Suppr超能文献

一种用于了解和预防青少年大麻影响驾驶的数字健康工具:对大麻使用简短干预反应的横断面研究

A Digital Health Tool to Understand and Prevent Cannabis-Impaired Driving Among Youth: A Cross-sectional Study of Responses to a Brief Intervention for Cannabis Use.

作者信息

Moreno Georgina, van Mierlo Trevor

机构信息

Department of Economics, California State University, Long Beach, Long Beach, CA, United States.

South Bay Economics LLC, Hermosa Beach, CA, United States.

出版信息

JMIR Form Res. 2021 Mar 2;5(3):e25583. doi: 10.2196/25583.

Abstract

BACKGROUND

Cannabis legalization has raised concern about an increased risk of cannabis-impaired driving, particularly among youth. Youth advocates and policy makers require cost-effective tools to target educational resources to promote responsible cannabis use.

OBJECTIVE

The objective of this paper is threefold. First, it describes how a youth advocacy organization disseminated a low-cost digital brief intervention to educate and inform young people about responsible cannabis use. Second, it illustrates how digital tools can help promote understanding about attitudes and behaviors toward cannabis while simultaneously offering tailored education. Finally, this paper contributes to examining behavioral factors associated with youth cannabis-impaired driving by quantifying relationships between cannabis users' willingness to drive impaired and self-reported demographic and behavioral factors.

METHODS

This paper analyzed data from 1110 completed Check Your Cannabis (CYC) brief interventions between March 2019 and October 2020. The CYC asks respondents a brief set of questions about their cannabis use and their personal beliefs and behaviors. Respondents receive comprehensive feedback about their cannabis use and how it compares with others. They also receive a summary of reported behaviors with brief advice. An ordered probit model was used to test relationships between cannabis use, demographics, and driving behaviors to gain further insights.

RESULTS

The vast majority (817/1110, 73.6%) of respondents reported using cannabis. However, a much smaller share of respondents reported problems associated with their cannabis use (257/1110, 23.2%) or driving after cannabis use (342/1110, 30.8%). We found statistically significant relationships between driving after cannabis use and age; Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) risk score; and polysubstance use. However, we did not find gender to be a significant determinant of driving after cannabis use. We estimated that every 10-point increase in the ASSIST score increased the probability of sometimes driving after cannabis use by 7.3% (P<.001). Relative to respondents who reported never drinking alcohol or using other substances with cannabis, those who sometimes drink or use other substances with cannabis were 13% (P<.001) more likely to sometimes or always drive after using cannabis.

CONCLUSIONS

The digital health tool cost the youth advocacy organization approximately Can $0.90 (US $0.71) per use. Due to the tool's unlimited use structure, the per-use cost would further decrease with increased use by the organization's target population. Based on our results, public health campaigns and other interventions may consider tailoring resources to frequent cannabis users, youth with high ASSIST scores, and those with polysubstance abuse. The cost-effectiveness of delivering digital brief interventions with unlimited use is attractive, as increased use decreases the per-user cost. Further research examining the efficacy of digital health interventions targeting problematic cannabis use is required.

摘要

背景

大麻合法化引发了人们对大麻影响下驾驶风险增加的担忧,尤其是在年轻人中。青年倡导者和政策制定者需要具有成本效益的工具,以便将教育资源用于促进负责任地使用大麻。

目的

本文的目的有三个。第一,它描述了一个青年倡导组织如何传播一种低成本的数字简短干预措施,以教育和告知年轻人负责任地使用大麻。第二,它说明了数字工具如何有助于促进对大麻态度和行为的理解,同时提供量身定制的教育。最后,本文通过量化大麻使用者在受影响状态下驾驶的意愿与自我报告的人口统计学和行为因素之间的关系,为研究与青年大麻影响下驾驶相关的行为因素做出贡献。

方法

本文分析了2019年3月至2020年10月期间1110份完成的“检查你的大麻使用情况”(CYC)简短干预的数据。CYC向受访者询问了一组关于他们大麻使用情况、个人信念和行为的简短问题。受访者会收到关于他们大麻使用情况以及与他人比较情况的全面反馈。他们还会收到所报告行为的总结及简短建议。使用有序概率模型来测试大麻使用、人口统计学和驾驶行为之间的关系,以获得进一步的见解。

结果

绝大多数(817/1110,7

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8878/7967219/2630caebadea/formative_v5i3e25583_fig1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验