Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry

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 com...

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Bibliographic Details
Published in:Journal of psychiatric research 2016-12, Vol.83, p.94-102
Main Authors: Ioannidis, Konstantinos, Chamberlain, Samuel R, Treder, Matthias S, Kiraly, Franz, Leppink, Eric W, Redden, Sarah A, Stein, Dan J, Lochner, Christine, Grant, Jon E
Format: Article
Language:English
Subjects:
ADHD
Adolescent
Adult
Aged
Aged, 80 and over
Behavior, Addictive - epidemiology
Behavior, Addictive - psychology
Compulsive Behavior - epidemiology
Compulsivity
Female
Humans
Impulsivity
Internet
Internet use
Machine Learning
Male
Middle Aged
Obsessive-Compulsive Disorder - epidemiology
Obsessive-Compulsive Disorder - psychology
OCD
Online Systems
Predictive Value of Tests
Psychiatry
Reproducibility of Results
ROC Curve
South Africa - epidemiology
Surveys and Questionnaires
United States - epidemiology
Young Adult
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Summary: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 
ISSN:0022-3956
1879-1379
DOI:10.1016/j.jpsychires.2016.08.010