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

大麻保护行为策略中人内变异性的测量和建模：一种使用量表开发、每日数据和机器学习方法的新方法

• 批准号: 10604567
• 负责人: Kirstyn Nicole Smith-LeCavalier
• 金额: $ 4.77万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-16 至 2026-03-15
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10604567
• 关键词: Address; Age; Alcohol consumption; Alcohols; Algorithms; Behavior; Behavioral; Brain; Cannabis; Complement; Cross-Sectional Studies; DSM-IV; Data; Data Collection; Data Pooling; Data Science; Development; Education; Epidemiology; Etiology; Feedback; Focus Groups; Foundations; Frequencies; Future; Gambling; Goals; Grant; Harm Reduction; Impairment; Individual; Intervention; Intervention Studies; Judgment; Knowledge; Learning; Legal; Literature; Machine Learning; Manuscripts; Marijuana; Marijuana Dependence; Measurement; Measures; Mediating; Memory; Memory impairment; Methodology; Methods; Modeling; Motor; National Institute of Drug Abuse; Outcome; Persons; Phase; Prevalence; Prevention; Psychometrics; Public Health; Recommendation; Reporting; Research; Research Design; Research Personnel; Research Training; Resources; Review Literature; Risk Factors; Sampling; Sex Behavior; Social Change; Surveys; Techniques; Technology; Training; Validation; Work; Writing; addiction; condoms; design; improved; insight; interest; machine learning method; machine learning model; marijuana use; multilevel analysis; novel; novel strategies; personalized approach; phase 2 study; predictive modeling; psychosocial; random forest; research data dissemination; response; substance use; symposium; tool; undergraduate student; young adult

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.

项目概要/摘要 大麻是美国最常用的受控物质。1,2 年轻人（YA；18-25 岁） 报告使用率最高，最近的流行病学调查显示，YA 的比例均有所增加 使用大麻以及使用者的使用频率。3,4 由于频繁和大量使用大麻 与各种短期和长期不良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、教育成果不佳），5-7 需要 帮助个人减少使用和/或与使用相关的危害的方法之一。 相关危害是使用大麻的保护性行为策略（PBS），该策略由策略和策略组成。 个人可以在使用之前、期间、之后使用或代替使用，以减少使用或后果。8,9 回顾 评估显示，PBS 使用频率与过去 30 天大麻使用率较低及后果相关 并调解各种风险因素与大麻结果之间的关系，8,10–15 强调 PBS 作为 然而，针对 PBS 的干预措施的研究仍在进行中。 混合9,16,17；这种混合支持可能是由于 PBS 文献中的空白造成的。 研究由横断面、人与人之间的回顾性设计组成，因此我们缺乏理解 PBS 的新兴研究表明，哪种策略在何种情况下对谁有效。 个人是否使用任何 PBS 的人与人之间和人内（即每日）差异，如果使用，则哪些 这表明需要每天测量大麻 PBS 以增加对大麻的了解。 个人如何以及何时使用 PBS，以及 PBS 在什么情况下有效或无效。 这些差距，拟议的 F31 将采取一种新颖的多方法方法，结合规模开发工作， 日常数据研究设计和机器学习方法的具体目标包括（1）开发和验证。 每日测量大麻 PBS；(2) 利用每日数据设计和机器学习技术 针对给定的每种策略开发预测 PBS 功效（使用/后果减少）的模型 为了补充这些目标，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害方法，重点是大麻 PBS (2) 心理测量； (3)日常数据研究 设计和方法；(4) 研究传播，包括手稿/资助写作和会议 研究结果将对未来的 PBS 干预研究产生重要影响。 结果可用于更好地了解日常水平的大麻 PBS 并改进未来基于技术的 PBS 采取干预措施减少大麻相关危害。