Prevalence and Associated Factors of Internet Addiction among Clinical Medical Students of a Nigerian Private University

Abstract

Aim: This study aimed to determine the prevalence and associated factors of internet addiction among undergraduate clinical medical students of a Nigerian private university.
 Study Design: Cross sectional descriptive study
 Place and Duration of Study: College of Medicine and Allied Health Sciences, Bingham University, Jos campus, Plateau state, Nigeria, between May and July 2021.
 Methods: We included 383 (147 males and 236 females, age range 18 – 49 years) clinical medical students of Bingham University. Data was collected using a self-administered questionnaire which included questions about socio-demographic factors, the pattern of internet use and the standardized Internet Addiction Test questionnaire developed by Dr Kimberly S. Young in 1998. Logistic regression analysis was used to determine associations between internet addiction and various risk factors.
 Results: Out of 402 students who were administered questionnaires, 383 completed the questionnaires giving a response rate of 95.3%. Of these, 145 (37.9%) were mildly addicted to the internet, 30 (7.8%) were moderately addicted and 1 (0.3%) was severely addicted to the internet. The mean age of the study participants was 22.7 ± 2.7 years. Logistic regression analysis showed that age (P = .002), and having trouble sleeping (P = .002) were significantly but negatively associated with the development of internet addiction while hours spend on social media (P < .001) was significantly and positively associated.
 Conclusions: Internet addiction is an emerging disorder in this era of almost global access to internet facilities.

Internet Addiction among College Students from 20 Countries: Scoping Review of Risk Factors and Impact on Academic Procrastination and Sleep Quality

“…Additional sleep quality dysfunctions affected positively by IA included daytime sleeping (Citil et al, 2022;Demir et al, 2020), daytime dysfunction (Ayran et al, 2019;Lin et al, 2019), use of sleep medication (Ayran et al, 2019;Lin et al, 2019), sleep latency (Ayran et al, 2019;Lin et al, 2019). Also, IA was similarly affected by sleepless nights or problems sleeping (Ihekaike et al, 2021;Lin et al, 2019;Sharma et al, 2014) and sleep deprivation (Li et al, 2015). However, few studies reported no significant direct effect of IA on poor sleep quality (You et al, 2021;Zhang, Wu, 2020) and insomnia (Obiagaeri, Chidozie, 2020).…”