.1 was used to construct the model, and the Bootstrap method was used to test for mediating effects.Results:(1)Among the 14 036 high school students, there were 1 752 (12.48%) students who were addicted to mobile phones.There were significant differences in gender in the scores including BPNS(boy: 4.43±0.79, girl: 4.36±0.79), GSP(boy: 2.72±1.01, girl: 2.76±1.03) and T-S FOMOS(boy: 1.73±0.60, girl: 1.84±0.64), ( t=5.22, -10.58, -2.78, all P<0.01). Among different grades, there were significant differences in the scores of BPNS, T-S FOMOS, MPAI, and GSP( F=25.43, 39.50, 53.45, 14.59, all P<0.01). (2)Basic psychological needs score were positively correlated with fear of missing out, mobile phone addiction and phubbing( r=-0.432--0.294, all P<0.01). Phubbing were negatively correlated with fear of missing out and mobile phone addiction( r=0.744, 0.538, both P<0.01). Fear of missing out were negatively correlated with mobile phone( r=0.646, P<0.01). (3)The basic psychological needs satisfaction had a direct effect on phubbing behavior, and the effect value was -0.188 (95% CI: -0.173--0.204). The mediating effect of fear of missing out between the basic psychological needs satisfaction and phubbing behavior was -0.035(95% CI: -0.028--0.042). The mediating effect of mobile phone between the basic psychological needs satisfaction and phubbing behavior was -0.203(95% CI: -0.191--0.214). Fear of missing out and mobile phone addiction played a chain mediating role between them, and the mediating effect value was -0.134(95% CI: -0.125--0.143), which accounted for 23.93%(-0.134/-0.560) of the total effect. Conclusion:The high level basic psychological needs satisfaction can alleviate the occurrence of phubbing behavior. It may be achieved by decreasing fear of missing out and reducing mobile phone addiction.