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Volume 14, Issue 2 (2026)
Health Educ Health Promot 2026, 14(2): 263-269 |
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Received: 2026/02/23 | Accepted: 2026/05/9 | Published: 2026/06/11
Received: 2026/02/23 | Accepted: 2026/05/9 | Published: 2026/06/11
How to cite this article
Alfitri R, Kuntoro K, Kuntoro K, Qomaruddin M, Maulina R. Cognitive and Self-Determination Predictors of Adolescents’ Commitment to Prevent Smartphone Addiction. Health Educ Health Promot 2026; 14 (2) :263-269
URL: http://hehp.daneshafarand.org/article-4-87232-en.html
URL: http://hehp.daneshafarand.org/article-4-87232-en.html
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1- Department of Public Health, Faculty of Public Health, Airlangga University, Surabaya, Indonesia
2- Department of Biostatistics and Population Study, Faculty of Public Health, Airlangga University, Surabaya, Indonesia
3- Department of Health Promotion and Behavioral Sciences, Faculty of Public Health, Airlangga University, Surabaya, Indonesia
4- Faculty of Health Sciences, Institute Technology Sains and Health R.S. dr Soepraoen, Malang, Indonesia
2- Department of Biostatistics and Population Study, Faculty of Public Health, Airlangga University, Surabaya, Indonesia
3- Department of Health Promotion and Behavioral Sciences, Faculty of Public Health, Airlangga University, Surabaya, Indonesia
4- Faculty of Health Sciences, Institute Technology Sains and Health R.S. dr Soepraoen, Malang, Indonesia
Abstract (8 Views)
Aims: Smartphone addiction among adolescents has become a growing public health concern. This study applied the health promotion model and self-determination theory to identify cognitive and motivational factors associated with adolescents’ commitment to preventing smartphone addiction.
Instrument & Methods: This cross-sectional study included 307 senior high school students from five public schools in Malang City, Indonesia, selected using cluster random sampling. Data were collected using validated questionnaires adapted from the health promotion model framework, the Basic Psychological Needs Scale, and the Smartphone Application-Based Addiction Scale. Structural equation modeling using SmartPLS was employed to examine the relationships among perceived barriers, self-efficacy, competence, autonomy, relatedness, and commitment to preventing smartphone addiction.
Findings: Competence was the strongest positive predictor of commitment to preventing smartphone addiction (β=0.488; p<0.001), followed by relatedness (β=0.213; p<0.001). Self-efficacy showed a significant negative association with commitment (β=−0.124; p=0.02). In contrast, autonomy (β=0.016; p>0.05) and perceived barriers (β=−0.047; p>0.05) were not significant predictors. The measurement model demonstrated satisfactory validity and reliability, while the structural model showed an acceptable fit (SRMR=0.069) and explained 45.2% of the variance in commitment (R²=0.452).
Conclusion: Competence, relatedness, and self-efficacy predict adolescents’ commitment to preventing smartphone addiction, with competence emerging as the strongest predictor.
Instrument & Methods: This cross-sectional study included 307 senior high school students from five public schools in Malang City, Indonesia, selected using cluster random sampling. Data were collected using validated questionnaires adapted from the health promotion model framework, the Basic Psychological Needs Scale, and the Smartphone Application-Based Addiction Scale. Structural equation modeling using SmartPLS was employed to examine the relationships among perceived barriers, self-efficacy, competence, autonomy, relatedness, and commitment to preventing smartphone addiction.
Findings: Competence was the strongest positive predictor of commitment to preventing smartphone addiction (β=0.488; p<0.001), followed by relatedness (β=0.213; p<0.001). Self-efficacy showed a significant negative association with commitment (β=−0.124; p=0.02). In contrast, autonomy (β=0.016; p>0.05) and perceived barriers (β=−0.047; p>0.05) were not significant predictors. The measurement model demonstrated satisfactory validity and reliability, while the structural model showed an acceptable fit (SRMR=0.069) and explained 45.2% of the variance in commitment (R²=0.452).
Conclusion: Competence, relatedness, and self-efficacy predict adolescents’ commitment to preventing smartphone addiction, with competence emerging as the strongest predictor.
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