Year-end Sale % OFF 
Abstract
Problematic internet use is common, functionally impairing, and in need of further study. Its relationship with obsessive-compulsive and impulsive disorders is unclear. Our objective was to evaluate whether problematic internet use can be predicted from recognised forms of impulsive and compulsive traits and symptomatology. We recruited volunteers aged 18 and older using media advertisements at two sites (Chicago USA, and Stellenbosch, South Africa) to complete an extensive online survey. State-of-the-art out-of-sample evaluation of machine learning predictive models was used, which included Logistic Regression, Random Forests and Naïve Bayes. Problematic internet use was identified using the Internet Addiction Test (IAT). 2006 complete cases were analysed, of whom 181 (9.0%) had moderate/severe problematic internet use. Using Logistic Regression and Naïve Bayes we produced a classification prediction with a receiver operating characteristic area under the curve (ROC-AUC) of 0.83 (SD 0.03) whereas using a Random Forests algorithm the prediction ROC-AUC was 0.84 (SD 0.03) [all three models superior to baseline models p < 0.0001]. The models showed robust transfer between the study sites in all validation sets [p < 0.0001]. Prediction of problematic internet use was possible using specific measures of impulsivity and compulsivity in a population of volunteers. Moreover, this study offers proof-of-concept in support of using machine learning in psychiatry to demonstrate replicability of results across geographically and culturally distinct settings.
Highlights
The Internet has become an integral part of modern life, and has given rise to a wide range of problematic behaviors associated with its use (Cao et al, 2007)
Our study identified similar associations replicating previous results, and ascertained that indicators of impulsivity, like ADHD and BIS-11 sub-scores, are useful to make out-of-sample predictions of problematic internet use (PIU), which adds to the validity to those associations and highlights the fact that impulsivity as a dimension, and as a categorical variable, is important for PIU
This two-site original investigation showed that problematic internet use (PIU) can be predicted from a number of impulsivity and compulsivity variables, as well as baseline demographic and other clinical characteristics
Summary
The Internet has become an integral part of modern life, and has given rise to a wide range of problematic behaviors associated with its use (Cao et al, 2007). Some of those behaviors, like excessive online gaming, online buying and gambling, frequent email checking, prolific use of social media, and viewing pornography have been reported to cause significant impairment of everyday functioning of some individuals, to the extent that mental health professional help is sought or national health authorities are concerned (Choi, 2007; American Academy of Pediatrics, (2015)). There has been anecdotal evidence of serious physical harm and death by cardiovascular collapse, the majority reported from East Asian countries, and one case in the UK, in individuals who have engaged in ‘marathon’ internet sessions (more than 24 h of continuous activity) of mass multiplayer online gaming (Tam and Walter, 2013; Kiraly et al, 2015)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
