Individual, contextual, and geospatial influences on decisions to drive under the influence of cannabis

个人、背景和地理空间对大麻影响下驾驶决策的影响

• 批准号: 10585451
• 负责人: JANE METRIK
• 金额: $ 71.04万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10585451
• 关键词: Accelerometer; Acute; Affect; Affective; Area; Automobile Driving; Behavior; Cannabidiol; Cannabinoids; Cannabis; Cannabis policy; Cannabis retail; Cellular Phone; Characteristics; Cognition; Cues; Dangerousness; Data; Decision Making; Destinations; Development; Devices; Ecological momentary assessment; Elements; Event; Frequencies; Friends; Geographic Locations; Geography; Impairment; Impulsivity; Individual; Individual Differences; Interview; Intoxication; Laws; Life; Link; Location; Machine Learning; Maps; Measures; Methods; Movement; Near-Infrared Spectroscopy; Neighborhoods; Outcome; Participant; Pattern; Perception; Persons; Policies; Positioning Attribute; Prevention; Process; Public Health; Reporting; Research; Risk; Risk Factors; Risk Reduction; Route; Safety; Science; Short-Term Memory; Social Environment; Speed; Technology; Testing; Tetrahydrocannabinol; Time; Travel; Weather; Work; cannabis use behavior; contextual factors; driving skills; driving under influence; experience; indexing; insight; interest; marijuana legalization; marijuana use; marijuana user; mobile computing; negative affect; peer; permissiveness; pharmacologic; prospective; public health relevance; risky driving; sobriety; social; spatiotemporal; substance use

PROJECT SUMMARY/ABSTRACT With expanding cannabis legalization and concurrent significant increases in cannabis-related traffic fatalities, driving under the influence of cannabis (DUIC) has become a major public health concern. Despite the impairment in driving skills and increased crash risk associated with cannabis intoxication, little is known about how individuals make the decision to drive (or not) after using cannabis. The proposed project advances our team’s work on cannabis and DUIC risk factors to identify person-level (e.g., DUIC cognitions), within-person processes (e.g., subjective intoxication, affective state, cannabinoid concentration, quantity, route of administration), and contextual influences (e.g., social context, environmental conditions) that predict DUIC behaviors in daily life. The proposed multi-method project will provide unique qualitative and quantitative information about individual-level DUIC behavior using geographically explicit ecological momentary assessment (EMA), which combines event-level data on cannabis use with spatio-temporal data on vehicle movement. Our preliminary data has demonstrated the feasibility of these methods by integrating passively collected continuous location data using a vehicle-based GPS tracking device with EMA data on driving and cannabis use. Frequent and less frequent cannabis users (N = 260) will complete smartphone measures of cannabis use, concurrent substance use, affect, momentary impulsivity, perceived DUIC dangerousness, driving intentions, driving motives and destinations/location, and context during a 4-week EMA period. DUIC (i.e., driving within a pharmacologically-relevant timeframe) will be identified by integrating geospatial data (i.e., latitude/longitude/time and vehicle’s movement) passively and continuously collected in the field with EMA data on cannabis use. Weekly testing of participant cannabis for Δ⁹-tetrahydrocannabinol (THC) and cannabidiol (CBD) concentration will be done using a near-infrared spectroscopy device. This will be the first study to prospectively examine the influence of within-person and contextual predictors on DUIC likelihood and distance traveled (Aim 1). We will explore driving-related cognitions, indices of working memory capacity, and user characteristics in relation to cannabis use and driving experience as potential moderators of the effects of event-level predictors on DUIC (Aim 2). To provide insights on contextual factors and decision-making processes related to DUIC, participants will complete a narrative qualitative interview focused on annotating maps of representative DUIC and non-DUIC trips generated by GPS data during one of the four weeks of the EMA period (Aim 3). Exploratory machine learning analyses will be used to characterize and distinguish DUIC episodes from non-DUIC driving episodes (Aim 4). The current project will be the first full-scale study to integrate such real-time individual-level exposure data with DUIC outcomes of interest. Findings can have important implications for cannabis regulatory science and DUIC prevention efforts, which is critically needed to protect public safety.

项目总结/摘要 随着大麻合法化的扩大和与大麻有关的交通死亡人数的同时大幅增加， 大麻影响下驾驶（DUIC）已成为一个主要的公共卫生问题。尽管 与大麻中毒相关的驾驶技能受损和撞车风险增加， 个人如何决定使用大麻后开车（或不开车）。拟议项目推进了我们的 团队在大麻和DUIC风险因素方面的工作，以确定个人水平（例如，DUIC认知），人内 过程（例如，主观中毒，情感状态，大麻素浓度，量，途径 管理），以及上下文影响（例如，社会背景、环境条件）预测DUIC 日常生活中的行为。拟议的多方法项目将提供独特的定性和定量 关于使用地理上明确生态瞬时的个体水平DUIC行为的信息 评估（EMA），将大麻使用的事件级数据与车辆的时空数据相结合 运动我们的初步数据已经证明了这些方法的可行性，通过被动集成 使用基于车辆的GPS跟踪设备收集连续位置数据，并提供驾驶时的EMA数据， 使用大麻频繁和不太频繁的大麻使用者（N = 260）将完成智能手机测量， 大麻使用，同时使用物质，影响，瞬间冲动，感知DUIC的危险性， 驾驶意图，驾驶动机和目的地/位置，以及4周EMA期间的背景。DUIC (i.e.,在药理学相关时间范围内的驾驶）将通过整合地理空间数据（即， 纬度/经度/时间和车辆的运动）被动地和连续地在现场收集EMA数据 关于大麻的使用每周对参与者大麻进行Δ β-四氢大麻酚（THC）和大麻二酚检测 (CBD)将使用近红外光谱装置进行浓缩。这将是第一项 前瞻性地检查人内和环境预测因素对DUIC可能性的影响， 移动距离（目标1）。我们将探讨驾驶相关的认知，工作记忆容量的指数， 与大麻使用和驾驶经验有关的使用者特征是大麻使用和驾驶经验的影响的潜在调节因素 DUIC的事件级预测因子（目标2）。提供有关背景因素和决策的见解 与DUIC相关的过程，参与者将完成一个叙事定性访谈，重点是注释 地图的代表DUIC和非DUIC行程产生的GPS数据在一个四周的 EMA周期（目标3）。探索性机器学习分析将用于表征和区分DUIC 非DUIC驾驶事件（Aim 4）。目前的项目将是第一个全面的研究， 将这种实时个人水平暴露数据与感兴趣的DUIC结果相结合。调查结果可能具有 大麻监管科学和DUIC预防工作的重要影响，这是迫切需要的 保护公共安全。