冲动性子领域的组合可预测酒精中毒频率。
A Combination of Impulsivity Subdomains Predict Alcohol Intoxication Frequency.
作者信息
O'Halloran Laura, Pennie Brian, Jollans Lee, Kiiski Hanni, Vahey Nigel, Rai Laura, Bradley Louisa, Lalor Robert, Whelan Robert
机构信息
School of Psychology, Trinity College Dublin, Dublin 2, Ireland.
School of Psychology, University College Dublin, Dublin 4, Ireland.
出版信息
Alcohol Clin Exp Res. 2018 Jun 15. doi: 10.1111/acer.13779.
BACKGROUND
Impulsivity, broadly characterized as the tendency to act prematurely without foresight, is linked to alcohol misuse in college students. However, impulsivity is a multidimensional construct and different subdomains likely underlie different patterns of alcohol misuse. Here, we quantified the association between alcohol intoxication frequency and alcohol consumption frequency and choice, action, cognitive, and trait domains of impulsivity.
METHODS
University student drinkers (n = 106) completed a battery of demographic and alcohol-related items, as well as self-report and task-based measures indexing different facets of impulsivity. Two orthogonal latent factors, intoxication frequency and alcohol consumption frequency, were generated. Their validity was demonstrated with respect to adverse consequences of alcohol use. Machine learning with penalized regression and feature selection was then utilized to predict intoxication and alcohol consumption frequency using all impulsivity subdomains. Out-of-sample validation was used to quantify model performance.
RESULTS
Impulsivity measures alone were significant predictors of intoxication frequency, but not consumption frequency. Propensity for increased intoxication frequency was characterized by increased trait impulsivity, including the Disinhibition subscale of the Sensation Seeking Scale, Attentional and Non-planning subscales of the Barratt Impulsiveness Scale, increased task-based cognitive impulsivity (response time variability), and increased choice impulsivity (steeper delay discounting on a delay discounting questionnaire). A model combining impulsivity domains with other risk factors (gender; nicotine, cannabis, and other drug use; executive functioning; and learning processes) was also significant but did not outperform the model comprising of impulsivity alone.
CONCLUSIONS
Intoxication frequency, but not consumption frequency, was characterized by a number of impulsivity subdomains.
背景
冲动性，广义上被定义为缺乏远见而过早行动的倾向，与大学生酒精滥用有关。然而，冲动性是一个多维度的概念，不同的子领域可能是不同酒精滥用模式的基础。在此，我们量化了酒精中毒频率与酒精消费频率以及冲动性的选择、行动、认知和特质领域之间的关联。
方法
大学生饮酒者（n = 106）完成了一系列人口统计学和与酒精相关的项目，以及索引冲动性不同方面的自我报告和基于任务的测量。生成了两个正交的潜在因素，即中毒频率和酒精消费频率。它们在酒精使用的不良后果方面的有效性得到了证明。然后利用带有惩罚回归和特征选择的机器学习，使用所有冲动性子领域来预测中毒和酒精消费频率。样本外验证用于量化模型性能。
结果
仅冲动性测量是中毒频率的显著预测因素，但不是消费频率的显著预测因素。中毒频率增加的倾向表现为特质冲动性增加，包括感觉寻求量表的去抑制子量表、巴拉特冲动性量表的注意力和非计划性子量表、基于任务的认知冲动性增加（反应时间变异性）以及选择冲动性增加（延迟折扣问卷上更陡峭的延迟折扣）。一个将冲动性领域与其他风险因素（性别；尼古丁、大麻和其他药物使用；执行功能；以及学习过程）相结合的模型也具有显著性，但并不优于仅由冲动性组成的模型。
结论
中毒频率而非消费频率的特征是多个冲动性子领域。
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