Identification of Prospective Predictors of Alcohol Initiation During Early Adolescence

青春期早期饮酒的前瞻性预测因素的鉴定

• 批准号: 10823917
• 负责人: Andrew Moore
• 金额: $ 4.21万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10823917
• 关键词: 10 year old; Address; Adolescent; Age; Alcohol consumption; Alcohols; American; Attitude; Brain; Child; Classification; Consumption; Data; Data Analyses; Data Set; Development; Diagnosis; Early Intervention; Environmental Impact; Environmental Risk Factor; Equation; Factor Analysis; Future; Goals; Individual; Intervention; Investigation; Life; Literature; Machine Learning; Measures; Mediating; Methods; Modeling; National Institute on Alcohol Abuse and Alcoholism; Neurocognitive; Outcome; Participant; Predictive Factor; Prevention; Research; Research Personnel; Rewards; Risk; Risk Factors; Sampling; Techniques; Testing; Time; Weight; Work; Youth; alcohol availability; alcohol risk; alcohol use disorder; alcohol use initiation; cognitive development; data-driven model; early adolescence; machine learning model; multidimensional data; neuroimaging; neurophysiology; novel; peer; preadolescence; prevent; prospective; random forest; underage drinking

Project Summary/Abstract Recent initiatives from the National Institute on Alcohol Abuse and Alcoholism have focused on the prevention of child and adolescent alcohol use. To address this, the proposed project focuses on identifying salient, prospective predictors and inferred causes of early alcohol initiation (EAI) or the consumption of a full drink containing alcohol before the age of 16. The aims of the proposed project will test current assumptions of predictors of alcohol initiation (Aim 1a), compare a priori risk factors with novel, data-driven risk factors (Aim 1b), infer causal relationships between factors and EAI (Aim 2), and, as an exploratory aim, isolate individual measures as predictors using machine learning techniques (Aim 3). To accomplish these aims, we will be leveraging the Adolescent Brain Cognitive Development (ABCD) study consisting of over 11,000 youth participants. ABCD provides multiple time points of neuroimaging, neurocognitive, and environmental measures starting when youth are 9-to-10-years-old allowing for the incorporation of high-dimensional data into a single framework. Thus, the resulting sample provides a unique opportunity to use advanced quantitative methods including structural equation modeling, exploratory factor analysis, and random forest machine learning to highlight what drives youth to initiate alcohol use during this risky developmental period. The outcomes of this project aim to inform research on prevention and intervention of EAI to ultimately delay alcohol initiation to more developmentally appropriate ages.

项目摘要/摘要 美国国家酒精滥用和酒精中毒研究所的最新举措重点是预防 儿童和青少年饮酒。为了解决这个问题，拟议的项目着重于识别显着性， 前瞻性预测因素和推断出早期饮酒的原因（EAI）或食用全饮料 在16岁之前含有酒精。拟议项目的目的将测试当前的假设 酒精启动的预测因素（AIM 1A），将先验风险因素与新型的，数据驱动的风险因素（AIM 1B）进行比较（AI​​M 1B）， 推断因素与EAI之间的因果关系（AIM 2），并作为探索性目的隔离个体 使用机器学习技术作为预测指标（AIM 3）。为了实现这些目标，我们将 利用青少年脑认知发展（ABCD）研究，该研究由11,000多名青年组成 参与者。 ABCD提供了多个时间点神经影像，神经认知和环境措施 首先，年轻人是9到10岁，允许将高维数据纳入一个 框架。因此，结果样本提供了使用高级定量方法的独特机会 包括结构方程建模，探索性因素分析和随机森林机器学习 强调了促使青年在这个危险的发展时期启动饮酒的方法。结果的结果 项目旨在为EAI的预防和干预研究提供信息，以最终将酒精启动推迟到更多 在发展上适当的年龄。