Megan A Moreno1, Jens Eickhoff2, Qianqian Zhao2, Henry N Young3, Elizabeth D Cox1. 1. Department of Pediatrics, University of Wisconsin-Madison. 2. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison. 3. School of Pharmacy, University of Georgia, Athens, GA.
Abstract
OBJECTIVE: The purpose of this 4-year study was to assess the prevalence over time and predictors of PIU using the Problematic and Risky Internet Use Screening Scale (PRIUSS). We also identified an Intermediate risk PRIUSS score. STUDY DESIGN: In this longitudinal cohort study we recruited participants using random selection from 2 colleges, participants completed a yearly PRIUSS. We used multivariate logistic regression analysis to evaluate predictors of PIU. We pursued receiver operating curve (ROC) analysis to identify an Intermediate risk PRIUSS score. Finally, we applied Markov modeling to test the dynamics of moving through PIU risk states over time. RESULTS: Our 319 participants included 56% females, 58% from the Midwest and 75% Caucasian. PIU prevalence estimates varied between 9% and 11% over the four years. PIU risk status from the previous time period was identified as the main predictor for PIU (OR=24.1, 95% CI: 12.8-45.4, p<0.0001). ROC analysis identified the optimal threshold for defining Intermediate risk was a PRIUSS score of 15. CONCLUSION: This longitudinal study of PIU among college students found that risks were present across groups and over time. The most salient predictor of PIU was being at risk at the previous time point. Based on results, we propose a PRIUSS score of 15 as an Intermediate risk cut-off to better identify those at risk of developing PIU.
OBJECTIVE: The purpose of this 4-year study was to assess the prevalence over time and predictors of PIU using the Problematic and Risky Internet Use Screening Scale (PRIUSS). We also identified an Intermediate risk PRIUSS score. STUDY DESIGN: In this longitudinal cohort study we recruited participants using random selection from 2 colleges, participants completed a yearly PRIUSS. We used multivariate logistic regression analysis to evaluate predictors of PIU. We pursued receiver operating curve (ROC) analysis to identify an Intermediate risk PRIUSS score. Finally, we applied Markov modeling to test the dynamics of moving through PIU risk states over time. RESULTS: Our 319 participants included 56% females, 58% from the Midwest and 75% Caucasian. PIU prevalence estimates varied between 9% and 11% over the four years. PIU risk status from the previous time period was identified as the main predictor for PIU (OR=24.1, 95% CI: 12.8-45.4, p<0.0001). ROC analysis identified the optimal threshold for defining Intermediate risk was a PRIUSS score of 15. CONCLUSION: This longitudinal study of PIU among college students found that risks were present across groups and over time. The most salient predictor of PIU was being at risk at the previous time point. Based on results, we propose a PRIUSS score of 15 as an Intermediate risk cut-off to better identify those at risk of developing PIU.
Authors: Lauren A Jelenchick; Jens Eickhoff; Dimitri A Christakis; Richard L Brown; Chong Zhang; Meghan Benson; Megan A Moreno Journal: Comput Human Behav Date: 2014-06-01
Authors: N A Fineberg; Z Demetrovics; D J Stein; K Ioannidis; M N Potenza; E Grünblatt; M Brand; J Billieux; L Carmi; D L King; J E Grant; M Yücel; B Dell'Osso; H J Rumpf; N Hall; E Hollander; A Goudriaan; J Menchon; J Zohar; J Burkauskas; G Martinotti; M Van Ameringen; O Corazza; S Pallanti; S R Chamberlain Journal: Eur Neuropsychopharmacol Date: 2018-10-10 Impact factor: 4.600