Volume 14, Issue 2 (6-2026)
jmsthums 2026, 14(2): 27-37 |
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Javanmard P, Noferesti A, Rostami R. The relative contribution of predictive components of internet addiction and its association with depression and stress among young adults in Urmia. jmsthums 2026; 14 (2) :27-37
URL: http://jms.thums.ac.ir/article-1-1440-en.html
URL: http://jms.thums.ac.ir/article-1-1440-en.html
1- Department of Clinical Psychology, Faculty of Psychology and Educational Sciences, Aras International Campus, University of Tehran, Tehran, Iran
Abstract: (12 Views)
Background & Aim: Internet addiction is a public health concern characterized by excessive internet use that interferes with daily functioning. Therefore, this study aimed to determine the factors associated with internet addiction and examine its relationship with stress and depression among young adults in Urmia, Iran.
Methods: This descriptive-analytical cross-sectional study included 600 young adults aged 18–30 years who were recruited through convenience sampling in Urmia, Iran, during 2024–2025. Participants completed the questionnaires in person. The Young Internet Addiction Test (IAT) and the Depression, Anxiety, and Stress Scale (DASS-21) were used for data collection. Data were analyzed using descriptive statistics (frequency and percentage) and inferential statistical tests, including Pearson’s correlation coefficients and multiple linear regression. Statistical significance was set at 1%.
Results: The study participants included 342 women (57%) and 258 men (43%). Multiple regression analysis showed that the strongest predictors of internet addiction were depression (95% CI: 0.40–0.97; β = 0.23), stress (95% CI: 0.35–0.91; β = 0.21), age (95% CI: 2.74–5.47; β = 0.25), monthly income (95% CI: −5.41 to −3.31; β = −0.36), mother’s occupation (95% CI: 1.33–3.38; β = 0.17), and family socioeconomic status (95% CI: 0.21–4.60; β = 0.09; p < 0.03). Overall, the regression model explained 38.5% of the variance in internet addiction.
Conclusion: Based on the findings, designing and implementing practical interventions, including educational programs to enhance coping skills in schools and universities, group counseling interventions to reduce symptoms of depression and stress, and supportive services for low-income families, appear to be necessary.
Methods: This descriptive-analytical cross-sectional study included 600 young adults aged 18–30 years who were recruited through convenience sampling in Urmia, Iran, during 2024–2025. Participants completed the questionnaires in person. The Young Internet Addiction Test (IAT) and the Depression, Anxiety, and Stress Scale (DASS-21) were used for data collection. Data were analyzed using descriptive statistics (frequency and percentage) and inferential statistical tests, including Pearson’s correlation coefficients and multiple linear regression. Statistical significance was set at 1%.
Results: The study participants included 342 women (57%) and 258 men (43%). Multiple regression analysis showed that the strongest predictors of internet addiction were depression (95% CI: 0.40–0.97; β = 0.23), stress (95% CI: 0.35–0.91; β = 0.21), age (95% CI: 2.74–5.47; β = 0.25), monthly income (95% CI: −5.41 to −3.31; β = −0.36), mother’s occupation (95% CI: 1.33–3.38; β = 0.17), and family socioeconomic status (95% CI: 0.21–4.60; β = 0.09; p < 0.03). Overall, the regression model explained 38.5% of the variance in internet addiction.
Conclusion: Based on the findings, designing and implementing practical interventions, including educational programs to enhance coping skills in schools and universities, group counseling interventions to reduce symptoms of depression and stress, and supportive services for low-income families, appear to be necessary.
Type of Study: Research |
Subject:
Special
Received: 2025/08/23 | Accepted: 2025/12/3 | Published: 2026/10/19
Received: 2025/08/23 | Accepted: 2025/12/3 | Published: 2026/10/19
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