Predictive Models of Alcohol Consumption

酒精消费的预测模型

• 批准号: 7976057
• 负责人: GEORGIY BOBASHEV
• 金额: $ 8.77万
• 依托单位: RESEARCH TRIANGLE INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7976057
• 关键词: Alcohol abuse; Alcohol consumption; Alcohol or Other Drugs use; Alcoholic beverage heavy drinker; Alcohols; Arts; Automobile Driving; Awareness; Behavior; Biological Neural Networks; Calendar; Characteristics; Clinical; Consumption; DSM-IV; Data; Data Collection; Data Quality; Data Set; Dependence; Discipline; Economics; Epidemiology; Family; Friends; Future; Gender; General Population; Holidays; Individual; Internet; Intervention; Linear Regressions; Methodological Studies; Methodology; Modeling; Non-linear Models; Output; Patient Self-Report; Patients; Pattern; Physics; Public Health; Records; Relapse; Relative (related person); Reporting; Research; Research Personnel; Sampling; Schedule; Series; Statistical Models; Stress; Technology; Testing; Time; Validation; Woman; Work; addiction; alcohol abuse therapy; alcohol research; base; binge drinker; cost; disorder later incidence prevention; drinking; innovation; insight; meetings; men; motivational enhancement therapy; pharmacokinetic model; predictive modeling; prevent; public health relevance; response; sobriety; stability testing; success; theories; time use; treatment program; voice recognition

DESCRIPTION (provided by applicant): The objective of this methodological study is to develop and systematically test predictive models of an individual's alcohol consumption. The ability to forecast alcohol consumption in patients can potentially help individualized treatment programs. For binge drinkers, predicting the timing of their next episode provides additional awareness that could help to control, or even to prevent, the entire episode. For heavy drinkers, predictive models can forecast the windows of treatment opportunity when the patient is most sober and responsive to intervention. For those who are attempting to quit drinking, forecasting the next relapse episode could be used to trigger motivational interviewing or other timely interventions that will help prevent the relapse. We will analyze two data sets containing long time series (2 years and 6 months) of individual daily records of alcohol use, stress, and other factors. We will adapt innovative predictive models developed to forecast future alcohol consumption as well as identify and explain a variety of daily use patterns. The data on individual alcohol consumption is very limited, and this is the first study aiming to build forecasting models using such data. This study offers the field a 'next step' with an innovative analysis approach that can possibly offer clinical implications for relapse prevention, increased treatment efficiency, and enhanced understanding of the factors driving the variety of daily patterns of use. PUBLIC HEALTH RELEVANCE: The data on daily individual alcohol consumption is critical for understanding alcohol abuse/addiction. This study is the first one to build forecasting models using such data. This study offers the field a 'next step' with an innovative analysis approach that can possibly offer clinical implications for relapse prevention, increased treatment efficiency, and enhanced understanding of the factors driving the variety of daily patterns of use.

描述（由申请人提供）：本方法学研究的目的是开发和系统地测试个人饮酒量的预测模型。预测患者饮酒量的能力可能有助于个性化治疗方案。对于酗酒者来说，预测下一次发作的时间可以提供额外的意识，有助于控制甚至预防整个事件。对于重度饮酒者，预测模型可以预测患者最清醒和对干预反应最灵敏时的治疗机会窗口。对于那些试图戒酒的人来说，预测下一次复发事件可以用来触发动机访谈或其他及时的干预措施，这将有助于防止复发。我们将分析两个数据集，其中包含酒精使用，压力和其他因素的个人日常记录的长时间序列（2年和6个月）。我们将采用创新的预测模型来预测未来的酒精消费量，并识别和解释各种日常使用模式。关于个人饮酒量的数据非常有限，这是第一项旨在使用这些数据建立预测模型的研究。这项研究为该领域提供了一种创新的分析方法，可能为预防复发，提高治疗效率提供临床意义，并增强对驱动各种日常使用模式的因素的理解。 公共卫生相关性：个人每日饮酒量的数据对于了解酒精滥用/成瘾至关重要。本研究是第一个利用这些数据建立预测模型的研究。这项研究为该领域提供了一种创新的分析方法，可能为预防复发，提高治疗效率提供临床意义，并增强对驱动各种日常使用模式的因素的理解。