Internet Addiction: Prevalence and Patterns among Professional College Students in Urban Area – A Cross-sectional Study DOI Creative Commons

Aditya Oruganti,

Jaya Chandra Muppa,

Balakrishna Kolanati

et al.

Journal of Public Health and Primary Care, Journal Year: 2024, Volume and Issue: 5(3), P. 167 - 172

Published: Sept. 1, 2024

Abstract Background: Internet addiction, characterized by excessive and compulsive online behavior, has become a global issue, particularly among students. India, with its rapidly growing internet population, is witnessing surge in addiction youth and, especially professional college Objectives: This study aims to assess the prevalence patterns of medical engineering students Belagavi, Karnataka. Methodology: A cross-sectional was conducted 640 (320 320 engineering) using simple random sampling. Data were collected from participants who have used for at least 6 months via self-administered questionnaire, including Young’s Addiction Scale, categorizing into normal, mild, moderate, severe levels. Statistical analysis performed SPSS version 25.0. Results: Of students, 58.4% moderately addicted, 32.5% mildly 6.3% severely addicted. Among 74.4% 15.6% 8.4% Engineering exhibited significantly higher levels compared ( P < 0.05), behavioral differences use emotional responses. Conclusion: The reveals high showing tendency toward problematic use. These findings underscore need early intervention awareness programs address consequences on students’ well-being.

Language: Английский

Global prevalence of internet addiction among university students: a systematic review and meta-analysis DOI
Xin Liu,

Zhen Gui,

Zi-Mu Chen

et al.

Current Opinion in Psychiatry, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 14, 2025

Purpose of review The prevalence internet addiction among university students has been extensively studied worldwide, however, the findings have mixed. This meta-analysis aimed to examine global in and identify its potential moderators. Recent A total 101 eligible studies, comprising 128,020 participants across 38 countries territories, were included. pooled was 41.84% [95% confidence interval (95% CI): 35.89–48.02]. Significant differences observed different income levels, regions, periods COVID-19 pandemic, cut-off values Internet Addiction Test (IAT). Sample size negatively associated with prevalence, while depression positively prevalence. Male had a significantly higher risk compared female [pooled odd ratio (OR): 1.32, 95% CI: 1.19–1.46]. Summary found that high students, which increased since pandemic. Screening intervention measures address should prioritize an including male those from lowerincome regions depression.

Language: Английский

Citations

2
Cell phone addiction and sleep disturbance among medical students in Jiangsu Province, China: the mediating role of psychological resilience and the moderating role of gender DOI Creative Commons
Bin Hu, Qi Wu, Yujia Xie

et al.

Frontiers in Psychiatry, Journal Year: 2024, Volume and Issue: 15

Published: May 15, 2024

Background Cell phone addiction presents a widespread and severe physical mental health concern, now recognized as global public issue. Among medical college students, the issue of poor sleep quality has become particularly prevalent. This study aimed to investigate relationship between cell disturbance in population exploring potential mediating role psychological resilience moderating impact gender. Methods A random cluster sampling method was employed survey 5,048 students from four colleges Jiangsu Province, China, utilizing Mobile Phone Addiction Index (MPAI), Connor-Davidson Resilience Scale (CD-RISC), Pittsburgh Sleep Quality (PSQI) for data collection. Statistical analyses were conducted using SPSS 26.0 PROCESS macro version 4.1. To assess mediation, Model 4 utilized, while 15 effect Results The results revealed significant positive correlation disturbance, with found partially mediate this relationship. Moreover, gender observed significantly moderate on disturbance. Specifically, bootstrap analysis indicated interaction ( Coeff. = -0.0215, P &lt; 0.001), stronger males simple slope 0.0616, t 16.66, 0.001) compared females 0.0401, 9.51, P&lt; 0.001). Conclusion Ultimately, identified partial mediator playing association.

Language: Английский

Citations

5
Assessment of the Relationship Between Internet Addiction, Psychological Well-Being, and Sleep Quality: A Cross-Sectional Study Involving Adult Population DOI Creative Commons
Mehmet Emin Arayıcı, Sema GÜLTEKİN ARAYICI, Özüm Erki̇n

et al.

Behavioral Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 344 - 344

Published: March 11, 2025

Internet addiction is an emerging public health concern among adults, potentially affecting psychological well-being and sleep quality. Although a substantial body of research has focused on adolescents younger less known about middle-aged older adult populations. This study investigated the relationships between addiction, quality, in 629 adults (aged 30–60 years) examined socio-demographic predictors addiction. Participants completed online questionnaires assessing well-being, quality (Pittsburgh Sleep Quality Index). The final sample had mean age 39.4 (SD = 7.8), with 53.4% female participants. Most were employed (77.9%), nearly half held undergraduate degree (49.1%). score was 38.1 ± 13.6. Poor prevalent (67.2%), positively correlated total PSQI scores (r 0.593; p < 0.001). Higher inversely associated both −0.417; 0.001) poor −0.490; Younger age, gender, regular employment, higher income predicted greater whereas having lower scores. Taken together, findings this emphasize importance addressing to mitigate excessive use mid-life populations, particularly those at risk.

Language: Английский

Citations

0
Association between internet addiction and sleep quality in medical students: a longitudinal study DOI Creative Commons

Chaowei Guo,

Ming Chen, Xiaotong Ji

et al.

Frontiers in Psychology, Journal Year: 2025, Volume and Issue: 16

Published: March 12, 2025

Objective The study aimed to confirm the hysteresis effect of internet addiction on sleep quality and examine association between among medical students from first third academic year. Methods A repeated measures observational cohort was conducted, involving 667 at China Medical University 2017 2019. Kruskal-Wallis test used analyze measurement data, cross-lagged panel models were employed assess associations within across different time intervals. Results Internet significantly associated with ( p &lt; 0.001). Notably, in year positively second Conclusion This underscores importance understanding as progress through their years. Attention should be directed towards long-term adverse effects future students.

Language: Английский

Citations

0
AI-Driven Digital Well-being: Developing Machine Learning Model to Predict and Mitigate Internet Addiction DOI

Shajahan Wahed,

Mutaz Abdel Wahed

LatIA, Journal Year: 2025, Volume and Issue: 3, P. 134 - 134

Published: March 1, 2025

Background: Internet addiction has become a major public health issue due to the increased dependence on digital technology, affecting mental and overall well-being. Artificial intelligence (AI) offers innovative approaches predicting mitigating excessive internet use. Objective: This study aims develop evaluate AI-driven machine learning models for by analyzing behavioral patterns psychological indicators. Methods: Open-access datasets from “Kaggle”, such as “Smartphone Usage Data” “Social Media Mental Health”, were analyzed using deep models, including Random Forest, XGBoost, Neural Networks, Natural Language Processing (NLP) techniques. Model performance was assessed based accuracy, precision, recall, F1-score, AUC-ROC. Results: Networks XGBoost achieved highest accuracy (91% 90%, respectively), surpassing traditional like Logistic Regression SVM. Clustering anomaly detection techniques provided further insights into user behavior, while NLP revealed emotional thematic associated with addiction. Conclusion: effectively predict classify addiction, offering scalable personalized interventions promote Future research should focus addressing ethical concerns improving real-time deployment of these models.

Language: Английский

Citations

0
The association between school bullying involvement and Internet addiction among Chinese Southeastern adolescents: a moderated mediation model with depression and smoking DOI Creative Commons

Yuhang She,

Liping Li

Frontiers in Psychiatry, Journal Year: 2025, Volume and Issue: 16

Published: March 25, 2025

Background School bullying and Internet addiction are both common public health problems for adolescents. Several studies found an association between school addiction; however, the underlying mediating moderating mechanisms of complex relationship limited. Objective This study explored role depression in whether smoking moderated Chinese southeastern Methods A cross-sectional was conducted Guangdong Province Southeast China June 2021. Associations addiction, bullying, were estimated using Spearman correlation analysis, mediation effect moderation examined Model 4 7 Hayes’ PROCESS macro. Results The results included 1992 adolescents, 23.5% 28.0% participants reported experiences respectively. There a significant depression, ( p &lt; 0.01). direct effects on [ β = 0.565, SE 0.053, 95% CI (0.461, 0.669)], partially mediated with size being 36.5%. And played -0.166, 0.058, (-0.280, -0.052)]. Conclusions In depression.

Language: Английский

Citations

0
AI-Driven Digital Well-being: Developing Machine Learning Model to Predict and Mitigate Internet Addiction DOI

Shajahan Wahed,

Mutaz Abdel Wahed

LatIA, Journal Year: 2025, Volume and Issue: 3, P. 73 - 73

Published: March 3, 2025

Background: Internet addiction has become a major public health issue due to the increased dependence on digital technology, affecting mental and overall well-being. Artificial intelligence (AI) offers innovative approaches predicting mitigating excessive internet use. Objective: This study aims develop evaluate AI-driven machine learning models for by analyzing behavioral patterns psychological indicators. Methods: Open-access datasets from “Kaggle”, such as “Smartphone Usage Data” “Social Media Mental Health”, were analyzed using deep models, including Random Forest, XGBoost, Neural Networks, Natural Language Processing (NLP) techniques. Model performance was assessed based accuracy, precision, recall, F1-score, AUC-ROC. Results: Networks XGBoost achieved highest accuracy (91% 90%, respectively), surpassing traditional like Logistic Regression SVM. Clustering anomaly detection techniques provided further insights into user behavior, while NLP revealed emotional thematic associated with addiction. Conclusion: effectively predict classify addiction, offering scalable personalized interventions promote Future research should focus addressing ethical concerns improving real-time deployment of these models.

Language: Английский

Citations

0
Smartphone Addiction, Anxiety, Depression, and Academic Performance in University Students: A Cross-Sectional Study DOI

Shazli Ezzat Ghazali,

Ponnusamy Subramaniam, Hend Faye AL-shahrani

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 17, 2025

Abstract In today's globalized world, technology significantly influences daily life. While it offers convenience, also affects individuals in various ways. The increasing use of smartphones has raised concerns about smartphone addiction. This study seeks to examine the relationship between addiction, anxiety, depression, and academic performance among university students. A total 1,846 students (1,362 females 484 males; mean age = 19.62 ± 1.11) participated research. An online questionnaire was distributed, including Smartphone Addiction Scale-M (SAS-M), Beck Anxiety Inventory-M (BAI-M), Depression (BDI-M). Descriptive analysis revealed scores depression respondents as 105.78 22.38, 11.66 10.93, 7.28 7.89, respectively. Further through simple linear regression indicated a statistically significant positive (p < 0.001). Specifically, addiction identified predictor anxiety (b 0.006, t 12.084, p 0.001) 0.005, 10.770, However, found no performance. concluded that college are particularly vulnerable which can result heightened depression. Consequently, comprehensive intervention programs essential address enhance mental health

Language: Английский

Citations

0
Internet addiction in adults: a narrative review DOI Open Access
Tiffany Field

Journal of Psychology & Clinical Psychiatry, Journal Year: 2025, Volume and Issue: 16(1), P. 54 - 60

Published: Jan. 1, 2025

In this narrative review, summaries are given of research published in 2024 on internet addiction adults. The papers focused the prevalence addiction, negative effects, comorbidities, predictors/risk factors, mechanisms and buffers. ranged from 21-76% across cultures as well within professions by severity. effects included depression, pain, sleep problems. comorbidities include anxiety, PTSD ADHD. factors can be categorized personality characteristics, family problems, fear missing out, emotional disorders. potential underlying biological for dysfunction multiple regions brain serotonin dopamine neurotransmitter systems. buffers being married belonging to an extended family. Surprisingly, online photography was only intervention that appeared current literature. Methodological limitations most studies cross-sectional samples almost exclusively young

Language: Английский

Citations

0
Dose-Response Associations of Internet Use Time and Internet Addiction With Depressive Symptoms Among Chinese Children and Adolescents: Cross-Sectional Study DOI Creative Commons

J Li,

Weidi Sun, Z. Y. Luo

et al.

JMIR Public Health and Surveillance, Journal Year: 2024, Volume and Issue: 10, P. e53101 - e53101

Published: Sept. 23, 2024

Children's lives are increasingly mediated by digital technologies, yet evidence regarding the associations between internet use and depression is far from comprehensive remains unclear.

Language: Английский

Citations

1