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

项目总结/摘要 大麻是美国最常用的受管制物质。1，2年轻人（YA; 18-25岁） 报告使用率最高，最近的流行病学调查显示， 3，4由于频繁和大量使用大麻是一个普遍现象， 与各种短期和长期不想要的身体和心理社会结果相关（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。一种减轻物质的方法- 保护性行为策略（PBS）。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、之后或代替使用，以减少使用或后果。 PBS使用频率与过去30天大麻使用和后果较低相关 并介导各种风险因素和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，对以PBS为重点的干预措施的研究 混合9，16，17;这种混合支持可能是由于PBS文献中的空白。特别是，大多数PBS 研究包括横截面，人与人之间的回顾性设计，因此我们缺乏了解 在什么情况下哪些策略对谁有效新兴的PBS研究表明， 人与人之间（即，每日）个体是否使用任何PBS的变异性，如果是， 他们使用的策略。这表明需要每天测量大麻PBS，以增加对 个人如何以及何时使用PBS，以及在什么情况下PBS有效或无效。解决 这些差距，拟议的F31将采取一种新颖的，多方法的方法，包括规模的发展工作， 一个日常数据学习的设计，和机器学习的方法。具体目标包括（1）开发和验证 大麻PBS的每日测量;以及（2）利用每日数据设计和机器学习技术， 开发模型，预测PBS的有效性（减少使用/后果），为每一个战略，为给定的 单独的.为了补充这些目标，申请人将接受（1）病因学，预防， （2）心理测量学;（3）药物使用的干预和减少危害方法，重点是大麻PBS;（4）心理测量学 包括多层次建模和机器学习的开发和定量方法;（3）日常数据研究 设计和方法;（4）研究传播，包括手稿/赠款写作和会议 介绍。研究结果将对未来的PBS干预研究具有重要意义。具体地说， 结果可用于更好地了解大麻PBS的日常水平，并改善未来的技术基础 PBS的干预措施，以减少大麻相关的危害。

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.