Schuckit Marc A, Smith Tom L, Kalmijn Jelger, Skidmore Jessica, Clausen Peyton, Shafir Alexandra, Saunders Gretchen, Bystritsky Hannah, Fromme Kim
Department of Psychiatry, University of California, San Diego, La Jolla, California.
Alcohol Clin Exp Res. 2015 Feb;39(2):308-16. doi: 10.1111/acer.12620. Epub 2015 Feb 6.
Heavy drinking is common during transitions from high school to college. Optimal programs for diminishing risks for high alcohol consumption often tailor the approach to the specific needs of students. This study describes the results of an Internet-based prevention protocol that tailors the information to the risk associated with a pre-existing phenotype, the Low level of Response (Low LR) to alcohol.
Using stratified random assignment, 454 freshmen with Low and High LR values were assigned to 2 education groups (LR-based where all examples were given the context of the Low LR model of heavy drinking or a State Of The Art (SOTA) Group where the same lessons were taught but without an emphasis on LR) or a no-intervention Control Group. Individuals in the 2 education groups viewed 50-minute online videos once per week for 4 weeks. Changes in drinking patterns were assessed at Baseline, 4 weeks, and 8 weeks using a 2 (LR status) by 3 (education group) by 3 (time points) analysis of variance, with additional tests for ethnicity and sex.
Low LR participants tended to decrease their usual (p < 0.06) and maximum (p < 0.05) drinks per occasion most prominently when assigned to the LR-based protocol, while those with High LRs improved more in the SOTA Group. The most robust differences were seen when controlling for ethnicity. The effect sizes were small to medium.
These results support the advantages of carrying out prevention via the Internet and in tailoring the approach to a pre-existing phenotype.
从高中过渡到大学期间,酗酒现象很常见。降低高酒精摄入量风险的最佳方案通常会根据学生的特定需求调整方法。本研究描述了一种基于互联网的预防方案的结果,该方案根据与预先存在的表型(对酒精的低反应水平(Low LR))相关的风险来调整信息。
采用分层随机分配,将454名低LR值和高LR值的新生分配到2个教育组(基于LR组,所有示例都围绕酗酒的低LR模型展开,或最先进(SOTA)组,讲授相同课程但不强调LR)或无干预对照组。2个教育组的个体每周观看一次50分钟的在线视频,共4周。在基线、4周和8周时,使用2(LR状态)×3(教育组)×3(时间点)方差分析评估饮酒模式的变化,并对种族和性别进行额外测试。
当被分配到基于LR的方案时,低LR参与者每次饮酒的通常量(p < 0.06)和最大量(p < 0.05)往往减少最为显著,而高LR参与者在SOTA组中改善更多。在控制种族时,差异最为明显。效应大小为小到中等。
这些结果支持通过互联网进行预防以及根据预先存在的表型调整方法的优势。