Identification of Prospective Predictors of Alcohol Initiation During Early Adolescence
Identification of Prospective Predictors of Alcohol Initiation During Early Adolescence
批准号:
10823917
负责人:
Andrew Moore
金额:
$4.21万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2024
资助国家:
美国
项目状态:
未结题
起止时间:
2024-01-01 至 2025-12-31
关键词:
10 year oldAddressAdolescentAgeAlcohol consumptionAlcoholsAmericanAttitudeBrainChildClassificationConsumptionDataData AnalysesData SetDevelopmentDiagnosisEarly InterventionEnvironmental ImpactEnvironmental Risk FactorEquationFactor AnalysisFutureGoalsIndividualInterventionInvestigationLifeLiteratureMachine LearningMeasuresMediatingMethodsModelingNational Institute on Alcohol Abuse and AlcoholismNeurocognitiveOutcomeParticipantPredictive FactorPreventionResearchResearch PersonnelRewardsRiskRisk FactorsSamplingTechniquesTestingTimeWeightWorkYouthalcohol availabilityalcohol riskalcohol use disorderalcohol use initiationcognitive developmentdata-driven modelearly adolescencemachine learning modelmultidimensional dataneuroimagingneurophysiologynovelpeerpreadolescencepreventprospectiverandom forestunderage drinking
中文摘要
点击翻译按钮获取中文摘要
英文摘要
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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
海外基金