Moreno Megan A, Eickhoff Jens, Zhao Qianqian, Young Henry N, Cox Elizabeth D
Department of Pediatrics, University of Wisconsin-Madison.
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison.
J Pediatr X. 2019 Spring;1. doi: 10.1016/j.ympdx.2019.100006. Epub 2019 Jul 26.
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.
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.
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.
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.
这项为期4年的研究旨在使用问题性和风险性互联网使用筛查量表(PRIUSS)评估病理性互联网使用(PIU)随时间的患病率及其预测因素。我们还确定了PRIUSS的中度风险评分。
在这项纵向队列研究中,我们从两所大学中随机选取参与者,参与者每年完成一次PRIUSS评估。我们使用多因素逻辑回归分析来评估PIU的预测因素。我们进行了受试者工作特征曲线(ROC)分析以确定PRIUSS的中度风险评分。最后,我们应用马尔可夫模型来测试随时间推移在PIU风险状态之间转换的动态变化。
我们的319名参与者中,56%为女性,58%来自中西部地区,75%为白种人。在这四年中,PIU患病率估计在9%至11%之间变化。上一时期的PIU风险状态被确定为PIU的主要预测因素(OR=24.1,95%CI:12.8 - 45.4,p<0.0001)。ROC分析确定定义中度风险的最佳阈值是PRIUSS评分为15分。
这项针对大学生PIU的纵向研究发现,各群体在不同时间均存在风险。PIU最显著的预测因素是上一个时间点处于风险状态。基于研究结果,我们建议将PRIUSS评分为15分作为中度风险临界值,以更好地识别有发展为PIU风险的人群。