Exploring Subjective Well-Being in Adolescents: The Role of Mental Health and Addictive Behaviors through Machine Learning

Background: Adolescents’ subjective well-being (SWB) is strongly linked to mental health, academic achievement, social relationships, and quality of life, and is a key predictor of life outcomes in adulthood. Mental health and addictive behaviors are the two main factors influencing SWB. This study aimed to identify key mental health and addictive behavior factors associated with adolescent SWB through machine learning models. Methods: The data for this study comes from the Health Behaviour in School-aged Children (HBSC) survey 2017/18. The study data contains health data from 60,450 adolescents aged 10–16 years. The study used the XGBoost machine learning model to analyze the impact of mental health and addictive behaviors on adolescent SWB. Gain was used to analyze the significance of the variables. The direction of action of the variables and the interaction between the variables were analyzed using the SHapley Additive exPlanations (SHAP) method. Results: The model in this study has an accuracy of 86.7% and an AUC value of 0.85, showing its good predictive performance. Six key variables were filtered through Gain analysis. Feeling low and health as the two most important factors affecting SWB, with these two variables contributing 51.38% and 19.65%, respectively. Friends and thinking body as major factors influencing SWB in mental health. Smoking lifetime and sweets as major factors influencing SWB in addictive behaviors. The interactions and characteristic dependencies between these variables were further analyzed. The results showed that feeling low, friends, and sweets had a positive effect on SWB, while health and smoking lifetime showed a negative effect. In addition, a moderate thinking body contributes to SWB, whereas being too fat and too thin are both associated with decreased levels of SWB. Conclusion: Mental health and addictive behavioral factors such as feeling low, friends, sweets, and smoking lifetime were significant factors influencing SWB. This provides a scientific basis for the development of public health policies and interventions aimed at enhancing adolescent well-being.

Authors

Publication Details

Journal
International Journal of Mental Health Promotion
Published
2025-01-01
DOI
https://doi.org/10.32604/ijmhp.2025.062808
Citations
2
Primary Topic
Mental Health Research Topics
Type
article
Field-Weighted Citation Impact
4.24
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring Subjective Well-Being in Adolescents: The Role of Mental Health and Addictive Behaviors through Machine Learning

Luze Xie, Xingyi Yang, Sitong Chen, Yajing Xu et al.
2 citations
International Journal of Mental Health Promotion
Mental Health Research Topics
4.24
article

Exploring Subjective Well-Being in Adolescents: The Role of Mental Health and Addictive Behaviors through Machine Learning

Luze Xie, Xingyi Yang, Sitong Chen, Yajing Xu, Zhan Gao, Menghan Bao
article en
2 citations

Abstract

Background: Adolescents’ subjective well-being (SWB) is strongly linked to mental health, academic achievement, social relationships, and quality of life, and is a key predictor of life outcomes in adulthood. Mental health and addictive behaviors are the two main factors influencing SWB. This study aimed to identify key mental health and addictive behavior factors associated with adolescent SWB through machine learning models. Methods: The data for this study comes from the Health Behaviour in School-aged Children (HBSC) survey 2017/18. The study data contains health data from 60,450 adolescents aged 10–16 years. The study used the XGBoost machine learning model to analyze the impact of mental health and addictive behaviors on adolescent SWB. Gain was used to analyze the significance of the variables. The direction of action of the variables and the interaction between the variables were analyzed using the SHapley Additive exPlanations (SHAP) method. Results: The model in this study has an accuracy of 86.7% and an AUC value of 0.85, showing its good predictive performance. Six key variables were filtered through Gain analysis. Feeling low and health as the two most important factors affecting SWB, with these two variables contributing 51.38% and 19.65%, respectively. Friends and thinking body as major factors influencing SWB in mental health. Smoking lifetime and sweets as major factors influencing SWB in addictive behaviors. The interactions and characteristic dependencies between these variables were further analyzed. The results showed that feeling low, friends, and sweets had a positive effect on SWB, while health and smoking lifetime showed a negative effect. In addition, a moderate thinking body contributes to SWB, whereas being too fat and too thin are both associated with decreased levels of SWB. Conclusion: Mental health and addictive behavioral factors such as feeling low, friends, sweets, and smoking lifetime were significant factors influencing SWB. This provides a scientific basis for the development of public health policies and interventions aimed at enhancing adolescent well-being.

International Journal of Mental Health PromotionVol. 27(5)
Openalex Percentile: Top 8%
Mental Health Research Topics
4.24
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.