Real-time prediction of marijuana use & effects of use on cognition in the natural environment

实时预测大麻使用情况

• 批准号: 9329948
• 负责人: Tammy Chung
• 金额: $ 23.42万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2019-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9329948
• 关键词: Abstinence; Acute; Address; Adverse effects; Age; Age of Onset; Algorithms; Attention; Automobile Driving; Awareness; Behavior; Cellular Phone; Cognition; Communities; Data; Data Collection; Detection; Development; Devices; Drug usage; Ecological momentary assessment; Enrollment; Environment; Female; Foundations; Frequencies; Gender; Geography; Goals; Health; Health Technology; Healthcare; Heart Rate; Hour; Injury; Intake; Intervention; Interview; Intoxication; Length; Life; Literature; Location; Machine Learning; Marijuana; Metadata; Methods; Modeling; Monitor; Moods; National Institute of Drug Abuse; Participant; Patient Self-Report; Performance; Positioning Attribute; Procedures; Protocols documentation; Recording of previous events; Recruitment Activity; Relaxation; Reporting; Research; Sampling; Schedule; Severities; Short-Term Memory; System; Testing; Text; Time; Travel; Withdrawal; addiction; age group; base; cognitive function; cognitive task; cognitive testing; computer science; daily functioning; data mining; improved; in vivo; innovation; mHealth; male; marijuana use; marijuana use disorder; marijuana user; marijuana withdrawal; mobile computing; prediction algorithm; programs; reduce marijuana use; young adult

ABSTRACT Some young adult marijuana (MJ) users report adverse effects of MJ use on cognition that impact daily functioning, with negative consequences such as injury and fatality due to driving while under the influence of MJ. Research on the effects of MJ use on cognition, however, has produced mixed findings. MJ effects on cognition may depend on factors such as history and current severity of marijuana use, time since last MJ use (including possible MJ withdrawal effects), and gender. This R21 aims to address limitations of existing research by (1) starting to develop an algorithm to predict MJ use using smartphone data in regular/heavy MJ users based on “routine” or “habitual use”, and (2) examining effects of MJ use on cognition using smartphone- based cognitive testing in the natural environment. Development of an algorithm to predict MJ use would facilitate systematic assessment of MJ effects on cognitive functioning through more efficient scheduling of smartphone cognitive testing among regular/heavy MJ users in relation to daily routines. Cognitive testing by smartphone in the natural environment is an innovative method that has shown validity, and permits sampling of cognitive functioning within and across days in relation to MJ use. This project will enroll non-treatment seeking young adult (ages 18-25) MJ users from the community, representing “low”, “regular”, and “heavy” MJ use, with 50% female at each level of use. Participants will complete a baseline lab assessment, 30-day data collection using smartphone and wearable devices (e.g., wristband), and a debriefing interview. Piloting will optimize the protocol and methods for compliance. Smartphones will collect continuously sensed data (e.g., geolocation) for input to an algorithm to predict MJ use in regular/heavy MJ users. This R21 will identify which types of data, available through smartphone, provide optimal detection of routines in MJ use among regular/heavy users. Smartphone cognitive testing will be administered at various times during acute MJ intoxication and various naturalistically occurring lengths of MJ abstinence to examine effects of MJ use on selected aspects of cognitive functioning in daily life. Development of an algorithm to predict MJ use in regular/heavy MJ users based on smartphone data could, for example, facilitate real-time assessment of MJ effects on cognition through improved sampling of cognition in relation to acute and non-acute effects of MJ use. This R21 will provide the foundation for a research program that aims to examine MJ effects on cognitive functioning in vivo, and could support the development of just-in-time intervention to reduce MJ use. This R21 aligns with NIDA's strategic goal of determining consequences of drug use, and cross-cutting themes of highlighting real-world relevance of research and leveraging mobile health technologies to reduce drug use.

摘要 一些年轻的成年大麻（MJ）使用者报告说，MJ的使用对认知产生了不良影响， 功能，造成负面后果，如受伤和死亡，由于驾驶的影响下， MJ然而，关于MJ使用对认知的影响的研究得出了不同的结果。MJ影响 认知可能取决于一些因素，如大麻使用的历史和目前的严重程度，自上次使用MJ以来的时间， （包括可能的MJ戒断效应）和性别。R21旨在解决现有 研究：（1）开始开发一种算法，使用智能手机数据预测常规/重度MJ的使用情况 基于“常规”或“习惯使用”的用户，以及（2）检查MJ使用对使用智能手机的认知的影响- 在自然环境中进行认知测试。开发一种预测MJ使用的算法将 通过更有效的时间安排，促进系统评估MJ对认知功能的影响， 常规/重度MJ用户的智能手机认知测试与日常生活有关。认知测试， 智能手机在自然环境中是一种创新的方法，已显示出有效性，并允许采样 与MJ使用相关的认知功能。本项目将招募非治疗 从社区中寻找年轻的成年人（18-25岁）MJ用户，代表“低”，“常规”和“重”MJ 使用，每一级使用中女性占50%。参与者将完成基线实验室评估，30天数据 使用智能手机和可穿戴设备的收集（例如，腕带），以及一次汇报面试。飞行员将 优化方案和方法以实现合规性。智能手机将收集连续感测的数据（例如， 地理位置）用于算法的输入以预测常规/重度MJ用户中的MJ使用。这一R21将确定哪些 通过智能手机提供的数据类型，提供了MJ使用中的例程的最佳检测 经常/重度使用者。智能手机认知测试将在急性MJ期间的不同时间进行 中毒和各种自然发生的MJ禁欲长度，以检查MJ使用对 日常生活中认知功能的某些方面。开发一种预测MJ使用的算法， 例如，基于智能手机数据的常规/重度MJ用户可以促进MJ的实时评估 通过改善与MJ急性和非急性效应相关的认知采样对认知的影响 使用.这个R21将为一个旨在研究MJ对认知能力的影响的研究项目提供基础。 在体内发挥作用，并可以支持开发及时干预，以减少MJ的使用。R21 符合NIDA确定药物使用后果的战略目标，以及 强调研究与现实世界的相关性，并利用移动的卫生技术减少药物使用。