Abstract
BACKGROUND: Globally, due to population diversity, the prevalence of problematic internet use (PIU) varies from 7.3 to 51%. This study aims to assess correlates of problematic internet use among undergraduate medical students of Delhi and derive a model for allocating new subjects among categories of internet users. MATERIAL AND METHODS: A cross-sectional study was conducted on 201 medical-undergraduate students in a medical college of Delhi from April 1st to May 31st, 2019. A semi-structured and pre-tested questionnaire was used to collect demographic information and factors affecting PIU. Dr. Kimberly Young's Internet Addiction Test (IAT) tool was used to assess PIU. Binary logistic regression has been applied to assess the correlates of PIU, and step-wise discriminant analysis (DA) has been applied to derive a model for allocation of new subjects among categories of internet users. Statistical Package for Social Sciences (Trial version 27.0; SPSS Inc., Chicago, IL) software was used for statistical analysis. RESULT: Total 41.3% of the subjects had PIU. Univariate analysis shows that internet use for emotional support, watching adult content, and gambling were significantly associated with PIU; however, in binary logistic regression, chatting, emotional support and watching online adult content were significant risk factors for PIU. The discriminant model correctly classified 66.2% of respondents into average and problematic internet user groups. CONCLUSION: We should create awareness among medical students regarding problematic internet use and its potential harms; this could be included in the foundation course of curriculum implementation support program (CISP) for MBBS students.