Measurement and Modeling of Within-Person Variability in Cannabis Protective Behavioral Strategies: A Novel Approach Using Scale Development, Daily Data, and Machine Learning Methods

项目摘要/摘要 大麻是美国最常用的受控物质。1、2年轻人(YA；18-25岁) 报告的使用率最高，最近的流行病学调查显示，YA和YA的比例都有所增加 在使用大麻的人中使用大麻和使用频率。3，4因为频繁和大量使用大麻是 与各种短期和长期有害的身体和心理社会结果有关(例如，改变 大脑发育，判断力和记忆力受损，教育成果差)，5-7需要 帮助个人减少使用和/或使用相关危害的方法。减轻物质负担的一种方法是- 相关危害是使用保护性行为策略(PBS)。针对大麻的公共广播系统包括战略和 个人可以在使用之前、期间、之后或替代使用来减少使用或结果。8，9回顾 评估，PBS的使用频率与过去30天较低的大麻使用量和后果有关 并调解了各种风险因素与大麻结果之间的关系，8，10-15强调PBS是一种 减少大麻使用和危害的有希望的方法。然而，对PBS重点干预的研究是 混合的9，16，17；这种混合的支持可能是由于公共广播公司文献中的空白。具体地说，大多数公共广播公司 研究包括横断面、人与人之间的回顾设计，因此我们缺乏理解 关于哪些策略在什么情况下对谁起作用。新出现的公共广播公司研究表明，两者都 个人是否使用公共广播服务，如果使用公共广播服务，个人之间和个人内部(即，每天)的可变性 他们使用的策略。这表明有必要对大麻PBS进行每日测量，以增加对 个人如何以及何时使用公共广播服务，以及公共广播服务在什么情况下有效或无效。致信地址 这些差距，拟议的F31将采取一种新颖的、多方法的方法，结合规模开发工作， 一种日常数据学习设计，以及机器学习方法。具体目标包括(1)开发和验证 (2)利用每日数据设计和机器学习技术来 开发预测PBS有效性(减少使用/后果)的模型 个人的。为了实现这些目标，申请人将接受以下方面的培训：(1)病因学、预防、 药物使用的干预和减少危害的方法，重点是大麻PBS；(2)心理测量学 开发和量化方法，包括多水平建模和机器学习；(3)日常数据研究 设计和方法；(4)研究传播，包括手稿/赠款撰写和会议 演示文稿。研究结果将对未来的PBS干预研究具有重要意义。具体来说， 结果可以用来更好地了解大麻PBS的日常水平，并改进未来的基于技术的 减少大麻相关危害的公共广播服务干预措施。

PROJECT SUMMARY/ABSTRACT Cannabis is the most commonly used controlled substance in the US.1,2 Young adults (YA; ages 18-25) report highest rates of use, and recent epidemiological surveys show an increase in both proportion of YA using cannabis and frequency of use among those that use.3,4 As frequent and heavy cannabis use is associated with a variety of short- and long-term unwanted physical and psychosocial outcomes (e.g., altered brain development, impaired judgement and memory, poor educational outcomes),5–7 there is a need for approaches to help individuals reduce use and/or use-related harms. One approach to mitigating substance- related harm is using protective behavioral strategies (PBS). PBS for cannabis consist of strategies an individual can use before, during, after, or instead of using to reduce use or consequence.8,9 In retrospective assessments, frequency of PBS use is associated with lower past 30-day cannabis use and consequences and mediates the relation between various risk factors and cannabis outcomes,8,10–15 highlighting PBS as a promising means of reducing cannabis use and harms. However, research on PBS-focused interventions is mixed9,16,17; this mixed support may be due to gaps in the PBS literature. Specifically, the majority of PBS research has consisted of cross-sectional, between-person retrospective designs, thus we lack understanding about which strategies work for whom under what circumstances. Emerging PBS research suggests both between- and within-person (i.e., daily) variability in whether an individual uses any PBS, and if so, which strategies they use. This suggests a need for a daily measure of cannabis PBS to increase understanding of how and when individuals utilize PBS and under what circumstances PBS are or are not effective. To address these gaps, the proposed F31 will take a novel, multimethod approach incorporating scale development work, a daily data study design, and machine learning methods. Specific Aims include (1) developing and validating a daily measure of cannabis PBS; and (2) utilizing a daily data design and machine learning techniques to develop models predicting PBS efficacy (reductions in use/consequences) for each strategy for a given individual. To complement these aims, the applicant will receive training in (1) etiology, prevention, intervention, and harm reduction methods for substance use, with a focus on cannabis PBS; (2) psychometric development and quantitative methods including multilevel modeling and machine learning; (3) daily data study design and methodology; and (4) research dissemination, including manuscript/grant writing and conference presentations. Study findings will have important implications for future PBS intervention research. Specifically, results can be used to better understand cannabis PBS on a daily level and improve future technology-based PBS interventions to reduce cannabis-related harms.