Using machine learning to model problematic smartphone use severity: The significant role of fear of missing out. (April 2020) - Elhai, Jon D.; Yang, Haibo; Rozgonjuk, Dmitri; Montag, Christian
Highlights: Machine learning was used to model problematic smartphone use (PSU) severity. Ridge, lasso and elastic net machine learning algorithms performed best. FOMO was most related to PSU severity. Abstract: We examined a model of psychopathology variables, age and sex as correlates of problematic smartphone use (PSU) severity using supervised machine learning in a sample of Chinese undergraduate students. A sample of 1097 participants completed measures querying demographics, and psychological measures of PSU, depression and anxiety symptoms, fear of missing out (FOMO), and rumination. We used several different machine learning algorithms to train our statistical model of age, sex and the psychological variables in modeling PSU severity, trained using many simulated replications on a random subset of participants, and externally tested on the remaining subset of participants. Shrinkage algorithms (lasso, ridge, and elastic net regression) performing slightly but statistically better than other algorithms. Results from the training subset generalized to the test subset, without substantial worsening of fit using traditional fit indices. FOMO had the largest relative contribution in modeling PSU severity when adjusting for other covariates in the model. Results emphasize the significance of FOMO to the construct of PSU.